diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 7bc53939..ea9b5db9 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -16,7 +16,6 @@ env:
   IMAGE_PREFIX: ${{ github.repository_owner }}/edit-mind
 
 jobs:
-  
   build-and-push:
     runs-on: ubuntu-latest
     permissions:
@@ -25,7 +24,7 @@ jobs:
       packages: write
       artifact-metadata: write
       attestations: write
-        
+
     strategy:
       matrix:
         service:
@@ -36,23 +35,26 @@ jobs:
             dockerfile: docker/Dockerfile.web
             context: .
         platform:
+          - linux/amd64
           - linux/arm64
     
     steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        
-      - name: "node-cleanup"
+      - name: Maximize build space
         run: |
           sudo rm -rf /usr/share/dotnet
           sudo rm -rf /usr/local/lib/android
           sudo rm -rf /opt/ghc
           sudo rm -rf /opt/hostedtoolcache/CodeQL
+          sudo rm -rf /usr/local/.ghcup
           sudo rm -rf /usr/share/swift
           sudo rm -rf /usr/local/share/boost
+          sudo apt-get autoremove -y
           sudo apt-get clean
-          docker system prune -af --volumes
+          sudo docker system prune -a -f
           df -h
+      
+      - name: Checkout code
+        uses: actions/checkout@v4
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
@@ -73,11 +75,10 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Generate platform tag
-        id: platform-tag
+      - name: Generate platform suffix
+        id: platform
         run: |
-          PLATFORM_TAG=$(echo "${{ matrix.platform }}" | sed 's/\//-/g')
-          echo "tag=${PLATFORM_TAG}" >> $GITHUB_OUTPUT
+          echo "suffix=$(echo ${{ matrix.platform }} | sed 's/\//-/g')" >> $GITHUB_OUTPUT
 
       - name: Extract metadata (tags, labels)
         id: meta
@@ -85,30 +86,45 @@ jobs:
         with:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-${{ matrix.service.name }}
           tags: |
-            type=raw,value=latest-${{ steps.platform-tag.outputs.tag }},enable={{is_default_branch}}
-            type=semver,pattern={{version}}-${{ steps.platform-tag.outputs.tag }}
-            type=semver,pattern={{major}}.{{minor}}-${{ steps.platform-tag.outputs.tag }}
-            type=sha,prefix={{branch}}-${{ steps.platform-tag.outputs.tag }}-
-      - name: Build and push Docker image
+            type=ref,event=branch,suffix=-${{ steps.platform.outputs.suffix }}
+            type=semver,pattern={{version}},suffix=-${{ steps.platform.outputs.suffix }}
+            type=semver,pattern={{major}}.{{minor}},suffix=-${{ steps.platform.outputs.suffix }}
+      
+      - name: Set up env files
+        run: |
+          cp .env.example .env
+          cp .env.system.example .env.system
+
+      - name: Start ChromaDB (testing)
+        run: docker compose up -d chroma
+    
+      - name: Build and Test
         uses: docker/build-push-action@v6
+        with:
+          context: ${{ matrix.service.context }}
+          file: ${{ matrix.service.dockerfile }}
+          target: testing
+          platforms: ${{ matrix.platform }}
+          push: false 
+          cache-from: type=gha,scope=${{ matrix.service.name }}-${{ matrix.platform }}
+          cache-to: type=gha,mode=max,scope=${{ matrix.service.name }}-${{ matrix.platform }}
+
+      - name: Build and push Docker image
         id: push
-        if: github.event_name != 'pull_request'
+        uses: docker/build-push-action@v6
         with:
           context: ${{ matrix.service.context }}
           file: ${{ matrix.service.dockerfile }}
           target: production
-          platforms: linux/arm64
+          platforms: ${{ matrix.platform }}
           build-args: |
             NODE_VERSION=22.20.0
             PNPM_VERSION=10.20.0
           push: ${{ github.event_name != 'pull_request' }}
-          load: false
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          cache-from: |
-            type=gha,scope=${{ matrix.service.name }}-${{ matrix.platform }}
-            type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-${{ matrix.service.name }}:buildcache-${{ matrix.platform }}
-          cache-to: type=gha,mode=max,scope=${{ matrix.service.name }}-${{ matrix.platform }}
+          cache-from: type=gha,scope=${{ matrix.service }}-${{ matrix.platform }}
+          cache-to: type=gha,mode=max,scope=${{ matrix.service }}-${{ matrix.platform }}
           sbom: false
 
       - name: Generate artifact attestation
@@ -119,12 +135,42 @@ jobs:
           subject-digest: ${{ steps.push.outputs.digest }}
           push-to-registry: true
 
-  create-release:
+  create-manifest:
     needs: build-and-push
     runs-on: ubuntu-latest
+    if: github.event_name != 'pull_request'
     permissions:
-      contents: write
+      packages: write
+    strategy:
+      matrix:
+        service:
+          - name: background-jobs
+          - name: web
+    steps:
+      - name: Log in to GHCR
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Create and push manifest
+        run: |
+          TAG="${{ github.ref_name }}"
+          if [[ "${{ github.ref }}" == "refs/heads/main" ]]; then
+            TAG="main"
+          fi
+          
+          docker buildx imagetools create -t ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-${{ matrix.service.name }}:${TAG} \
+            ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-${{ matrix.service.name }}:${TAG}-linux-amd64 \
+            ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-${{ matrix.service.name }}:${TAG}-linux-arm64
+
+  create-release:
+    needs: create-manifest
+    runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
+    permissions:
+      contents: write
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -135,7 +181,6 @@ jobs:
           tag_name: ${{ github.ref_name }}
           name: Release ${{ github.ref_name }}
           draft: true
-          prerelease: false
           files: |
             docker-compose.yml
             .env.example
diff --git a/.gitignore b/.gitignore
index 6623f7d8..65fecb9b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -31,3 +31,5 @@ test-results/*
 .env.dev
 docker/.env.system
 .env.system
+!python/.faces
+*.pkl
diff --git a/apps/background-jobs/package.json b/apps/background-jobs/package.json
index 13395212..b697e7cc 100644
--- a/apps/background-jobs/package.json
+++ b/apps/background-jobs/package.json
@@ -1,6 +1,6 @@
 {
   "name": "background-jobs",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "private": true,
   "type": "module",
   "scripts": {
diff --git a/apps/background-jobs/src/index.ts b/apps/background-jobs/src/index.ts
index 169b53fd..bb8de765 100644
--- a/apps/background-jobs/src/index.ts
+++ b/apps/background-jobs/src/index.ts
@@ -5,12 +5,13 @@ import { BullMQAdapter } from '@bull-board/api/bullMQAdapter'
 import { ExpressAdapter } from '@bull-board/express'
 import foldersRoute from './routes/folders'
 import stitcherRoute from './routes/stitcher'
+import faceRoute from './routes/face'
 import { config } from './config'
-import { faceMatcherQueue, immichImporterQueue, videoQueue, videoStitcherQueue } from './queue'
+import { immichImporterQueue, videoQueue, videoStitcherQueue } from './queue'
 import './jobs/videoIndexer'
-import './jobs/faceMatcher'
 import './jobs/ImmichImporter'
 import './jobs/videoStitcher'
+import './jobs/faceLabelling'
 
 import { pythonService } from '@shared/services/pythonService'
 import { initializeWatchers } from './watcher'
@@ -28,7 +29,6 @@ if (process.env.NODE_ENV === 'development') {
   createBullBoard({
     queues: [
       new BullMQAdapter(videoQueue),
-      new BullMQAdapter(faceMatcherQueue),
       new BullMQAdapter(immichImporterQueue),
       new BullMQAdapter(videoStitcherQueue),
     ],
@@ -40,6 +40,7 @@ if (process.env.NODE_ENV === 'development') {
 
 app.use('/folders', foldersRoute)
 app.use('/stitcher', stitcherRoute)
+app.use('/face', faceRoute)
 
 app.get('/health', (_req, res) => res.json({ status: 'ok' }))
 
diff --git a/apps/background-jobs/src/jobs/ImmichImporter.ts b/apps/background-jobs/src/jobs/ImmichImporter.ts
index 0364f286..c6808483 100644
--- a/apps/background-jobs/src/jobs/ImmichImporter.ts
+++ b/apps/background-jobs/src/jobs/ImmichImporter.ts
@@ -1,10 +1,8 @@
 import { decryptApiKey } from '@shared/services/encryption'
 import { Worker, Job } from 'bullmq'
-import { connection } from '../queue'
+import { connection } from '../services/redis'
 import { ImmichImporterJobData } from '@shared/types/immich'
 import { getAllImmichFaces } from '@shared/services/immich'
-import { reindexFaces } from '@shared/utils/faces'
-import { pythonService } from '@shared/services/pythonService'
 import { prisma } from 'src/services/db'
 
 async function processImmichImporterJob(job: Job<ImmichImporterJobData>) {
@@ -14,9 +12,7 @@ async function processImmichImporterJob(job: Job<ImmichImporterJobData>) {
     })
     if (!integration) throw new Error('Integration not found')
     const apiKey = decryptApiKey(integration.immichApiKey)
-    const facesFiles = await getAllImmichFaces({ baseUrl: integration.immichBaseUrl, apiKey })
-    if (!pythonService.isServiceRunning) await pythonService.start()
-    if (job.id) await reindexFaces(facesFiles, job.id)
+    await getAllImmichFaces({ baseUrl: integration.immichBaseUrl, apiKey })
   } catch (error) {
     console.error(error)
   }
diff --git a/apps/background-jobs/src/jobs/faceLabelling.ts b/apps/background-jobs/src/jobs/faceLabelling.ts
new file mode 100644
index 00000000..3a502d62
--- /dev/null
+++ b/apps/background-jobs/src/jobs/faceLabelling.ts
@@ -0,0 +1,130 @@
+import { Worker, Job } from 'bullmq'
+import { connection } from '../services/redis'
+import { logger } from '@shared/services/logger'
+import { FaceLabellingJobData } from '@shared/types/face'
+import type { FaceDetectionData } from '@shared/types/unknownFace'
+import { promises as fs } from 'fs'
+import { existsSync } from 'fs'
+import path from 'path'
+import type { Scene } from '@shared/types/scene'
+import { FACES_DIR, PROCESSED_VIDEOS_DIR, UNKNOWN_FACES_DIR } from '@shared/constants'
+import { getByVideoSource, updateMetadata } from '@shared/services/vectorDb'
+
+async function processFaceLabellingJob(job: Job<FaceLabellingJobData>) {
+  const { faces, name } = job.data
+  logger.info({ jobId: job.id }, 'Starting Face labelling job')
+
+  const personDir = path.join(FACES_DIR, name)
+  if (!existsSync(personDir)) {
+    await fs.mkdir(personDir, { recursive: true })
+  }
+
+  for (const face of faces) {
+    try {
+      const jsonPath = path.join(UNKNOWN_FACES_DIR, face.jsonFile)
+
+      if (existsSync(jsonPath)) {
+        let faceData: FaceDetectionData
+        try {
+          faceData = JSON.parse(await fs.readFile(jsonPath, 'utf8'))
+        } catch {
+          continue
+        }
+
+        const imageFile = faceData.image_file
+        const srcImagePath = path.join(UNKNOWN_FACES_DIR, imageFile)
+        const destImagePath = path.join(personDir, imageFile)
+
+        const scenes = await getByVideoSource(faceData.video_path)
+        const sortedAppearances = faceData.all_appearances?.sort((a, b) => a.frame_index - b.frame_index)
+
+        if (sortedAppearances && scenes && scenes.length > 0) {
+          const firstAppearance = sortedAppearances[0]
+          const lastAppearance = sortedAppearances[sortedAppearances.length - 1]
+
+          for (const scene of scenes) {
+            // Check if the face appears at any point during the scene
+            const overlapsScene =
+              firstAppearance.timestamp_seconds <= scene.endTime && lastAppearance.timestamp_seconds >= scene.startTime
+
+            if (!overlapsScene) continue
+
+            if (scene.faces.includes(face.faceId)) {
+              scene.faces = scene.faces.map((f) => (f === face.faceId ? name : f))
+            }
+
+            if (scene.facesData) {
+              scene.facesData = scene.facesData.map((f) =>
+                f.name === face.faceId ? { ...f, name, confidence: 100 } : f
+              )
+            }
+
+            await updateMetadata(scene)
+          }
+
+          const videoDir = path.join(PROCESSED_VIDEOS_DIR, path.basename(faceData.video_path))
+          const scenesJsonPath = path.join(videoDir, 'scenes.json')
+
+          if (existsSync(scenesJsonPath)) {
+            const fileScenes: Scene[] = JSON.parse(await fs.readFile(scenesJsonPath, 'utf8'))
+            let modified = false
+
+            for (const scene of fileScenes) {
+              const inRange =
+                scene.startTime <= faceData.last_appearance?.timestamp_seconds &&
+                scene.endTime >= faceData.first_appearance?.timestamp_seconds
+
+              if (!inRange) continue
+
+              let sceneModified = false
+
+              if (scene.faces.includes(face.faceId)) {
+                scene.faces = scene.faces.map((f) => (f === face.faceId ? name : f))
+                sceneModified = true
+              }
+
+              if (scene.facesData) {
+                const hadFace = scene.facesData.some((f) => f.name === face.faceId)
+                scene.facesData = scene.facesData.map((f) =>
+                  f.name === face.faceId ? { ...f, name, confidence: 100 } : f
+                )
+                if (hadFace) sceneModified = true
+              }
+
+              if (sceneModified) modified = true
+            }
+
+            if (modified) {
+              await fs.writeFile(scenesJsonPath, JSON.stringify(fileScenes, null, 2), 'utf8')
+            }
+          }
+        }
+        if (existsSync(srcImagePath)) {
+          await fs.copyFile(srcImagePath, destImagePath)
+          await fs.unlink(srcImagePath)
+        }
+        try {
+          await fs.unlink(jsonPath)
+        } catch (error) {
+          logger.warn(error)
+        }
+      } else {
+        try {
+          const imageFile = face.jsonFile.replace('.json', '.jpg')
+          const srcImagePath = path.join(UNKNOWN_FACES_DIR, imageFile)
+          await fs.unlink(srcImagePath)
+        } catch (error) {
+          logger.warn(error)
+        }
+      }
+    } catch (err) {
+      logger.error('Label error: ' + err)
+    }
+  }
+  logger.info({ jobId: job.id }, 'Face labelling job completed')
+}
+
+export const faceLabellingWorker = new Worker('face-labelling', processFaceLabellingJob, {
+  connection,
+  concurrency: 3,
+})
diff --git a/apps/background-jobs/src/jobs/faceMatcher.ts b/apps/background-jobs/src/jobs/faceMatcher.ts
deleted file mode 100644
index 6fbc4eed..00000000
--- a/apps/background-jobs/src/jobs/faceMatcher.ts
+++ /dev/null
@@ -1,150 +0,0 @@
-import { Worker, Job } from 'bullmq'
-import { promises as fs } from 'fs'
-import path from 'path'
-import { getByVideoSource, updateMetadata } from '@shared/services/vectorDb'
-import type { Scene } from '@shared/types/scene'
-import { pythonService } from '@shared/services/pythonService'
-import { connection } from '../queue'
-import { findMatchingFaces, reindexFaces } from '@shared/utils/faces'
-import { FaceLabelingJobData, MatchResult } from '@shared/types/face'
-import { FACES_DIR, PROCESSED_VIDEOS_DIR } from '@shared/constants'
-import { logger } from '@shared/services/logger'
-import { safeUpdateKnownFaces, safeUpdateScenesFile } from 'src/utils/locker'
-
-async function processFaceMatcherJob(job: Job<FaceLabelingJobData>) {
-  const { personName, referenceImages, unknownFacesDir } = job.data
-
-  logger.info(
-    { jobId: job.id, personName, referenceImagesCount: referenceImages.length, unknownFacesDir },
-    'Starting face matcher job'
-  )
-
-  if (!pythonService.isServiceRunning()) {
-    logger.info('Python service not running, starting it')
-    await pythonService.start()
-    logger.info('Python service started successfully')
-  }
-
-  await job.updateProgress(1)
-  const processedFaces: Array<{ name: string; image_path: string }> = []
-
-  const result = await findMatchingFaces(personName, referenceImages, unknownFacesDir, async (progress) => {
-    const { match } = progress
-    if (match) {
-      const imagePath = await processMatch(match, personName, unknownFacesDir)
-
-      if (imagePath) {
-        processedFaces.push({
-          name: personName,
-          image_path: path.basename(imagePath.toString()),
-        })
-      }
-    }
-    const progressPercent = progress.progress || 0
-    await job.updateProgress(progressPercent)
-  })
-
-  logger.info({ jobId: job.id, matchesFound: result.matches.length }, 'Face matching completed')
-
-  try {
-    logger.info({ jobId: job.id }, 'All matches processed successfully')
-
-    if (job.id) await reindexFaces(processedFaces, job.id)
-    logger.info({ jobId: job.id }, 'Face reindexing completed')
-  } catch (error) {
-    logger.error({ jobId: job.id, error: error instanceof Error ? error.message : error }, 'Error processing matches')
-    throw error
-  }
-}
-
-async function processMatch(match: MatchResult, personName: string, unknownFacesDir: string) {
-  const { json_file, image_file, face_id, face_data } = match
-
-  try {
-    const scenes: Scene[] = await getByVideoSource(face_data.video_path)
-
-    const faceTimestamp = parseFloat(face_data.timestamp_seconds.toString())
-
-    const modifiedScenes = scenes
-      .map((scene) => {
-        if (scene.startTime <= faceTimestamp && scene.endTime >= faceTimestamp) {
-          scene.faces = scene.faces.map((face) => (face === face_id ? personName : face))
-          if (scene.facesData) {
-            scene.facesData = scene.facesData?.map((f) => (f.name === face_id ? { ...f, name: personName } : f))
-          }
-          return scene
-        }
-        return undefined
-      })
-      .filter((scene): scene is Scene => scene !== undefined)
-
-    const videoDir = path.join(PROCESSED_VIDEOS_DIR, path.basename(face_data.video_path))
-
-    const scenesPath = path.join(videoDir, 'scenes.json')
-
-    // Update vector DB
-    for (const scene of modifiedScenes) {
-      await updateMetadata(scene)
-    }
-
-    // Update scenes.json file with lock
-    await safeUpdateScenesFile(scenesPath, face_id, personName, faceTimestamp)
-
-    logger.info({ modifiedScenesCount: modifiedScenes.length, personName }, 'Scene metadata updated successfully')
-    const personDir = path.join(FACES_DIR, personName)
-
-    const dirExists = await fs
-      .access(personDir)
-      .then(() => true)
-      .catch(() => false)
-
-    if (!dirExists) {
-      await fs.mkdir(personDir, { recursive: true })
-    }
-
-    const oldImagePath = path.join(unknownFacesDir, image_file)
-    const newImagePath = path.join(personDir, image_file)
-
-    await fs.rename(oldImagePath, newImagePath)
-
-    await safeUpdateKnownFaces(personName, newImagePath)
-
-    const jsonPath = path.join(unknownFacesDir, json_file)
-    await fs.unlink(jsonPath)
-
-    logger.info({ face_id, personName, imageFile: image_file }, 'Match processed successfully')
-    return newImagePath
-  } catch (error) {
-    logger.error(
-      {
-        imageFile: image_file,
-        faceId: face_id,
-        personName,
-        error: error instanceof Error ? error.message : error,
-        stack: error instanceof Error ? error.stack : undefined,
-      },
-      'Error processing match'
-    )
-    throw error
-  }
-}
-
-export const faceMatcherWorker = new Worker('face-matcher', processFaceMatcherJob, {
-  connection,
-  concurrency: 1,
-})
-
-faceMatcherWorker.on('completed', (job) => {
-  logger.info({ jobId: job.id }, 'Face labeling job completed')
-})
-
-faceMatcherWorker.on('failed', (job: Job | undefined, err: Error) => {
-  logger.error(
-    {
-      jobId: job?.id,
-      error: err.message,
-      stack: err.stack,
-    },
-    'Face labeling job failed'
-  )
-})
diff --git a/apps/background-jobs/src/jobs/videoIndexer.ts b/apps/background-jobs/src/jobs/videoIndexer.ts
index 191893de..9a0a8aca 100644
--- a/apps/background-jobs/src/jobs/videoIndexer.ts
+++ b/apps/background-jobs/src/jobs/videoIndexer.ts
@@ -1,6 +1,6 @@
 import { Worker, Job } from 'bullmq'
 import { prisma } from '../services/db'
-import { connection } from '../queue'
+import { connection } from '../services/redis'
 import path from 'path'
 import { existsSync, mkdirSync, promises as fs } from 'fs'
 import { PROCESSED_VIDEOS_DIR, THUMBNAILS_DIR } from '@shared/constants'
diff --git a/apps/background-jobs/src/jobs/videoStitcher.ts b/apps/background-jobs/src/jobs/videoStitcher.ts
index 00456313..c83c8cb0 100644
--- a/apps/background-jobs/src/jobs/videoStitcher.ts
+++ b/apps/background-jobs/src/jobs/videoStitcher.ts
@@ -1,6 +1,6 @@
 import { generateCompilationResponse } from '@shared/services/modelRouter'
 import { Worker, Job } from 'bullmq'
-import { connection } from '../queue'
+import { connection } from '../services/redis'
 import { stitchVideos } from '@shared/utils/sticher'
 import { getVideoWithScenesBySceneIds } from '@shared/services/vectorDb'
 import { prisma } from '../services/db'
diff --git a/apps/background-jobs/src/queue.ts b/apps/background-jobs/src/queue.ts
index f46c7f88..a6f5c8cb 100644
--- a/apps/background-jobs/src/queue.ts
+++ b/apps/background-jobs/src/queue.ts
@@ -1,12 +1,5 @@
 import { Queue } from 'bullmq'
-import { config } from './config'
-import IORedis from 'ioredis'
-
-export const connection = new IORedis({
-  host: config.redisHost,
-  port: config.redisPort,
-  maxRetriesPerRequest: null,
-})
+import { connection } from './services/redis'
 
 export const videoQueue = new Queue('video-indexing', {
   connection,
@@ -19,7 +12,7 @@ export const videoQueue = new Queue('video-indexing', {
   },
 })
 
-export const faceMatcherQueue = new Queue('face-matcher', {
+export const immichImporterQueue = new Queue('immich-importer', {
   connection,
   defaultJobOptions: {
     attempts: 3,
@@ -30,7 +23,7 @@ export const faceMatcherQueue = new Queue('face-matcher', {
   },
 })
 
-export const immichImporterQueue = new Queue('immich-importer', {
+export const videoStitcherQueue = new Queue('video-stitcher', {
   connection,
   defaultJobOptions: {
     attempts: 3,
@@ -41,7 +34,7 @@ export const immichImporterQueue = new Queue('immich-importer', {
   },
 })
 
-export const videoStitcherQueue = new Queue('video-stitcher', {
+export const faceLabellingQueue = new Queue('face-labelling', {
   connection,
   defaultJobOptions: {
     attempts: 3,
@@ -51,4 +44,3 @@ export const videoStitcherQueue = new Queue('video-stitcher', {
     },
   },
 })
-
diff --git a/apps/background-jobs/src/routes/face.ts b/apps/background-jobs/src/routes/face.ts
new file mode 100644
index 00000000..d1f8d502
--- /dev/null
+++ b/apps/background-jobs/src/routes/face.ts
@@ -0,0 +1,39 @@
+import express from 'express'
+import { faceLabellingQueue } from '../queue'
+import { FaceLabellingJobData } from '@shared/types/face'
+
+const router = express.Router()
+
+router.post('/label', async (req, res) => {
+  const { faces, name } = req.body as FaceLabellingJobData
+
+  if (!faces) {
+    return res.status(400).json({ error: 'faces is required' })
+  }
+
+  if (!name) {
+    return res.status(400).json({ error: 'name is required' })
+  }
+
+  try {
+    await faceLabellingQueue.add(
+      'face-labelling',
+      {
+        faces,
+        name,
+      },
+      {
+        removeOnComplete: true,
+      }
+    )
+
+    res.json({
+      message: 'Face labelling job queued',
+    })
+  } catch (error) {
+    console.error(error)
+    res.status(500).json({ error: 'Failed to queue face labelling job' })
+  }
+})
+
+export default router
diff --git a/apps/background-jobs/src/services/faces.ts b/apps/background-jobs/src/services/faces.ts
index d3034263..cbd4ba8f 100644
--- a/apps/background-jobs/src/services/faces.ts
+++ b/apps/background-jobs/src/services/faces.ts
@@ -1,9 +1,7 @@
 import { promises as fs } from 'fs'
 import path from 'path'
 import { existsSync } from 'fs'
-import { faceMatcherQueue } from 'src/queue'
-import { AddFaceLabelingJobParams } from '@shared/types/face'
-import { FACES_DIR, KNOWN_FACES_FILE, UNKNOWN_FACES_DIR } from '@shared/constants'
+import { FACES_DIR, UNKNOWN_FACES_DIR } from '@shared/constants';
 
 const FACES_PER_PAGE = 40
 
@@ -50,32 +48,31 @@ export const getAllUnknownFaces = async (page = 1, limit = FACES_PER_PAGE) => {
 }
 
 export const getAllKnownFaces = async () => {
-  if (!existsSync(KNOWN_FACES_FILE)) {
-    fs.writeFile(KNOWN_FACES_FILE, JSON.stringify({}), 'utf8')
+  if (!existsSync(FACES_DIR)) {
+    await fs.mkdir(FACES_DIR, { recursive: true })
+    return null
   }
-  if (existsSync(KNOWN_FACES_FILE)) {
-    const facesData = await fs.readFile(KNOWN_FACES_FILE, 'utf-8')
-    const faces = JSON.parse(facesData)
+  const peopleFolders = await fs.readdir(FACES_DIR, { withFileTypes: true })
 
-    const cleanedFaces: Record<string, string[]> = {}
-    for (const [person, paths] of Object.entries(faces)) {
-      cleanedFaces[person] = (paths as string[]).map((path) => path.replace(FACES_DIR, ''))
-    }
+  const result: Record<string, string> = {}
 
-    return cleanedFaces
-  }
+  for (const entry of peopleFolders) {
+    if (!entry.isDirectory()) continue
 
-  return null
-}
+    const personName = entry.name
+    const personFolder = path.join(FACES_DIR, personName)
 
-export const addFaceLabelingJob = async (params: AddFaceLabelingJobParams) => {
-  const job = await faceMatcherQueue.add('face-matcher', params, {
-    attempts: 3,
-    backoff: {
-      type: 'exponential',
-      delay: 5000,
-    },
-  })
+    const files = await fs.readdir(personFolder)
+    const jpgFiles = files.filter((f) => f.toLowerCase().endsWith('.jpg'))
 
-  return job
-}
+    if (jpgFiles.length === 0) continue
+
+    jpgFiles.sort()
+
+    const lastImage = jpgFiles[jpgFiles.length - 1]
+
+    result[personName] = path.join(personName, lastImage)
+  }
+
+  return result
+}
\ No newline at end of file
diff --git a/apps/background-jobs/src/services/redis.ts b/apps/background-jobs/src/services/redis.ts
new file mode 100644
index 00000000..1afced19
--- /dev/null
+++ b/apps/background-jobs/src/services/redis.ts
@@ -0,0 +1,8 @@
+import { config } from '../config'
+import IORedis from 'ioredis'
+
+export const connection = new IORedis({
+  host: config.redisHost,
+  port: config.redisPort,
+  maxRetriesPerRequest: null,
+})
diff --git a/apps/background-jobs/src/utils/locker.ts b/apps/background-jobs/src/utils/locker.ts
index 218a68da..d0c447f6 100644
--- a/apps/background-jobs/src/utils/locker.ts
+++ b/apps/background-jobs/src/utils/locker.ts
@@ -1,11 +1,8 @@
-import { KNOWN_FACES_FILE } from '@shared/constants'
 import { Scene } from '@shared/schemas'
 import { logger } from '@shared/services/logger'
 import { existsSync } from 'fs'
 import { readFile, open, unlink, writeFile } from 'fs/promises'
 
-const LOCK_FILE = KNOWN_FACES_FILE + '.lock'
-
 export async function acquireLock(lockPath: string) {
   while (true) {
     try {
@@ -27,31 +24,6 @@ async function releaseLock(lockPath: string) {
   }
 }
 
-export async function safeUpdateKnownFaces(personName: string, newImagePath: string) {
-  await acquireLock(LOCK_FILE)
-
-  try {
-    let faces: Record<string, string[]> = {}
-
-    try {
-      const content = await readFile(KNOWN_FACES_FILE, 'utf-8')
-      faces = JSON.parse(content)
-    } catch {
-      faces = {}
-    }
-
-    if (!faces[personName]) {
-      faces[personName] = []
-    }
-
-    faces[personName].push(newImagePath)
-
-    await writeFile(KNOWN_FACES_FILE, JSON.stringify(faces, null, 2))
-  } finally {
-    await releaseLock(LOCK_FILE)
-  }
-}
-
 export async function safeUpdateScenesFile(
   scenesPath: string,
   faceId: string,
@@ -69,7 +41,6 @@ export async function safeUpdateScenesFile(
 
     const allScenes: Scene[] = JSON.parse(await readFile(scenesPath, 'utf-8'))
 
-    // Update the scenes in the array
     let modified = false
     for (const scene of allScenes) {
       if (scene.startTime <= faceTimestamp && scene.endTime >= faceTimestamp) {
diff --git a/apps/desktop/lib/conveyor/handlers/app-handler.ts b/apps/desktop/lib/conveyor/handlers/app-handler.ts
index b4c7c436..9992ef52 100644
--- a/apps/desktop/lib/conveyor/handlers/app-handler.ts
+++ b/apps/desktop/lib/conveyor/handlers/app-handler.ts
@@ -22,7 +22,6 @@ import { convertTimeToWords } from '@shared/utils/time'
 import { getLocationName } from '@shared/utils/location'
 import { FACES_DIR, PROCESSED_VIDEOS_DIR, THUMBNAILS_DIR } from '@shared/constants'
 import { handle, sender } from '@/lib/main/shared'
-import { reindexFaces } from '@shared/utils/faces'
 
 export const registerAppHandlers = (app: App, webContents: WebContents) => {
   const send = sender(webContents)
@@ -249,7 +248,6 @@ export const registerAppHandlers = (app: App, webContents: WebContents) => {
 
     try {
       const jsonPath = path.join(unknownFacesDir, jsonFile)
-      const jobId = ''
 
       try {
         await fs.unlink(jsonPath)
@@ -257,7 +255,6 @@ export const registerAppHandlers = (app: App, webContents: WebContents) => {
         console.error(error)
       }
 
-      return await reindexFaces([], jobId)
 
       return { success: true }
     } catch (error) {
diff --git a/apps/desktop/package.json b/apps/desktop/package.json
index 4dd30930..cc504602 100644
--- a/apps/desktop/package.json
+++ b/apps/desktop/package.json
@@ -1,6 +1,6 @@
 {
   "name": "desktop",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "description": "AI-Powered Video Indexing and Search",
   "main": "./out/main/main.js",
   "license": "MIT",
diff --git a/apps/web/app/features/training/components/KnownFacesGrid.tsx b/apps/web/app/features/training/components/KnownFacesGrid.tsx
index b31c0a03..b583e090 100644
--- a/apps/web/app/features/training/components/KnownFacesGrid.tsx
+++ b/apps/web/app/features/training/components/KnownFacesGrid.tsx
@@ -11,12 +11,8 @@ export const KnownFacesGrid: React.FC<KnownFacesGridProps> = ({ knownFaces }) =>
       {knownFaces.map((face) => (
         <div key={face.name} className="group">
           <div className="relative aspect-square rounded-2xl overflow-hidden bg-zinc-900 ring-1 ring-white/10 hover:ring-white/30 transition-all duration-300 hover:scale-105">
-            {face.images.length > 0 ? (
-              <img
-                src={`/faces${face.images[face.images.length - 1]}`}
-                alt={face.name}
-                className="w-full h-full object-cover"
-              />
+            {face.image ? (
+              <img src={`/faces/${face.image}`} alt={face.name} className="w-full h-full object-cover" />
             ) : (
               <div className="w-full h-full flex items-center justify-center">
                 <svg className="w-16 h-16 text-gray-700" fill="none" stroke="currentColor" viewBox="0 0 24 24">
@@ -35,9 +31,6 @@ export const KnownFacesGrid: React.FC<KnownFacesGridProps> = ({ knownFaces }) =>
 
           <div className="mt-3 px-1">
             <p className="font-semibold text-base truncate">{face.name}</p>
-            <p className="text-sm text-gray-500 mt-0.5">
-              {face.images.length} sample{face.images.length !== 1 ? 's' : ''}
-            </p>
           </div>
         </div>
       ))}
diff --git a/apps/web/app/features/training/components/LabelingForm.tsx b/apps/web/app/features/training/components/LabelingForm.tsx
index a7aad594..e610e014 100644
--- a/apps/web/app/features/training/components/LabelingForm.tsx
+++ b/apps/web/app/features/training/components/LabelingForm.tsx
@@ -96,7 +96,7 @@ export const LabelingForm: React.FC<LabelingFormProps> = ({
               <option value="" className="bg-zinc-900">Choose a person...</option>
               {knownFaces.map((face) => (
                 <option key={face.name} value={face.name} className="bg-zinc-900">
-                  {face.name} · {face.images.length} samples
+                  {face.name}
                 </option>
               ))}
             </select>
diff --git a/apps/web/app/features/training/components/StatusNotifications.tsx b/apps/web/app/features/training/components/StatusNotifications.tsx
deleted file mode 100644
index 21b61968..00000000
--- a/apps/web/app/features/training/components/StatusNotifications.tsx
+++ /dev/null
@@ -1,104 +0,0 @@
-import React from 'react'
-import { X, CheckCircle, AlertCircle, Loader2 } from 'lucide-react'
-
-interface MatchingStatus {
-  isMatching: boolean
-  progress: number
-  matchesFound: number
-  currentPerson: string
-  error: string | null
-}
-
-interface StatusNotificationsProps {
-  matchingStatus: MatchingStatus
-  successMessage: string | null
-  onDismissSuccess: () => void
-  onDismissError: () => void
-}
-
-export const StatusNotifications: React.FC<StatusNotificationsProps> = ({
-  matchingStatus,
-  successMessage,
-  onDismissSuccess,
-  onDismissError,
-}) => {
-  return (
-    <div className="fixed top-4 right-4 z-50 space-y-3 max-w-md">
-      {successMessage && (
-        <div className="bg-green-500/10 backdrop-blur-xl border border-green-500/20 rounded-2xl p-4 shadow-2xl animate-in slide-in-from-top-5 duration-300">
-          <div className="flex items-start gap-3">
-            <div className="shrink-0">
-              <CheckCircle className="w-5 h-5 text-green-400" />
-            </div>
-            <div className="flex-1">
-              <h4 className="text-sm font-semibold text-white mb-1">Success</h4>
-              <p className="text-sm text-gray-300">{successMessage}</p>
-            </div>
-            <button
-              onClick={onDismissSuccess}
-              className="shrink-0 text-gray-400 hover:text-white transition-colors"
-            >
-              <X className="w-4 h-4" />
-            </button>
-          </div>
-        </div>
-      )}
-
-      {matchingStatus.error && (
-        <div className="bg-red-500/10 backdrop-blur-xl border border-red-500/20 rounded-2xl p-4 shadow-2xl animate-in slide-in-from-top-5 duration-300">
-          <div className="flex items-start gap-3">
-            <div className="shrink-0">
-              <AlertCircle className="w-5 h-5 text-red-400" />
-            </div>
-            <div className="flex-1">
-              <h4 className="text-sm font-semibold text-white mb-1">Error</h4>
-              <p className="text-sm text-gray-300">{matchingStatus.error}</p>
-            </div>
-            <button
-              onClick={onDismissError}
-              className="shrink-0 text-gray-400 hover:text-white transition-colors"
-            >
-              <X className="w-4 h-4" />
-            </button>
-          </div>
-        </div>
-      )}
-
-      {matchingStatus.isMatching && (
-        <div className="bg-blue-500/10 backdrop-blur-xl border border-blue-500/20 rounded-2xl p-4 shadow-2xl animate-in slide-in-from-top-5 duration-300">
-          <div className="flex items-start gap-3">
-            <div className="shrink-0">
-              <Loader2 className="w-5 h-5 text-blue-400 animate-spin" />
-            </div>
-            <div className="flex-1">
-              <h4 className="text-sm font-semibold text-white mb-2">
-                Finding Similar Faces
-              </h4>
-              <p className="text-sm text-gray-300 mb-3">
-                Searching for faces matching "{matchingStatus.currentPerson}"
-              </p>
-              
-              <div className="relative h-2 bg-black/40 rounded-full overflow-hidden">
-                <div
-                  className="absolute inset-y-0 left-0 bg-linear-to-r from-blue-500 to-purple-500 rounded-full transition-all duration-500"
-                  style={{ width: `${matchingStatus.progress}%` }}
-                />
-              </div>
-              
-              <div className="flex items-center justify-between mt-2">
-                <span className="text-xs text-gray-400">
-                  {Math.round(matchingStatus.progress)}% complete
-                </span>
-                {matchingStatus.matchesFound > 0 && (
-                  <span className="text-xs font-medium text-blue-400">
-                    {matchingStatus.matchesFound} matches found
-                  </span>
-                )}
-              </div>
-            </div>
-          </div>
-        </div>
-      )}
-    </div>
-  )
-}
\ No newline at end of file
diff --git a/apps/web/app/features/training/hooks/useTraining.ts b/apps/web/app/features/training/hooks/useTraining.ts
index 76cd423a..a54161ad 100644
--- a/apps/web/app/features/training/hooks/useTraining.ts
+++ b/apps/web/app/features/training/hooks/useTraining.ts
@@ -1,13 +1,6 @@
 import { useState, useEffect, useCallback } from 'react'
 import type { UnknownFace, KnownFace } from '@shared/types/face'
 
-interface MatchingStatus {
-  isMatching: boolean
-  progress: number
-  matchesFound: number
-  currentPerson: string
-  error: string | null
-}
 
 interface PaginationData {
   total: number
@@ -26,16 +19,8 @@ export const useTraining = () => {
   const [newFaceName, setNewFaceName] = useState<string>('')
   const [isLabeling, setIsLabeling] = useState<boolean>(false)
   const [activeTab, setActiveTab] = useState<string>('unknown')
-  const [matchingStatus, setMatchingStatus] = useState<MatchingStatus>({
-    isMatching: false,
-    progress: 0,
-    matchesFound: 0,
-    currentPerson: '',
-    error: null,
-  })
   const [successMessage, setSuccessMessage] = useState<string | null>(null)
 
-  // Pagination states
   const [unknownPagination, setUnknownPagination] = useState<PaginationData>({
     total: 0,
     page: 1,
@@ -64,10 +49,6 @@ export const useTraining = () => {
       })
     } catch (error) {
       console.error('Error fetching unknown faces:', error)
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'Failed to load unknown faces. Please refresh the page.',
-      }))
     } finally {
       setLoading(false)
     }
@@ -80,14 +61,12 @@ export const useTraining = () => {
       if (!response.ok) throw new Error('Failed to fetch known faces')
       const data = await response.json()
 
-      // If data.faces is already in array format from pagination
       if (Array.isArray(data.faces)) {
         setKnownFaces(data.faces)
       } else {
-        // Convert object format to array if needed
-        const knownFacesArray = Object.entries(data.faces).map(([name, images]) => ({
+        const knownFacesArray = Object.entries(data.faces).map(([name, image]) => ({
           name,
-          images: images as string[],
+          image: image as string,
         }))
         setKnownFaces(knownFacesArray)
       }
@@ -100,10 +79,6 @@ export const useTraining = () => {
       })
     } catch (error) {
       console.error('Error fetching known faces:', error)
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'Failed to load known faces. Please refresh the page.',
-      }))
     } finally {
       setLoading(false)
     }
@@ -152,106 +127,22 @@ export const useTraining = () => {
     }
   }, [selectedFaces.size, unknownFaces])
 
-  const dismissSuccess = useCallback(() => {
-    setSuccessMessage(null)
-  }, [])
-
-  const dismissError = useCallback(() => {
-    setMatchingStatus((prev) => ({ ...prev, error: null }))
-  }, [])
-
-  const pollMatchingStatus = useCallback(
-    async (personName: string) => {
-      const pollInterval = 2000
-      const maxPolls = 60
-
-      let pollCount = 0
-
-      const poll = async () => {
-        if (pollCount >= maxPolls) {
-          setMatchingStatus((prev) => ({
-            ...prev,
-            isMatching: false,
-          }))
-          return
-        }
-
-        try {
-          const response = await fetch(`/api/faces/matching-status?person=${encodeURIComponent(personName)}`)
-
-          if (!response.ok) {
-            throw new Error('Failed to fetch matching status')
-          }
-
-          const status = await response.json()
-
-          setMatchingStatus((prev) => ({
-            ...prev,
-            progress: status.progress || prev.progress,
-            matchesFound: status.matchesFound || 0,
-            isMatching: status.isActive,
-          }))
-
-          if (status.isActive) {
-            pollCount++
-            setTimeout(poll, pollInterval)
-          } else {
-            if (status.matchesFound > 0) {
-              setSuccessMessage(
-                `Automatic matching complete! Found and labeled ${status.matchesFound} additional matching face(s).`
-              )
-              await fetchData()
-            }
-            setMatchingStatus((prev) => ({
-              ...prev,
-              isMatching: false,
-            }))
-          }
-        } catch (error) {
-          console.error('Error polling matching status:', error)
-          setMatchingStatus((prev) => ({
-            ...prev,
-            isMatching: false,
-            error: 'Failed to get automatic matching status.',
-          }))
-        }
-      }
-
-      poll()
-    },
-    [fetchData]
-  )
   const handleLabelFaces = useCallback(async () => {
     if (selectedFaces.size === 0) {
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'Please select at least one face to label.',
-      }))
       return
     }
 
     const targetName = labelMode === 'existing' ? selectedKnownFace : newFaceName.trim()
 
     if (!targetName) {
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'Please select an existing face or enter a new name.',
-      }))
       return
     }
 
     setIsLabeling(true)
-    setMatchingStatus({
-      isMatching: true,
-      progress: 0,
-      matchesFound: 0,
-      currentPerson: targetName,
-      error: null,
-    })
+
     setSuccessMessage(null)
 
     try {
-      // Prepare all faces data
       const selectedFacesArray = Array.from(selectedFaces)
       const facesToLabel = selectedFacesArray
         .map((image_hash) => {
@@ -266,7 +157,6 @@ export const useTraining = () => {
         })
         .filter((face): face is { jsonFile: string; faceId: string } => face !== null)
 
-      // Single API call with all faces
       const response = await fetch('/api/faces/label', {
         method: 'POST',
         headers: {
@@ -284,7 +174,7 @@ export const useTraining = () => {
         throw new Error(result.error || 'Failed to label faces')
       }
 
-      const { labeledCount, failedCount } = result
+      const { labeledCount } = result
 
       if (labeledCount > 0) {
         setSuccessMessage(
@@ -293,33 +183,17 @@ export const useTraining = () => {
         )
       }
 
-      if (failedCount > 0) {
-        setMatchingStatus((prev) => ({
-          ...prev,
-          error: `${failedCount} face(s) failed to label. Please try again.`,
-        }))
-      }
-
       setSelectedFaces(new Set())
       setNewFaceName('')
       setSelectedKnownFace('')
 
       await fetchData()
-
-      // Start polling for matching status
-      if (labeledCount > 0) {
-        pollMatchingStatus(targetName)
-      }
     } catch (error) {
       console.error('Error labeling faces:', error)
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'An unexpected error occurred while labeling faces. Please try again.',
-      }))
     } finally {
       setIsLabeling(false)
     }
-  }, [selectedFaces, labelMode, selectedKnownFace, newFaceName, unknownFaces, fetchData, pollMatchingStatus])
+  }, [selectedFaces, labelMode, selectedKnownFace, newFaceName, unknownFaces, fetchData])
 
   const handleDeleteUnknownFace = useCallback(async (face: UnknownFace) => {
     try {
@@ -350,10 +224,6 @@ export const useTraining = () => {
       setSuccessMessage('Face deleted successfully.')
     } catch (error) {
       console.error('Error deleting face:', error)
-      setMatchingStatus((prev) => ({
-        ...prev,
-        error: 'Failed to delete face. Please try again.',
-      }))
     }
   }, [])
 
@@ -367,7 +237,6 @@ export const useTraining = () => {
     newFaceName,
     isLabeling,
     activeTab,
-    matchingStatus,
     successMessage,
     unknownPagination,
     knownPagination,
@@ -381,7 +250,5 @@ export const useTraining = () => {
     handleDeleteUnknownFace,
     handleUnknownPageChange,
     handleKnownPageChange,
-    dismissSuccess,
-    dismissError,
   }
 }
diff --git a/apps/web/app/routes/api.faces.label.ts b/apps/web/app/routes/api.faces.label.ts
index 351ee484..b8413b3d 100644
--- a/apps/web/app/routes/api.faces.label.ts
+++ b/apps/web/app/routes/api.faces.label.ts
@@ -1,150 +1,45 @@
-import { promises as fs } from 'fs'
-import path from 'path'
+import { FACES_DIR } from '@shared/constants'
+import { logger } from '@shared/services/logger'
 import { existsSync } from 'fs'
-import type { Scene } from '@shared/types/scene'
+import { mkdir } from 'fs/promises'
+import path from 'path'
 import type { ActionFunctionArgs } from 'react-router'
-import { getByVideoSource, updateMetadata } from '@shared/services/vectorDb'
-import { addFaceLabelingJob } from '../../../background-jobs/src/services/faces'
-import { FACES_DIR, KNOWN_FACES_FILE, PROCESSED_VIDEOS_DIR, UNKNOWN_FACES_DIR } from '@shared/constants'
-
-interface LabelFaceRequest {
-  jsonFile: string
-  faceId: string
-}
 
 export async function action({ request }: ActionFunctionArgs) {
   if (request.method !== 'POST') {
     return { success: false, error: 'Method not allowed' }
   }
 
-  const { faces, name } = (await request.json()) as { faces: LabelFaceRequest[]; name: string }
-  const unknownFacesDir = UNKNOWN_FACES_DIR
-
-  if (!faces || faces.length === 0) {
-    return { success: false, error: 'No faces provided' }
-  }
-
-  if (!name || !name.trim()) {
-    return { success: false, error: 'Name is required' }
-  }
-
-  const personDir = path.join(FACES_DIR, name)
-  if (!existsSync(personDir)) {
-    await fs.mkdir(personDir, { recursive: true })
-  }
+  const { faces, name } = await request.json()
 
-  let labeledCount = 0
-  let failedCount = 0
-  const errors: string[] = []
+  if (!faces?.length) return { success: false, error: 'No faces provided' }
+  if (!name?.trim()) return { success: false, error: 'Name is required' }
 
   try {
-    // Load existing known faces
-    let knownFaces: Record<string, string[]> = {}
-    if (existsSync(KNOWN_FACES_FILE)) {
-      knownFaces = JSON.parse(await fs.readFile(KNOWN_FACES_FILE, 'utf-8'))
-    }
-
-    if (!knownFaces[name]) {
-      knownFaces[name] = []
-    }
-
-    // Process each face
-    for (const face of faces) {
-      try {
-        const jsonPath = path.join(unknownFacesDir, face.jsonFile)
-        const faceData = JSON.parse(await fs.readFile(jsonPath, 'utf-8'))
-        const imageFile = faceData.image_file
-        const imagePath = path.join(unknownFacesDir, imageFile)
-        const videoDir = path.join(PROCESSED_VIDEOS_DIR, path.basename(faceData.video_path))
-
-        const scenesPath = path.join(videoDir, 'scenes.json')
-
-        // Update scenes in vector DB
-        const scenes: Scene[] = await getByVideoSource(faceData.video_path)
-        const modifiedScenes = scenes
-          .map((scene) => {
-            if (
-              scene.startTime >= faceData.frame_start_time_ms / 1000 &&
-              scene.endTime <= faceData.frame_end_time_ms / 1000
-            ) {
-              if (scene.faces.includes(face.faceId)) {
-                scene.faces = scene.faces.map((f) => (f === face.faceId ? name : f))
-                if (scene.facesData) {
-                  scene.facesData = scene.facesData?.map((f) => (f.name === face.faceId ? { ...f, name } : f))
-                }
-                return scene
-              }
-            }
-            return undefined
-          })
-          .filter((scene): scene is Scene => scene !== undefined)
-
-        for (const scene of modifiedScenes) {
-          await updateMetadata(scene)
-        }
-
-        if (existsSync(scenesPath)) {
-          const allScenes: Scene[] = JSON.parse(await fs.readFile(scenesPath, 'utf-8'))
-
-          // Update the scenes in the array
-          for (const scene of allScenes) {
-            if (
-              scene.startTime >= faceData.frame_start_time_ms / 1000 &&
-              scene.endTime <= faceData.frame_end_time_ms / 1000
-            ) {
-              if (scene.faces.includes(face.faceId)) {
-                scene.faces = scene.faces.map((f) => (f === face.faceId ? name : f))
-                if (scene.facesData) {
-                  scene.facesData = scene.facesData?.map((f) => (f.name === face.faceId ? { ...f, name } : f))
-                }
-                return scene
-              }
-            }
-          }
-
-          // Write back to scenes.json
-          await fs.writeFile(scenesPath, JSON.stringify(allScenes, null, 2))
-        }
-        // Move image to person directory
-        const newImagePath = path.join(personDir, imageFile)
-        await fs.copyFile(imagePath, newImagePath)
-        await fs.unlink(imagePath)
-
-        // Add to known faces
-        knownFaces[name].push(newImagePath)
-
-        // // Delete JSON file
-        await fs.unlink(jsonPath)
-
-        labeledCount++
-      } catch (error) {
-        console.error(`Error labeling face ${face.faceId}:`, error)
-        failedCount++
-        errors.push(`Failed to label face ${face.faceId}`)
-      }
+    const personDir = path.join(FACES_DIR, name)
+    if (!existsSync(personDir)) {
+      await mkdir(personDir, { recursive: true })
     }
-
-    // Save updated known faces
-    await fs.writeFile(KNOWN_FACES_FILE, JSON.stringify(knownFaces, null, 2))
-
-    // Trigger matching job once with all reference images
-    if (labeledCount > 0) {
-      const referenceImages = knownFaces[name]
-      await addFaceLabelingJob({
-        personName: name,
-        referenceImages: referenceImages.slice(0, 10),
-        unknownFacesDir,
-      })
+    const backgroundJobsUrl = process.env.BACKGROUND_JOBS_URL
+
+    const res = await fetch(`${backgroundJobsUrl}/face/label`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({ faces, name }),
+    })
+
+    const data = await res.json()
+    if (data.error) {
+      throw new Error(data.error)
     }
-
+    return { success: true }
+  } catch (error) {
+    logger.error(error)
     return {
-      success: true,
-      labeledCount,
-      failedCount,
-      errors: errors.length > 0 ? errors : undefined,
+      success: false,
+      error: 'Error adding face labeling job',
     }
-  } catch (error) {
-    console.error('Error labeling faces:', error)
-    return { success: false, error: 'Failed to label faces' }
   }
 }
diff --git a/apps/web/app/routes/api.faces.matching-status.ts b/apps/web/app/routes/api.faces.matching-status.ts
deleted file mode 100644
index 1314b9eb..00000000
--- a/apps/web/app/routes/api.faces.matching-status.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-import type { LoaderFunctionArgs } from 'react-router'
-import { faceMatcherQueue } from '../../../background-jobs/src/queue'
-
-export async function loader({ request }: LoaderFunctionArgs) {
-  const url = new URL(request.url)
-  const personName = url.searchParams.get('person')
-
-  if (!personName) {
-    return Response.json({ error: 'Person name required' }, { status: 400 })
-  }
-
-  try {
-    const jobs = await faceMatcherQueue.getJobs(['active', 'waiting', 'delayed'])
-    const activeJob = jobs.find((job) => job.data.personName === personName)
-
-    if (!activeJob) {
-      const completedJobs = await faceMatcherQueue.getJobs(['completed'], 0, 1)
-      const recentJob = completedJobs.find((job) => job.data.personName === personName)
-
-      if (recentJob) {
-        return Response.json({
-          isActive: false,
-          progress: 100,
-          matchesFound: recentJob.returnvalue?.matchesFound || 0,
-          status: 'completed',
-        })
-      }
-
-      return Response.json({
-        isActive: false,
-        progress: 0,
-        matchesFound: 0,
-        status: 'not_found',
-      })
-    }
-
-    const progress = activeJob.progress
-    const state = await activeJob.getState()
-
-    return Response.json({
-      isActive: state === 'active' || state === 'waiting',
-      progress: typeof progress === 'number' ? progress : 0,
-      matchesFound: 0, 
-      status: state,
-    })
-  } catch (error) {
-    console.error('Error fetching matching status:', error)
-    return Response.json({ error: 'Failed to fetch status' }, { status: 500 })
-  }
-}
diff --git a/apps/web/app/routes/app.search.tsx b/apps/web/app/routes/app.search.tsx
index 1c989b5b..eae113ae 100644
--- a/apps/web/app/routes/app.search.tsx
+++ b/apps/web/app/routes/app.search.tsx
@@ -11,9 +11,9 @@ import { hybridSearch } from '@shared/services/vectorDb'
 import { motion, AnimatePresence } from 'framer-motion'
 import { getUser } from '~/services/user.sever'
 import { SearchInput } from '~/features/search/components/SearchInput'
-import type { VideoSearchParams } from '@shared/types/search';
+import type { VideoSearchParams } from '@shared/types/search'
 import { generateActionFromPrompt } from '@shared/services/modelRouter'
-import { getSearchStats } from '@shared/utils/search';
+import { getSearchStats } from '@shared/utils/search'
 import { buildSearchQueryFromSuggestions } from '@shared/services/suggestion'
 import { logger } from '@shared/services/logger'
 
@@ -42,7 +42,7 @@ export async function action({ request }: { request: Request }) {
       searchQuery = buildSearchQueryFromSuggestions(suggestions)
       console.debug('Using suggestions for search:', searchQuery)
     } else {
-      searchQuery = await (await generateActionFromPrompt(query)).data
+      searchQuery = (await generateActionFromPrompt(query)).data
       console.debug('Generated search query from AI:', searchQuery)
     }
 
@@ -182,7 +182,9 @@ export default function SearchPage() {
           </>
         )}
 
-        {!hasResults && !isSearching && <EmptyState hasQuery={hasQuery} query={query} hasPerformedSearch={hasSearched} />}
+        {!hasResults && !isSearching && (
+          <EmptyState hasQuery={hasQuery} query={query} hasPerformedSearch={hasSearched} />
+        )}
       </main>
     </DashboardLayout>
   )
diff --git a/apps/web/app/routes/app.training.tsx b/apps/web/app/routes/app.training.tsx
index 46e004d9..73d814a1 100644
--- a/apps/web/app/routes/app.training.tsx
+++ b/apps/web/app/routes/app.training.tsx
@@ -3,7 +3,6 @@ import { Loader2 } from 'lucide-react'
 import { LabelingForm } from '~/features/training/components/LabelingForm'
 import { UnknownFacesGrid } from '~/features/training/components/UnknownFacesGrid'
 import { KnownFacesGrid } from '~/features/training/components/KnownFacesGrid'
-import { StatusNotifications } from '~/features/training/components/StatusNotifications'
 import { useTraining } from '~/features/training/hooks/useTraining'
 import type { MetaFunction } from 'react-router'
 import { DashboardLayout } from '~/layouts/DashboardLayout'
@@ -28,8 +27,6 @@ const Training: React.FC = () => {
     newFaceName,
     isLabeling,
     activeTab,
-    matchingStatus,
-    successMessage,
     setLabelMode,
     setSelectedKnownFace,
     setNewFaceName,
@@ -38,8 +35,6 @@ const Training: React.FC = () => {
     handleSelectAll,
     handleLabelFaces,
     handleDeleteUnknownFace,
-    dismissSuccess,
-    dismissError,
     unknownPagination,
     handleUnknownPageChange,
   } = useTraining()
@@ -57,13 +52,6 @@ const Training: React.FC = () => {
 
   return (
     <DashboardLayout sidebar={<Sidebar />}>
-      <StatusNotifications
-        matchingStatus={matchingStatus}
-        successMessage={successMessage}
-        onDismissSuccess={dismissSuccess}
-        onDismissError={dismissError}
-      />
-
       <div className="min-h-screen bg-black text-white">
         <div className="px-8 pt-16 pb-8">
           <div className="max-w-7xl mx-auto">
diff --git a/apps/web/package.json b/apps/web/package.json
index feaafc66..6653d805 100644
--- a/apps/web/package.json
+++ b/apps/web/package.json
@@ -1,6 +1,6 @@
 {
   "name": "web",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "private": true,
   "type": "module",
   "scripts": {
diff --git a/docker/Dockerfile.background-jobs b/docker/Dockerfile.background-jobs
index 5e292b52..8b56d868 100644
--- a/docker/Dockerfile.background-jobs
+++ b/docker/Dockerfile.background-jobs
@@ -22,87 +22,74 @@ COPY apps/background-jobs/package.json ./apps/background-jobs/
 COPY apps/web/package.json ./apps/web/
 COPY packages/shared/package.json ./packages/shared/
 COPY packages/prisma ./packages/prisma/
+COPY .npmrc ./
+COPY prisma.config.ts ./
 
 RUN --mount=type=cache,id=pnpm,target=/pnpm/store \
     pnpm install --frozen-lockfile 
 
 
-FROM base AS python-deps
+FROM --platform=$BUILDPLATFORM python:3.11-slim-bookworm AS python-deps
+
+ARG TARGETARCH
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3=3.11.* \
-    python3-venv \
-    python3-pip \
-    ffmpeg \
-    cmake \
-    build-essential \
-    libboost-all-dev \
-    libopenblas-dev \
-    liblapack-dev \
-    && ln -sf /usr/bin/python3 /usr/bin/python \
-    && rm -rf /var/lib/apt/lists/* \
-    && apt-get clean
-
-RUN python3 -m venv /app/.venv
-ENV PATH="/app/.venv/bin:$PATH" \
-    VIRTUAL_ENV="/app/.venv" 
+    build-essential cmake libopenblas-dev && rm -rf /var/lib/apt/lists/*
 
+WORKDIR /app
 COPY python/requirements.txt ./python/
 
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install --upgrade pip setuptools wheel && \
-    pip install dlib-bin && \
-    pip install face_recognition && \
-    pip install fer && \
-    pip install -r python/requirements.txt
+RUN uv venv /app/.venv && \
+    VIRTUAL_ENV=/app/.venv uv pip install --no-cache -r python/requirements.txt
 
+    
+    
 FROM node-deps AS builder
 
-COPY --from=node-deps ./app/node_modules /app/node_modules
-COPY apps ./apps
-COPY packages ./packages
-COPY python ./python
-COPY tsconfig.json ./
+COPY . .
 
-RUN pnpm rebuild @tailwindcss/oxide rollup sharp
-RUN pnpm --filter shared build
-RUN pnpm --filter background-jobs build
+RUN --mount=type=cache,id=pnpm,target=/pnpm/store \
+    pnpm install --frozen-lockfile
+
+RUN pnpm --filter prisma generate
+
+RUN pnpm --filter shared build && \
+    pnpm --filter background-jobs build
+
+FROM base AS prod-deps
+
+COPY pnpm-workspace.yaml pnpm-lock.yaml package.json prisma.config.ts ./
+COPY apps/background-jobs/package.json ./apps/background-jobs/
+COPY apps/web/package.json ./apps/web/
+COPY packages/shared/package.json ./packages/shared/
+COPY packages/prisma ./packages/prisma/
+COPY .npmrc ./
+COPY prisma.config.ts ./
+
+RUN --mount=type=cache,id=pnpm,target=/pnpm/store \
+    pnpm install --prod --frozen-lockfile
 
 FROM base AS production
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3=3.11.* \
-    python3-venv \
-    libopenblas0 \
-    liblapack3 \
-    libx11-6 \
-    libgtk-3-0 \
-    libboost-python1.74.0 \
-    libboost-thread1.74.0 \
-    libboost-filesystem1.74.0 \
-    ffmpeg \
-    libsm6 \
-    libxext6 \
-    libxrender1 \
-    libgomp1 \
-    && ln -sf /usr/bin/python3 /usr/bin/python \
-    && rm -rf /var/lib/apt/lists/* \
-    && apt-get clean
+    python3 ffmpeg libopenblas0 liblapack3 libsm6 libxext6 libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
 
 COPY --from=python-deps /app/.venv /app/.venv
-COPY --from=builder /app/python /app/python
 
-COPY --from=builder /app/node_modules /app/node_modules
-COPY --from=builder /app/apps /app/apps
-COPY --from=builder /app/packages /app/packages
-COPY --from=builder /app/pnpm-workspace.yaml /app/pnpm-lock.yaml /app/package.json /app/prisma.config.ts /app/tsconfig.json ./
+COPY --from=prod-deps /app/node_modules /app/node_modules
 
+COPY --from=builder /app/apps/background-jobs/dist ./apps/background-jobs/dist
+COPY --from=builder /app/packages/shared/dist ./packages/shared/dist
+COPY --from=builder /app/packages/prisma ./packages/prisma
+COPY --from=builder /app/package.json /app/pnpm-workspace.yaml /app/prisma.config.ts ./
 
-ENV NODE_ENV=production \
-    PATH="/app/.venv/bin:$PATH" \
-    VIRTUAL_ENV="/app/.venv"
+ENV NODE_ENV=production VIRTUAL_ENV="/app/.venv" PATH="/app/.venv/bin:$PATH"
 
-WORKDIR /app/
 
 EXPOSE 4000 8765
 
@@ -118,30 +105,22 @@ FROM builder AS development
 
 COPY --from=python-deps /app/.venv /app/.venv
 COPY --from=python-deps /app/python /app/python
+COPY python/requirements-dev.txt ./python/
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3=3.11.* \
-    python3-venv \
     libopenblas0 \
     liblapack3 \
-    libx11-6 \
-    libgtk-3-0 \
-    libboost-python1.74.0 \
-    libboost-thread1.74.0 \
-    libboost-filesystem1.74.0 \
     ffmpeg \
-    libsm6 \
-    libxext6 \
-    libxrender1 \
-    libgomp1 \
-    && ln -sf /usr/bin/python3 /usr/bin/python \
-    && rm -rf /var/lib/apt/lists/* \
-    && apt-get clean
+    libx11-6 libgtk-3-0 libsm6 libxext6 libxrender1 libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
 
 ENV NODE_ENV=development \
     PATH="/app/.venv/bin:$PATH" \
     VIRTUAL_ENV="/app/.venv"
 
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install -r python/requirements-dev.txt
+
 RUN pnpm --filter background-jobs build
 
 WORKDIR /app/
@@ -153,4 +132,27 @@ CMD ["sh", "-c", "\
   pnpm --filter prisma generate && \
   pnpm --filter prisma seed && \
   pnpm --filter background-jobs dev \
-"]
\ No newline at end of file
+"]
+
+
+
+FROM base AS testing
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3 ffmpeg libopenblas0 liblapack3 libsm6 libxext6 libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
+
+COPY python ./python/
+
+RUN uv venv /app/.venv && \
+    VIRTUAL_ENV=/app/.venv uv pip install --no-cache -r python/requirements.txt && \
+    uv pip install --no-cache -r python/requirements-dev.txt
+
+ENV NODE_ENV=testing VIRTUAL_ENV="/app/.venv" PATH="/app/.venv/bin:$PATH"
+
+WORKDIR /app/python
+RUN pytest tests
\ No newline at end of file
diff --git a/docker/Dockerfile.web b/docker/Dockerfile.web
index da11fe43..5345131f 100644
--- a/docker/Dockerfile.web
+++ b/docker/Dockerfile.web
@@ -17,6 +17,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     openssl \
     ca-certificates \
     curl \
+    python3 \
     && rm -rf /var/lib/apt/lists/*
 
 COPY pnpm-workspace.yaml pnpm-lock.yaml package.json prisma.config.ts .npmrc ./
@@ -53,6 +54,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     openssl \
     ca-certificates \
     curl \
+    python3 \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
@@ -61,6 +63,7 @@ COPY --from=builder /app/pnpm-workspace.yaml ./
 COPY --from=builder /app/pnpm-lock.yaml ./
 COPY --from=builder /app/package.json ./
 COPY --from=builder /app/prisma.config.ts ./
+COPY --from=builder /app/tsconfig.json ./
 COPY --from=builder /app/.npmrc ./
 
 COPY --from=builder /app/apps/web/package.json ./apps/web/
@@ -95,6 +98,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     openssl \
     ca-certificates \
     curl \
+    python3 \
     && rm -rf /var/lib/apt/lists/
 
 WORKDIR /app
@@ -120,3 +124,40 @@ CMD ["sh", "-c", "\
   pnpm --filter prisma seed && \
   pnpm --filter web dev --host 0.0.0.0 --port 3745  \
 "]
+
+
+
+FROM base AS testing
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    openssl \
+    ca-certificates \
+    curl \
+    python3 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY --from=builder /app/pnpm-workspace.yaml ./
+COPY --from=builder /app/pnpm-lock.yaml ./
+COPY --from=builder /app/package.json ./
+COPY --from=builder /app/prisma.config.ts ./
+COPY --from=builder /app/tsconfig.json ./
+COPY --from=builder /app/.npmrc ./
+
+COPY --from=builder /app/apps/web/package.json ./apps/web/
+COPY --from=builder /app/apps/background-jobs/package.json ./apps/background-jobs/
+COPY --from=builder /app/packages/shared/package.json ./packages/shared/
+COPY --from=builder /app/packages/prisma/package.json ./packages/prisma/
+COPY --from=builder /app/packages/prisma/schema.prisma ./packages/prisma
+
+RUN --mount=type=cache,id=pnpm,target=/pnpm/store \
+    pnpm install --frozen-lockfile --prod=false
+
+COPY --from=builder /app/packages/prisma ./packages/prisma
+COPY --from=builder /app/packages/shared ./packages/shared
+COPY --from=builder /app/apps/web ./apps/web
+
+
+ENV NODE_ENV=testing
+RUN pnpm --filter shared test tests/services/vectorDb.test.ts tests/services/modelRouter.test.ts
\ No newline at end of file
diff --git a/docker/docker-compose.dev.yml b/docker/docker-compose.dev.yml
index 0a35288e..b09dd415 100644
--- a/docker/docker-compose.dev.yml
+++ b/docker/docker-compose.dev.yml
@@ -35,7 +35,7 @@ services:
       - ${HOST_MEDIA_PATH:-./media}:/media/videos
       - whisper-models:/app/models
       - yolo-models:/app/models/yolo
-      - background_jobs_node_modules:/app/node_modules
+      - background_jobs_node_modules-dev:/app/node_modules
   web:
     container_name: edit-mind-web-dev
     build:
@@ -61,21 +61,19 @@ services:
     environment:
       NODE_ENV: development
       DATABASE_URL: postgresql://user:password@postgres:5432/app
-      CHROMA_URL: http://chroma:8000
-      REDIS_URL: redis://redis:6379
     volumes:
       - ../apps/web:/app/apps/web
       - ../packages:/app/packages
       - ../data:/app/data
       - ../tsconfig.json:/app/tsconfig.json
       - ${HOST_MEDIA_PATH:-./media}:/media/videos
-      - web_node_modules:/app/node_modules
+      - web_node_modules-dev:/app/node_modules
     networks:
       - app-network
 
   chroma:
     image: chromadb/chroma:1.3.5
-    container_name: edit-mind-chroma
+    container_name: edit-mind-chroma-dev
     restart: unless-stopped
     environment:
       CHROMA_CORS_ALLOW_ORIGINS: '["http://localhost:3745", "http://localhost:4000", "http://web:3745", "http://background-jobs:4000"]'
@@ -96,7 +94,7 @@ services:
     image: redis:7-alpine
     container_name: edit-mind-redis-dev
     ports:
-      - "${REDIS_PORT:-6379}:6379"
+      - "${REDIS_PORT:-6380}:6380"
     volumes:
       - redis_data_dev:/data
     networks:
@@ -105,7 +103,7 @@ services:
     image: postgres:16-alpine
     container_name: edit-mind-postgres-dev
     ports:
-      - "${POSTGRES_PORT:-5433}:5432"
+      - "${POSTGRES_PORT:-5434}:5432"
     environment:
       POSTGRES_USER: ${POSTGRES_USER:-user}
       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-password}
@@ -131,9 +129,9 @@ volumes:
     driver: local
   yolo-models:
     driver: local
-  web_node_modules:
+  web_node_modules-dev:
     driver: local
-  background_jobs_node_modules:
+  background_jobs_node_modules-dev:
     driver: local
 
 networks:
diff --git a/package.json b/package.json
index f9ceabf8..4e2cabce 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "edit-mind",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "description": "AI-Powered Video Indexing and Search",
   "main": "./out/main/main.js",
   "license": "MIT",
@@ -12,9 +12,7 @@
     "type": "git",
     "url": "https://github.com/iliashad/edit-mind"
   },
-  "scripts": {
-    "postinstall": "pnpm prisma generate"
-  },
+  "scripts": {},
   "dependencies": {
     "@prisma/client": "6.19.0",
     "bcryptjs": "^2.4.3"
diff --git a/packages/prisma/package.json b/packages/prisma/package.json
index c5fec216..36fd6cd2 100644
--- a/packages/prisma/package.json
+++ b/packages/prisma/package.json
@@ -1,6 +1,6 @@
 {
   "name": "prisma",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "private": true,
   "license": "MIT",
   "author": {
diff --git a/packages/shared/constants/index.ts b/packages/shared/constants/index.ts
index 503e5472..a1932dad 100644
--- a/packages/shared/constants/index.ts
+++ b/packages/shared/constants/index.ts
@@ -12,11 +12,10 @@ export const IS_WIN = process.platform === 'win32'
 
 // Directories
 export const THUMBNAILS_DIR = process.env.THUMBNAILS_PATH || '.thumbnails'
-export const FACES_DIR = process.env.FACES_DIR || '.faces'
+export const FACES_DIR = process.env.FACES_DIR || '.faces_db'
 export const PROCESSED_VIDEOS_DIR = process.env.PROCESSED_VIDEOS_DIR || path.resolve('.results')
 export const DATA_DIR = path.resolve('data')
 export const UNKNOWN_FACES_DIR = process.env.UNKNOWN_FACES_DIR || '.unknown_faces'
-export const KNOWN_FACES_FILE = process.env.KNOWN_FACES_FILE || '.faces.json'
 export const BACKGROUND_JOBS_DIR = process.env.BACKGROUND_JOBS_DIR || '/apps/background-jobs'
 
 export const CACHE_FILE = '.locations.json'
diff --git a/packages/shared/package.json b/packages/shared/package.json
index 04ea40e3..9f5e6160 100644
--- a/packages/shared/package.json
+++ b/packages/shared/package.json
@@ -1,6 +1,6 @@
 {
   "name": "shared",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "description": "AI-Powered Video Indexing and Search",
   "type": "module",
   "main": "dist/index.js",
diff --git a/packages/shared/services/gemini.ts b/packages/shared/services/gemini.ts
index d1ffe5bb..dbe05006 100644
--- a/packages/shared/services/gemini.ts
+++ b/packages/shared/services/gemini.ts
@@ -1,4 +1,4 @@
-import { GenerateContentResult, GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from '@google/generative-ai'
+import { GenerateContentResult, GoogleGenerativeAI, HarmCategory, HarmBlockThreshold, GenerativeModel } from '@google/generative-ai'
 import { GEMINI_API_KEY, GEMINI_MODEL_NAME } from '../constants'
 import {
   GENERAL_RESPONSE_PROMPT,
@@ -20,13 +20,12 @@ import { VideoSearchParamsSchema } from '@shared/schemas/search'
 
 const CONTEXT_WINDOW_LIMIT = 20000 // based on gemini-2.5-pro
 
-if (!GEMINI_API_KEY) {
-  throw new Error('Gemini API key missing')
+let model: GenerativeModel;
+if (GEMINI_API_KEY) {
+  const genAI = new GoogleGenerativeAI(GEMINI_API_KEY)
+  model = genAI.getGenerativeModel({ model: GEMINI_MODEL_NAME })
 }
 
-const genAI = new GoogleGenerativeAI(GEMINI_API_KEY)
-const model = genAI.getGenerativeModel({ model: GEMINI_MODEL_NAME })
-
 const formatHistory = (chatHistory?: ChatMessage[]) => {
   return chatHistory?.length
     ? `Recent conversation:\n${chatHistory
diff --git a/packages/shared/services/immich.ts b/packages/shared/services/immich.ts
index e42f4382..0d217ad0 100644
--- a/packages/shared/services/immich.ts
+++ b/packages/shared/services/immich.ts
@@ -1,7 +1,7 @@
 import * as fs from 'fs/promises'
 import * as path from 'path'
 import fetch from 'node-fetch'
-import { FACES_DIR } from '../constants'
+import { FACES_DIR } from '../constants';
 import { AssetsBucketResponse, Face, ImmichConfig, PeopleResponse, Person, TimeBucket } from '../types/immich'
 import * as Jimp from 'jimp'
 import { logger } from './logger'
@@ -163,7 +163,7 @@ async function processAssetForPerson(
   const imageBuffer = await client.getAssetImage(assetId)
 
   const faceFiles = await Promise.all(
-    matchedFaces.map((face) => extractAndSaveFace(imageBuffer, face, assetId, personDir))
+    matchedFaces.map((face) => extractAndSaveFace(imageBuffer, face, personDir))
   )
 
   return faceFiles.filter((file): file is string => file !== null)
@@ -172,7 +172,6 @@ async function processAssetForPerson(
 async function extractAndSaveFace(
   imageBuffer: Buffer,
   face: Face,
-  assetId: string,
   personDir: string
 ): Promise<string | null> {
   try {
@@ -195,6 +194,7 @@ async function extractAndSaveFace(
   }
 }
 
+
 async function savePersonThumbnail(client: ImmichClient, personId: string, personDir: string) {
   try {
     const thumbnail = await client.getPersonThumbnail(personId)
@@ -206,5 +206,5 @@ async function savePersonThumbnail(client: ImmichClient, personId: string, perso
 }
 
 function sanitizeName(name: string): string {
-  return name.replace(/[^a-zA-Z0-9-_]/g, '_')
+  return name.replace(/[^a-zA-Z0-9-_]/g, '_');
 }
diff --git a/packages/shared/services/pythonService.ts b/packages/shared/services/pythonService.ts
index a980b8af..356dc7ec 100644
--- a/packages/shared/services/pythonService.ts
+++ b/packages/shared/services/pythonService.ts
@@ -4,10 +4,16 @@ import { spawn, ChildProcess } from 'child_process'
 import WebSocket from 'ws'
 import { Analysis, AnalysisProgress } from '../types/analysis'
 import { IS_WIN, MAX_RESTARTS, PYTHON_PORT, PYTHON_SCRIPT, RESTART_BACKOFF_MS, VENV_PATH } from '../constants'
-import { FaceIndexingProgress, FaceMatchingProgress, FindMatchingFacesResponse } from '../types/face'
 import { TranscriptionProgress } from '../types/transcription'
 import { logger } from './logger'
-import { CallbackMap, PythonMessageType } from '../types/python'
+import { PythonMessageType } from '../types/python'
+
+interface JobCallbacks {
+  onProgress?: (data: any) => void
+  onResult?: (data: any) => void
+  onError?: (error: Error) => void
+  onComplete?: () => void
+}
 
 class PythonService {
   private static instance: PythonService
@@ -16,7 +22,7 @@ class PythonService {
   private serviceUrl: string
   private isRunning = false
   private restartCount = 0
-  private messageCallbacks: CallbackMap = {}
+  private jobCallbacks: Map<string, JobCallbacks> = new Map()
   private port: string
   private startPromise: Promise<string> | null = null
 
@@ -38,7 +44,6 @@ class PythonService {
 
   public async start(): Promise<string> {
     if (this.startPromise) {
-      logger.debug('Python service already starting, waiting...')
       return this.startPromise
     }
 
@@ -79,8 +84,6 @@ class PythonService {
 
     const servicePath = PYTHON_SCRIPT
 
-    logger.debug(`Spawning Python service: ${pythonExecutable} ${servicePath}`)
-
     this.serviceProcess = spawn(pythonExecutable, [servicePath, '--port', this.port, '--host', '0.0.0.0'], {
       stdio: ['ignore', 'pipe', 'pipe'],
       detached: false,
@@ -90,16 +93,6 @@ class PythonService {
       throw new Error('Failed to spawn Python service process')
     }
 
-    logger.debug(`Python service spawned with PID: ${this.serviceProcess.pid}`)
-
-    this.serviceProcess.stdout?.on('data', (data) => {
-      logger.debug(`[PythonService STDOUT]: ${data.toString().trim()}`)
-    })
-
-    this.serviceProcess.stderr?.on('data', (data) => {
-      logger.debug(`[PythonService STDERR]: ${data.toString().trim()}`)
-    })
-
     this.serviceProcess.on('error', (error) => {
       logger.error('Python service process error: ' + error)
     })
@@ -111,7 +104,6 @@ class PythonService {
       this.handleCrash()
     })
 
-    logger.debug('Waiting for Python service to be ready...')
     const maxAttempts = 15
     const delayMs = 1000
 
@@ -130,7 +122,6 @@ class PythonService {
           logger.error(error)
           throw new Error(`Python service failed to start within ${maxAttempts * delayMs}ms`)
         }
-        logger.debug(`Attempt ${attempt} failed, retrying in ${delayMs}ms...`)
         await new Promise((resolve) => setTimeout(resolve, delayMs))
       }
     }
@@ -149,6 +140,7 @@ class PythonService {
       this.serviceProcess.kill('SIGTERM')
       this.serviceProcess = null
     }
+    this.jobCallbacks.clear()
     this.isRunning = false
   }
 
@@ -159,115 +151,62 @@ class PythonService {
     onResult: (result: Analysis) => void,
     onError: (error: Error) => void
   ): void {
-    if (!this.isRunning || !this.client) {
-      onError(new Error('Python service is not running.'))
-      return
-    }
-
-    if (this.client.readyState !== WebSocket.OPEN) {
-      onError(new Error(`WebSocket not open. State: ${this.client.readyState}`))
+    if (this.client?.readyState !== WebSocket.OPEN) {
+      onError(new Error(`WebSocket not open. State: ${this.client?.readyState}`))
       return
     }
 
-    this.messageCallbacks['analysis_progress'] = onProgress
-    this.messageCallbacks['analysis_completed'] = onResult
-    this.messageCallbacks['analysis_error'] = onError
+    this.jobCallbacks.set(job_id, {
+      onProgress,
+      onResult,
+      onError,
+    })
 
     const message = {
       type: 'analyze',
-      payload: { video_path: videoPath, job_id },
+      payload: { video_path: encodeURI(videoPath), job_id },
     }
 
-    this.client.send(JSON.stringify(message))
+    try {
+      this.client.send(JSON.stringify(message))
+    } catch (error) {
+      this.jobCallbacks.delete(job_id)
+      onError(new Error(`Failed to send message: ${error}`))
+    }
   }
 
-  public async transcribe(
+  public transcribe(
     videoPath: string,
     jsonFilePath: string,
     job_id: string,
     onProgress: (progress: TranscriptionProgress) => void,
     onComplete: (result: void) => void,
     onError: (error: Error) => void
-  ): Promise<void> {
-    if (!this.isRunning || !this.client) {
-      onError(new Error('Python service is not running.'))
-      return
-    }
-
-    if (this.client.readyState !== WebSocket.OPEN) {
-      onError(new Error(`WebSocket not open. State: ${this.client.readyState}`))
+  ): void {
+    if (this.client?.readyState !== WebSocket.OPEN) {
+      onError(new Error(`WebSocket not open. State: ${this.client?.readyState}`))
       return
     }
 
-    this.messageCallbacks['transcription_progress'] = onProgress
-    this.messageCallbacks['transcription_completed'] = onComplete
-    this.messageCallbacks['transcription_error'] = onError
+    this.jobCallbacks.set(job_id, {
+      onProgress,
+      onComplete,
+      onError,
+    })
 
     const message = {
       type: 'transcribe',
-      payload: { video_path: videoPath, json_file_path: jsonFilePath, job_id },
+      payload: { video_path: encodeURI(videoPath), json_file_path: jsonFilePath, job_id },
     }
 
-    this.client.send(JSON.stringify(message))
-  }
-
-  public reindexFaces(
-    specificFaces: { name: string; image_path: string }[],
-    jobId: string,
-    onProgress: (progress: FaceIndexingProgress) => void,
-    onComplete: (result: void) => void,
-    onError: (error: Error) => void
-  ): void {
-    if (!this.isRunning || !this.client) {
-      onError(new Error('Python service is not running.'))
-      return
-    }
-
-    this.messageCallbacks['reindex_progress'] = onProgress
-    this.messageCallbacks['reindex_complete'] = onComplete
-    this.messageCallbacks['reindex_error'] = onError
-
-    const message = {
-      type: 'reindex_faces',
-      specific_faces: specificFaces,
-      job_id: jobId,
+    try {
+      this.client?.send(JSON.stringify(message))
+    } catch (error) {
+      this.jobCallbacks.delete(job_id)
+      onError(new Error(`Failed to send message: ${error}`))
     }
-
-    this.client.send(JSON.stringify(message))
   }
-  public findMatchingFaces(
-    personName: string,
-    referenceImages: string[],
-    unknownFacesDir: string,
-    onProgress: (progress: FaceMatchingProgress) => void,
-    onComplete: (result: FindMatchingFacesResponse) => void,
-    onError: (error: Error) => void
-  ): void {
-    if (!this.isRunning || !this.client) {
-      onError(new Error('Python service is not running.'))
-      return
-    }
 
-    if (this.client.readyState !== WebSocket.OPEN) {
-      onError(new Error(`WebSocket not open. State: ${this.client.readyState}`))
-      return
-    }
-
-    this.messageCallbacks['face_matching_progress'] = onProgress
-    this.messageCallbacks['face_matching_complete'] = onComplete
-    this.messageCallbacks['face_matching_error'] = onError
-    const message = {
-      type: 'find_matching_faces',
-      payload: {
-        person_name: personName,
-        reference_images: referenceImages,
-        unknown_faces_dir: unknownFacesDir,
-        tolerance: 0.4,
-      },
-    }
-
-    this.client.send(JSON.stringify(message))
-  }
   public getServiceUrl(): string {
     return this.serviceUrl
   }
@@ -286,7 +225,9 @@ class PythonService {
         reject(new Error('WebSocket connection timeout'))
       }, 3000)
 
-      this.client = new WebSocket(this.serviceUrl)
+      this.client = new WebSocket(this.serviceUrl, {
+        maxPayload: 1024 * 1024 * 1024, // 1GB
+      })
 
       this.client.on('open', () => {
         clearTimeout(timeout)
@@ -297,14 +238,52 @@ class PythonService {
       this.client.on('message', (data) => {
         try {
           const message = JSON.parse(data.toString())
-          const { type, payload, job_id } = message
+          const { type, payload } = message
+          const job_id = payload?.job_id
+
+          if (!job_id) {
+            logger.warn(`⚠️ Received message without job_id: ${type}`)
+            return
+          }
 
-          const callback = this.messageCallbacks[type as PythonMessageType]
+          const callbacks = this.jobCallbacks.get(job_id)
+
+          if (!callbacks) {
+            logger.warn(`⚠️ No callbacks registered for job_id: ${job_id}`)
+            return
+          }
 
-          if (callback) {
-            callback({ ...payload, job_id })
-          } else {
-            logger.warn(`⚠️ No callback registered for message type: ${type}`)
+          switch (type as PythonMessageType) {
+            case 'analysis_progress':
+              callbacks.onProgress?.(payload)
+              break
+
+            case 'analysis_completed':
+              callbacks.onResult?.(payload)
+              this.jobCallbacks.delete(job_id)
+              break
+
+            case 'analysis_error':
+              callbacks.onError?.(new Error(payload.message || 'Analysis failed'))
+              this.jobCallbacks.delete(job_id)
+              break
+
+            case 'transcription_progress':
+              callbacks.onProgress?.(payload)
+              break
+
+            case 'transcription_completed':
+              callbacks.onComplete?.()
+              this.jobCallbacks.delete(job_id)
+              break
+
+            case 'transcription_error':
+              callbacks.onError?.(new Error(payload.message || 'Transcription failed'))
+              this.jobCallbacks.delete(job_id)
+              break
+
+            default:
+              logger.warn(`⚠️ Unknown message type: ${type}`)
           }
         } catch (error) {
           logger.error('❌ Error processing message: ' + error)
diff --git a/packages/shared/tests/services/vectorDb.test.ts b/packages/shared/tests/services/vectorDb.test.ts
index f027e4f4..3b25d61d 100644
--- a/packages/shared/tests/services/vectorDb.test.ts
+++ b/packages/shared/tests/services/vectorDb.test.ts
@@ -23,7 +23,7 @@ import { EmbeddingInput } from '@shared/types/vector'
 import { Scene } from '@shared/types/scene'
 import { VideoSearchParams } from '@shared/types/search'
 
-const TEST_TIMEOUT = 40000
+const TEST_TIMEOUT = 40000 * 2
 
 const mockScene = (overrides?: Partial<Scene>): Scene => ({
   id: `scene-${Date.now()}-${Math.random()}`,
diff --git a/packages/shared/types/analysis.ts b/packages/shared/types/analysis.ts
index 271605bb..9805279b 100644
--- a/packages/shared/types/analysis.ts
+++ b/packages/shared/types/analysis.ts
@@ -1,7 +1,10 @@
 export interface Face {
   name: string
   location: [number, number, number, number]
-  emotion?: Record<string, number>
+  emotion: {
+    label: string,
+    confidence: number
+  }
   bbox: BBox
   confidence: number
 }
diff --git a/packages/shared/types/face.ts b/packages/shared/types/face.ts
index 1869eb51..9532ee34 100644
--- a/packages/shared/types/face.ts
+++ b/packages/shared/types/face.ts
@@ -9,12 +9,7 @@ export interface FaceIndexingProgress {
 }
 export interface KnownFace {
   name: string
-  images: string[]
-}
-export interface AddFaceLabelingJobParams {
-  personName: string
-  referenceImages: string[]
-  unknownFacesDir: string
+  image: string
 }
 
 export type FaceIndexProgress = {
@@ -47,10 +42,6 @@ export interface FaceMatchingProgress {
   match?: MatchResult
 }
 
-interface FaceData {
-  video_path: string
-  timestamp_seconds: number
-}
 
 export interface FindMatchingFacesResponse {
   success: boolean
@@ -58,4 +49,11 @@ export interface FindMatchingFacesResponse {
   matches_found: number
   matches: MatchResult[]
   reference_images_used: number
+}
+export interface FaceLabellingJobData {
+  faces: {
+    jsonFile: string
+    faceId: string
+  }[]
+  name: string
 }
\ No newline at end of file
diff --git a/packages/shared/types/unknownFace.ts b/packages/shared/types/unknownFace.ts
new file mode 100644
index 00000000..5c4a4571
--- /dev/null
+++ b/packages/shared/types/unknownFace.ts
@@ -0,0 +1,53 @@
+interface BoundingBox {
+  top: number;
+  right: number;
+  bottom: number;
+  left: number;
+  width: number;
+  height: number;
+}
+
+interface FrameDimensions {
+  width: number;
+  height: number;
+}
+
+interface Label {
+  name: string | null;
+  labeled_by: string | null;
+  labeled_at: string | null;
+  confidence: number | null;
+  notes: string | null;
+}
+
+interface Appearance {
+  frame_index: number;
+  timestamp_ms: number;
+  timestamp_seconds: number;
+  formatted_timestamp: string;
+  bounding_box: BoundingBox;
+  padded_bounding_box: BoundingBox;
+}
+
+export interface FaceDetectionData {
+  image_file: string;
+  json_file: string;
+  image_hash: string;
+  created_at: string;
+  video_path: string;
+  video_name: string;
+  frame_index: number;
+  timestamp_ms: number;
+  timestamp_seconds: number;
+  formatted_timestamp: string;
+  frame_dimensions: FrameDimensions;
+  face_id: string;
+  bounding_box: BoundingBox;
+  padded_bounding_box: BoundingBox;
+  first_appearance: Appearance;
+  all_appearances: Appearance[];
+  total_appearances: number;
+  label: Label;
+  last_updated: string;
+  last_appearance: Appearance;
+}
\ No newline at end of file
diff --git a/packages/shared/utils/faces.ts b/packages/shared/utils/faces.ts
deleted file mode 100644
index 909829cf..00000000
--- a/packages/shared/utils/faces.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-import { logger } from '../services/logger'
-import { pythonService } from '../services/pythonService'
-import { FaceIndexProgress, FaceMatchingProgress, FindMatchingFacesResponse } from '../types/face'
-
-type ProgressCallback = (progress: FaceIndexProgress) => Promise<void>
-
-export function reindexFaces(
-  specificFaces: { name: string; image_path: string }[],
-  jobId: string,
-  onProgress?: ProgressCallback
-): Promise<void> {
-  return new Promise((resolve, reject) => {
-    pythonService.reindexFaces(
-      specificFaces,
-      jobId,
-      async (progress) => {
-        if (onProgress) {
-          try {
-            await onProgress(progress)
-          } catch (error) {
-            logger.error('❌ Error in progress callback:' + error)
-          }
-        }
-      },
-      (result) => {
-        resolve(result)
-      },
-      (error) => {
-        reject(error)
-      }
-    )
-  })
-}
-export function findMatchingFaces(
-  personName: string,
-  referenceImages: string[],
-  unknownFacesDir: string,
-  onProgress?: (progress: FaceMatchingProgress) => void
-): Promise<FindMatchingFacesResponse> {
-  return new Promise((resolve, reject) => {
-    pythonService.findMatchingFaces(
-      personName,
-      referenceImages,
-      unknownFacesDir,
-      (progress) => {
-        if (onProgress) {
-          onProgress(progress)
-        }
-      },
-      (result) => {
-        resolve(result)
-      },
-      (error) => {
-        reject(error)
-      }
-    )
-  })
-}
diff --git a/packages/shared/utils/scenes.ts b/packages/shared/utils/scenes.ts
index beb8ecad..f3bdc151 100644
--- a/packages/shared/utils/scenes.ts
+++ b/packages/shared/utils/scenes.ts
@@ -87,7 +87,7 @@ export const createScenes = async (
       transcription: getTranscriptionForTimeRange(startTime, endTime),
       description: generateSceneDescription(frame.objects, frame.faces),
       shot_type: frame.shot_type,
-      emotions: [],
+      emotions: frame.faces?.map((face) => ({ name: face.name, emotion: face.emotion.label })),
       source: videoPath,
       camera: '',
       createdAt: 0,
diff --git a/packages/ui/package.json b/packages/ui/package.json
index 94ca18a0..e8916a7d 100644
--- a/packages/ui/package.json
+++ b/packages/ui/package.json
@@ -1,6 +1,6 @@
 {
   "name": "ui",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "private": true,
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",
diff --git a/python/.faces/Aiony Haust/image.jpg b/python/.faces/Aiony Haust/image.jpg
new file mode 100644
index 00000000..6b52f443
Binary files /dev/null and b/python/.faces/Aiony Haust/image.jpg differ
diff --git a/python/.faces/Ilias/image.jpg b/python/.faces/Ilias/image.jpg
new file mode 100644
index 00000000..5cba9b47
Binary files /dev/null and b/python/.faces/Ilias/image.jpg differ
diff --git a/python/add_face.py b/python/add_face.py
deleted file mode 100644
index fbd79e59..00000000
--- a/python/add_face.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import sys
-import os
-import json
-import face_recognition
-
-def add_face(name: str, image_path: str, known_faces_file: str) -> None:
-    """
-    Extracts a face encoding from an image and adds it to the known faces file.
-    """
-    if not os.path.exists(image_path):
-        print(json.dumps({"status": "error", "message": "Image file not found."}), file=sys.stderr)
-        sys.exit(1)
-
-    try:
-        image = face_recognition.load_image_file(image_path)
-        face_encodings = face_recognition.face_encodings(image)
-
-        if len(face_encodings) == 0:
-            print(json.dumps({"status": "warning", "message": "No face detected in image."}), file=sys.stderr)
-            sys.exit(0)  # Not an error, just no face found
-
-        if len(face_encodings) > 1:
-            print(json.dumps({"status": "warning", "message": "Multiple faces found. Using the first one."}), file=sys.stderr)
-
-        new_encoding = face_encodings[0].tolist()
-
-        # Load existing known faces
-        known_faces = []
-        if os.path.exists(known_faces_file):
-            try:
-                with open(known_faces_file, 'r') as f:
-                    content = f.read().strip()
-                    if content:  # Only parse if file is not empty
-                        known_faces = json.load(open(known_faces_file, 'r'))
-                    else:
-                        known_faces = []
-            except json.JSONDecodeError as e:
-                print(json.dumps({
-                    "status": "error", 
-                    "message": f"Corrupted known_faces.json: {e.msg} at line {e.lineno}"
-                }), file=sys.stderr)
-                sys.exit(1)
-        
-        # Add new face encoding in the format expected by FaceRecognizer
-        # Format: [{"name": "Person Name", "encoding": [...]}, ...]
-        known_faces.append({
-            "name": name,
-            "encoding": new_encoding
-        })
-
-        # Write back to file with proper formatting
-        with open(known_faces_file, 'w') as f:
-            json.dump(known_faces, f, indent=2)
-        
-        print(json.dumps({
-            "status": "success", 
-            "message": f"Face for {name} added successfully.",
-            "total_encodings": len(known_faces)
-        }))
-        sys.exit(0)
-
-    except Exception as e:
-        print(json.dumps({
-            "status": "error", 
-            "message": f"Error processing image: {str(e)}"
-        }), file=sys.stderr)
-        import traceback
-        traceback.print_exc(file=sys.stderr)
-        sys.exit(1)
-
-if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print(json.dumps({
-            "status": "error",
-            "message": "Usage: python add_face.py <name> <image_path> <known_faces_file>"
-        }), file=sys.stderr)
-        sys.exit(1)
-    
-    name = sys.argv[1]
-    image_path = sys.argv[2]
-    known_faces_file = sys.argv[3]
-    add_face(name, image_path, known_faces_file)
\ No newline at end of file
diff --git a/python/analysis_service.py b/python/analysis_service.py
index a4dbdf95..9a3636a0 100644
--- a/python/analysis_service.py
+++ b/python/analysis_service.py
@@ -8,16 +8,13 @@
 from dataclasses import dataclass, field
 from enum import Enum
 from datetime import datetime
-from concurrent.futures import Future
 import websockets
 from websockets.legacy.server import WebSocketServerProtocol  
 from websockets.exceptions import ConnectionClosed, ConnectionClosedOK, ConnectionClosedError
 
 from transcribe import TranscriptionService
 from analyze import AnalysisConfig, OutputManager, VideoAnalysisResult, VideoAnalyzer
-from batch_add_faces import batch_add_faces_from_folder
-from face_matcher import FaceMatchingResult
-import os
+import urllib.parse
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -54,7 +51,6 @@ class MessageType(Enum):
     # Client requests
     ANALYZE = "analyze"
     TRANSCRIBE = "transcribe"
-    REINDEX_FACES = "reindex_faces"
     HEALTH = "health"
     
     # Server responses
@@ -66,13 +62,6 @@ class MessageType(Enum):
     TRANSCRIPTION_PROGRESS = "transcription_progress"
     TRANSCRIPTION_COMPLETED = "transcription_completed"
     TRANSCRIPTION_ERROR = "transcription_error"
-    REINDEX_PROGRESS = "reindex_progress"
-    REINDEX_COMPLETE = "reindex_complete"
-    REINDEX_ERROR = "reindex_error"
-    FIND_MATCHING_FACES = "find_matching_faces"
-    FACE_MATCHING_PROGRESS = "face_matching_progress"
-    FACE_MATCHING_COMPLETE = "face_matching_complete"
-    FACE_MATCHING_ERROR = "face_matching_error"
 
 @dataclass
 class ServiceMetrics:
@@ -262,7 +251,6 @@ def __init__(
             max_concurrent_analyses
         )
         self.transcription_service: "TranscriptionService" = TranscriptionService()
-        self.reindex_lock: asyncio.Lock = asyncio.Lock()
 
         # Track active callback guards for cleanup
         self.active_guards: Set["CallbackGuard"] = set()
@@ -273,9 +261,7 @@ def __init__(
         ] = {
             MessageType.ANALYZE.value: self._handle_analyze,
             MessageType.TRANSCRIBE.value: self._handle_transcribe,
-            MessageType.REINDEX_FACES.value: self._handle_reindex_faces,
             MessageType.HEALTH.value: self._handle_health,
-            MessageType.FIND_MATCHING_FACES.value: self._handle_find_matching_faces,
         }
     
     def cleanup_guards(self, websocket: "WebSocketServerProtocol") -> None:
@@ -368,7 +354,10 @@ async def _handle_analyze(
         self, websocket: "WebSocketServerProtocol", payload: JsonDict
     ) -> None:
         """Handle video analysis request with concurrency control."""
-        video_path_str = payload.get("video_path")
+        
+        encoded_path = payload['video_path']
+        video_path_str = urllib.parse.unquote(encoded_path)
+
         if not isinstance(video_path_str, str):
             logger.error("Missing or invalid 'video_path' in payload")
             await self._send_message(
@@ -541,10 +530,10 @@ async def _handle_transcribe(
         self, websocket: "WebSocketServerProtocol", payload: JsonDict
     ) -> None:
         """Handle transcription request with live progress updates and debug logging."""
-        video_path = payload.get("video_path")
+        encoded_path = payload.get("video_path")
         json_file_path = payload.get("json_file_path")
 
-        if not isinstance(video_path, str) or not isinstance(json_file_path, str):
+        if not isinstance(encoded_path, str) or not isinstance(json_file_path, str):
             await self._send_message(
                 websocket,
                 MessageType.TRANSCRIPTION_ERROR,
@@ -558,7 +547,7 @@ async def _handle_transcribe(
             logger.error("Missing or invalid 'job_id' in payload")
             job_id = None  # Continue but log the issue
             
-        video_path_normalized = str(Path(video_path).resolve())
+        video_path_normalized = urllib.parse.unquote(encoded_path)
         self.state.start_transcription(video_path_normalized)
         logger.info(f"Started transcription for: {video_path_normalized}")
 
@@ -580,7 +569,7 @@ def progress_callback(progress: int, elapsed: str) -> None:
             result = await loop.run_in_executor(
                 None,
                 lambda: self.transcription_service.transcribe(
-                    video_path,
+                    video_path_normalized,
                     json_file_path,
                     progress_callback
                 )
@@ -607,209 +596,6 @@ def progress_callback(progress: int, elapsed: str) -> None:
             )
         finally:
             self.state.finish_transcription(video_path_normalized)
-            
-    async def _handle_find_matching_faces(
-        self, websocket: "WebSocketServerProtocol", payload: JsonDict
-    ) -> None:
-        """Handle face matching request."""
-        person_name = payload.get("person_name")
-        reference_images = payload.get("reference_images")
-        unknown_faces_dir = payload.get("unknown_faces_dir", "analysis_results/unknown_faces")
-        tolerance = payload.get("tolerance", 0.6)
-        
-        if not isinstance(person_name, str) or not person_name:
-            await self._send_message(
-                websocket,
-                MessageType.FACE_MATCHING_ERROR,
-                {"message": "Missing or invalid 'person_name'"}
-            )
-            return
-        
-        if not isinstance(reference_images, list) or not reference_images:
-            await self._send_message(
-                websocket,
-                MessageType.FACE_MATCHING_ERROR,
-                {"message": "Missing or invalid 'reference_images'"}
-            )
-            return
-        
-        job_id = payload.get("job_id")
-        if not isinstance(job_id, str):
-            logger.error("Missing or invalid 'job_id' in payload")
-            job_id = None
-        
-        logger.info(f"Starting face matching for {person_name} with {len(reference_images)} references")
-        
-        guard = CallbackGuard(websocket, self.connection_manager)
-        self.active_guards.add(guard)
-        
-        loop = asyncio.get_running_loop()
-        
-        def progress_callback(data: Dict[str, str]) -> None:
-            """Thread-safe synchronous progress callback for face matching."""            
-            progress_data: JsonDict = {
-                "person_name": person_name,
-                **data
-            }
-       
-            asyncio.run_coroutine_threadsafe(
-                self._send_message(websocket, MessageType.FACE_MATCHING_PROGRESS, progress_data, job_id=job_id),
-                loop
-            )
-            logger.debug(f"Sent face matching progress: {progress_data}")
-          
-        
-        try:
-            from face_matcher import find_and_label_matching_faces
-            
-            logger.info(f"Running face matching for {person_name}")
-            result: FaceMatchingResult = await find_and_label_matching_faces(
-                person_name=person_name,
-                reference_image_paths=reference_images,
-                unknown_faces_dir=unknown_faces_dir,
-                tolerance=tolerance,
-                progress_callback=progress_callback
-            )
-            
-            if result["success"]:
-                logger.info(f"Face matching complete: {result['matches_found']} matches found for {person_name}")
-                complete_data: JsonDict = {
-                    "person_name": person_name,
-                    "matches_found": result["matches_found"],
-                    "matches": result["matches"],
-                    "reference_images_used": result["reference_images_used"]
-                }
-                try:
-                    await self._send_message(
-                        websocket,
-                        MessageType.FACE_MATCHING_COMPLETE,
-                        complete_data,
-                        job_id=job_id
-                    )
-                except Exception as e:
-                    logger.warning(f"Failed to send face matching complete event: {e}")
-            else:
-                await self._send_message(
-                    websocket,
-                    MessageType.FACE_MATCHING_ERROR,
-                    {"message": result.get("error", "Unknown error")},
-                    job_id=job_id
-                )
-        
-        except Exception as e:
-            logger.exception(f"Face matching failed for {person_name}")
-            await self._send_message(
-                websocket,
-                MessageType.FACE_MATCHING_ERROR,
-                {"message": f"Face matching failed: {str(e)}"},
-                job_id=job_id
-            )
-        finally:
-            guard.cancel()
-            self.active_guards.discard(guard)
-
-
-    async def _handle_reindex_faces(
-        self, websocket: "WebSocketServerProtocol", payload: JsonDict
-    ) -> None:
-        """Handle face reindexing request with exclusive locking."""
-        if self.reindex_lock.locked():
-            logger.warning("Reindex request received while another reindex is active.")
-            await self._send_message(
-                websocket,
-                MessageType.REINDEX_ERROR,
-                {"message": "Face reindexing is already in progress. Please wait."}
-            )
-            return
-
-        job_id = payload.get("job_id")
-        specific_faces = payload.get("specific_faces")
-        if not isinstance(job_id, str):
-            logger.error("Missing or invalid 'job_id' in payload")
-            job_id = None
-
-        async with self.reindex_lock:
-            logger.info("Starting face reindexing...")
-            
-            faces_dir = os.getenv("FACES_DIR", ".faces")
-            known_faces_f = os.getenv("KNOWN_FACES_FILE_LOADED", ".known_faces.json")
-            
-            if not isinstance(faces_dir, str):
-                faces_dir = ".faces"
-            if not isinstance(known_faces_f, str):
-                known_faces_f = "known_faces.json"
-            
-            guard = CallbackGuard(websocket, self.connection_manager)
-            self.active_guards.add(guard)
-            
-            loop = asyncio.get_running_loop()
-            
-            def reindex_progress_callback(data: Dict[str, Union[str, int]]) -> None:
-                """Thread-safe synchronous progress callback for reindexing."""
-                if not guard.is_active():
-                    return
-                
-                elapsed = data.get("elapsed", "")
-                progress = data.get("progress", 0)
-                
-                if not isinstance(elapsed, str):
-                    elapsed = str(elapsed)
-                if not isinstance(progress, int):
-                    progress = 0
-                
-                progress_data: JsonDict = {
-                    "elapsed": elapsed,
-                    "progress": progress
-                }
-                
-                asyncio.run_coroutine_threadsafe(
-                    self._send_message(websocket, MessageType.REINDEX_PROGRESS, progress_data, job_id=job_id),
-                    loop
-                    )
-               
-
-            try:
-                logger.info("Running face reindexing")
-                success = await batch_add_faces_from_folder(
-                    progress_callback=reindex_progress_callback,
-                    specific_faces=specific_faces
-                )
-                
-                if success:
-                    logger.info("Face reindexing completed successfully.")
-                    complete_data: JsonDict = {
-                        "status": "done",
-                        "message": "Face reindexing completed successfully."
-                    }
-                    try:
-                        await self._send_message(
-                            websocket,
-                            MessageType.REINDEX_COMPLETE,
-                            complete_data,
-                            job_id=job_id
-                        )
-                    except Exception as e:
-                        logger.warning(f"Failed to send reindex complete event: {e}")
-                else:
-                    logger.error("Face reindexing failed. Check logs for details.")
-                    await self._send_message(
-                        websocket,
-                        MessageType.REINDEX_ERROR,
-                        {"message": "Face reindexing failed. Check service logs."},
-                        job_id=job_id
-                    )
-            
-            except Exception as e:
-                logger.exception("Face reindexing exception")
-                await self._send_message(
-                    websocket,
-                    MessageType.REINDEX_ERROR,
-                    {"message": f"Reindexing failed: {str(e)}"},
-                    job_id=job_id
-                )
-            finally:
-                guard.cancel()
-                self.active_guards.discard(guard)
 class WebSocketHandler:
     """Coordinates WebSocket connections and message processing."""
     
diff --git a/python/analyze.py b/python/analyze.py
index 9305b034..0993f9e4 100644
--- a/python/analyze.py
+++ b/python/analyze.py
@@ -385,8 +385,6 @@ def load_plugins(config: AnalysisConfig) -> List[AnalyzerPlugin]:
         ("ShotTypePlugin", "shot_type"),
         ("DominantColorPlugin", "dominant_color"),
         ("EnvironmentPlugin", "environment"),
-        ("ActivityPlugin", "activity"),
-        ("EmotionDetectionPlugin", "emotion_detection"),
         ("TextDetectionPlugin", "text_detection"),
     ]
 
diff --git a/python/batch_add_faces.py b/python/batch_add_faces.py
deleted file mode 100644
index c3c4e7cc..00000000
--- a/python/batch_add_faces.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import sys
-import os
-import json
-import asyncio
-from pathlib import Path
-from typing import Optional, Callable, List, Dict, Union
-import time 
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-async def create_faces_data_from_folder_async():
-    """
-    Asynchronously scans the specified faces directory, expecting subfolders named after individuals,
-    collects image paths, and optionally saves this data to a JSON file.
-    """
-    faces_data = {}
-    faces_directory= os.getenv("FACES_DIR", ".faces")
-
-    output_json_path = os.getenv("KNOWN_FACES_FILE", ".known_faces.json")
-    def _sync_scan():
-        if not os.path.exists(faces_directory):
-            print(f"ERROR(create_faces): .faces directory not found at {faces_directory}", file=sys.stderr)
-            return {}
-
-        found_people_count = 0
-        for person_name in os.listdir(faces_directory):
-            person_folder_path = os.path.join(faces_directory, person_name)
-            if os.path.isdir(person_folder_path):
-                found_people_count += 1
-                image_paths = []
-                for filename in os.listdir(person_folder_path):
-                    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
-                        relative_path = os.path.join(faces_directory, person_name, filename)
-                        image_paths.append(relative_path.replace("\\", "/"))
-                if image_paths:
-                    faces_data[person_name] = image_paths
-        return faces_data
-
-    faces_data = await asyncio.to_thread(_sync_scan)
-    
-    if faces_data:
-        try:
-            output_dir = os.path.dirname(output_json_path)
-            if output_dir and not os.path.exists(output_dir):
-                print(f"DEBUG(create_faces): Creating output directory: {output_dir}", file=sys.stderr)
-                os.makedirs(output_dir)
-
-            def _sync_dump():
-                with open(output_json_path, 'w') as f:
-                    json.dump(faces_data, f, indent=2)
-            await asyncio.to_thread(_sync_dump)
-            print(f"Successfully generated and saved faces data to {output_json_path}", file=sys.stderr)
-        except IOError as e:
-            print(f"Error saving faces data to {output_json_path}: {e}", file=sys.stderr)
-    else:
-        print(f"No faces data found in '{faces_directory}' to save to '{output_json_path}'.", file=sys.stderr)
-
-    return faces_data
-
-async def batch_add_faces_from_folder(
-    progress_callback: Optional[Callable[[Dict[str, Union[int, float]]], None]] = None,
-    specific_faces: Optional[List[Dict[str, str]]] = None
-):
-    """
-    If specific_faces is provided, only processes those specific images.
-    specific_faces format: 
-      [{"name": "John", "image_path": "photo1.jpg"}, ...]  # just filename
-    """
-    
-    faces_directory = os.getenv("FACES_DIR", ".faces")
-    known_faces_file = os.getenv("KNOWN_FACES_FILE_LOADED", ".known_faces.json")
-    
-    start_time = time.monotonic() 
-
-    async def report_progress(message: str, current: int = 0, total: int = 0):
-        elapsed = time.monotonic() - start_time
-        progress_percent = (current / total * 100) if total > 0 else 0
-        
-        hours, remainder = divmod(int(elapsed), 3600)
-        minutes, seconds = divmod(remainder, 60)
-        elapsed_str = f"{hours:02}:{minutes:02}:{seconds:02}"
-
-        if progress_callback:
-            try:
-                progress_callback({
-                    "current_item": current,
-                    "total_items": total,
-                    "progress": round(progress_percent, 1),
-                    "elapsed": int(elapsed)
-                })
-            except Exception as e:
-                print(f"Failed to send progress via callback: {e}", file=sys.stderr)
-
-        print(f"[{elapsed_str} | {progress_percent:.1f}%] {message}", file=sys.stderr)
-
-    if specific_faces is not None:
-        await report_progress("Using provided face list...", current=0, total=1)
-        
-        images_to_process = []
-        for face_info in specific_faces:
-            name = face_info['name']
-            image_path = face_info['image_path']
-            
-            image_path = os.path.join(faces_directory, name, image_path)
-            
-            images_to_process.append({
-                'name': name,
-                'image_path': image_path
-            })
-
-    script_dir = Path(__file__).parent
-    add_face_script = script_dir / "add_face.py"
-    
-    project_root = Path(os.getcwd())
-    
-    total_images = len(images_to_process)
-    processed_images = 0
-
-    if total_images == 0:
-        await report_progress("No images found to process.", current=0, total=0)
-        return True
-
-    for image_info in images_to_process:
-        name = image_info['name']
-        image_path_relative = image_info['image_path']
-        
-        processed_images += 1
-        
-        message_prefix = f"Processing image: {image_path_relative} (person: {name})"
-        await report_progress(message_prefix, processed_images, total_images)
-        
-        # Handle both relative and absolute paths
-        if os.path.isabs(image_path_relative):
-            absolute_image_path = Path(image_path_relative)
-        else:
-            absolute_image_path = project_root / image_path_relative
-        
-        if not absolute_image_path.exists():
-            warning_msg = f"Image not found at {absolute_image_path}. Skipping."
-            await report_progress(warning_msg, processed_images, total_images)
-            continue
-
-        command = [
-            sys.executable,
-            str(add_face_script),
-            name,
-            str(absolute_image_path),
-            str(project_root / known_faces_file)
-        ]
-        process = await asyncio.create_subprocess_exec(
-            *command,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.PIPE
-        )
-        
-        stdout_data = await process.communicate()
-        return_code = process.returncode 
-        
-        stdout_str = stdout_data.decode().strip()
-        if return_code == 0:
-            status_message = "Success"
-            if stdout_str:
-                try:
-                    print(stdout_str)
-                    json_output = json.loads(stdout_str)
-                    if json_output.get("status") == "warning":
-                        status_message = f"Warning: {json_output.get('message', 'Multiple faces found.')}"
-                    elif json_output.get("status") == "error":
-                        status_message = f"Error (despite code 0): {json_output.get('message', 'Unknown error')}"
-                except json.JSONDecodeError:
-                    status_message = f"Success (non-JSON output: {stdout_str[:50]}...)"
-            
-            await report_progress(status_message, processed_images, total_images)
-        else:
-            error_msg = f"add_face.py failed for {image_path_relative}. Return code: {process.returncode}"
-            print(error_msg, file=sys.stderr)
-            await report_progress(error_msg, processed_images, total_images)
-            return False
-
-    await report_progress("All faces processed.", total_images, total_images)
-    return True
\ No newline at end of file
diff --git a/python/face_matcher.py b/python/face_matcher.py
deleted file mode 100644
index bb46dbb2..00000000
--- a/python/face_matcher.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import os
-import sys
-import json
-import face_recognition
-import numpy as np
-from typing import List, Dict, Optional, Callable, TypedDict, Union
-import time
-from dataclasses import dataclass
-import logging
-
-logger = logging.getLogger(__name__)
-
-class FaceMatchingResult(TypedDict):
-    success: bool
-    error: Optional[str]
-    matches: List[Dict[str, str]]
-    person_name: str
-    matches_found: int
-    reference_images_used: int
-
-@dataclass
-class MatchResult:
-    """Result of a face match operation."""
-    json_file: str
-    image_file: str
-    confidence: float
-    face_data: dict
-
-class FaceMatcher:
-    """Handles face matching and labeling operations."""
-    
-    def __init__(self, tolerance: float = 0.6):
-        """
-        Initialize the face matcher.
-        
-        Args:
-            tolerance: Lower is more strict. Default 0.6 is a good balance.
-        """
-        self.tolerance = tolerance
-    
-    def load_reference_encodings(self, image_paths: List[str]) -> List[np.ndarray]:
-        """
-        Load face encodings from reference images.
-        
-        Args:
-            image_paths: List of paths to reference images
-            
-        Returns:
-            List of face encodings
-        """
-        encodings = []
-        
-        for image_path in image_paths:
-            try:
-                image = face_recognition.load_image_file(image_path)
-                face_encodings = face_recognition.face_encodings(image)
-                
-                if face_encodings:
-                    encodings.append(face_encodings[0])
-                    print(f"Loaded encoding from: {image_path}", file=sys.stderr)
-                else:
-                    print(f"Warning: No face found in {image_path}", file=sys.stderr)
-            except Exception as e:
-                print(f"Error loading {image_path}: {e}", file=sys.stderr)
-        
-        return encodings
-    
-    def remove_json_file(self, json_path: str, reason: str):
-        """
-        Remove a JSON file and log the reason.
-        
-        Args:
-            json_path: Path to the JSON file to remove
-            reason: Reason for removal
-        """
-        try:
-            os.remove(json_path)
-            print(f"Removed JSON file: {json_path} - Reason: {reason}", file=sys.stderr)
-            logger.info(f"Removed JSON file: {json_path} - Reason: {reason}")
-        except Exception as e:
-            print(f"Error removing {json_path}: {e}", file=sys.stderr)
-            logger.error(f"Error removing {json_path}: {e}")
-    
-    def find_matches(
-        self,
-        reference_encodings: List[np.ndarray],
-        unknown_faces_dir: str,
-        progress_callback: Optional[Callable[[Dict[str, Union[int, float]]], None]] = None,
-    ) -> List[MatchResult]:
-        """
-        Find all matching faces in the unknown faces directory.
-        
-        Args:
-            reference_encodings: List of face encodings to match against
-            unknown_faces_dir: Directory containing unknown face JSON files
-            progress_callback: Optional callback for progress updates
-            
-        Returns:
-            List of matching face results
-        """
-        matches = []
-        json_files = [f for f in os.listdir(unknown_faces_dir) if f.endswith('.json')]
-        total_files = len(json_files)
-        start_time = time.monotonic()
-        
-        print(f"Scanning {total_files} unknown faces for matches...", file=sys.stderr)
-        
-        for idx, json_file in enumerate(json_files, 1):
-            json_path = os.path.join(unknown_faces_dir, json_file)
-            
-            try:
-                # Load face data
-                with open(json_path, 'r') as f:
-                    face_data = json.load(f)
-                
-                image_file = face_data.get('image_file')
-                if not image_file:
-                    self.remove_json_file(json_path, "No image_file field in JSON")
-                    continue
-                
-                image_path = os.path.join(unknown_faces_dir, image_file)
-                
-                # Check if image file exists
-                if not os.path.exists(image_path):
-                    print(f"Image not found: {image_path}", file=sys.stderr)
-                    self.remove_json_file(json_path, f"Image file not found: {image_file}")
-                    continue
-                
-                # Load and encode the unknown face
-                unknown_image = face_recognition.load_image_file(image_path)
-                unknown_encodings = face_recognition.face_encodings(unknown_image)
-                
-                # Check if face was detected
-                if not unknown_encodings:
-                    print(f"No face found in {image_file}", file=sys.stderr)
-                    self.remove_json_file(json_path, f"No face detected in image: {image_file}")
-                    continue
-                
-                unknown_encoding = unknown_encodings[0]
-                
-                # Compare with reference encodings
-                distances = face_recognition.face_distance(reference_encodings, unknown_encoding)
-                min_distance = np.min(distances)
-                
-                if min_distance <= self.tolerance:
-                    confidence = 1.0 - min_distance
-                    matches.append(MatchResult(
-                        json_file=json_file,
-                        image_file=image_file,
-                        confidence=float(confidence),
-                        face_data=face_data
-                    ))
-                    logger.info(f"Face match found: {image_file} (confidence: {confidence:.2%})")
-                    face_id = self.get_face_id_from_json(json_file, unknown_faces_dir)
-                    elapsed = time.monotonic() - start_time
-                    progress_percent = (idx / total_files) * 100
-                    if progress_callback:
-                        progress_callback({
-                            "current": idx,
-                            "total": total_files,
-                            "progress": round(progress_percent, 1),
-                            "elapsed": int(elapsed),
-                            "match": {
-                                "json_file": json_file,
-                                "image_file": image_file,
-                                "confidence": float(confidence),
-                                "face_id": face_id,
-                                "face_data": face_data
-                            }
-                        })
-                    logger.info(f"Face match found and progress callback sent")
-                
-            except Exception as e:
-                print(f"Error processing {json_file}: {e}", file=sys.stderr)
-                logger.error(f"Error processing {json_file}: {e}")
-        
-        return matches
-    
-    def get_face_id_from_json(self, json_file: str, unknown_faces_dir: str) -> Optional[str]:
-        """Extract face_id from JSON file."""
-        json_path = os.path.join(unknown_faces_dir, json_file)
-        try:
-            with open(json_path, 'r') as f:
-                data = json.load(f)
-                return data.get('image_hash')
-        except Exception as e:
-            print(f"Error reading face_id from {json_file}: {e}", file=sys.stderr)
-            return None
-
-
-async def find_and_label_matching_faces(
-    person_name: str,
-    reference_image_paths: List[str],
-    unknown_faces_dir: str = "analysis_results/unknown_faces",
-    tolerance: float = 0.6,
-    progress_callback: Optional[Callable[[dict], None]] = None
-) -> FaceMatchingResult:
-    """
-    Find and return all faces matching the reference images.
-    
-    Args:
-        person_name: Name of the person to label
-        reference_image_paths: List of paths to reference images (the ones just labeled)
-        unknown_faces_dir: Directory containing unknown faces
-        tolerance: Face recognition tolerance (lower = more strict)
-        progress_callback: Optional callback for progress updates
-        
-    Returns:
-        Dictionary with matching faces information
-    """
-    import asyncio
-    
-    matcher = FaceMatcher(tolerance=tolerance)
-    
-    # Load reference encodings
-    print(f"Loading {len(reference_image_paths)} reference images for {person_name}...", file=sys.stderr)
-    reference_encodings = await asyncio.to_thread(
-        matcher.load_reference_encodings,
-        reference_image_paths
-    )
-    
-    if not reference_encodings:
-        return {
-            "success": False,
-            "error": "No valid face encodings found in reference images",
-            "matches": [],
-            "person_name": person_name,
-            "matches_found": 0,
-            "reference_images_used": 0,
-        }
-    
-    print(f"Found {len(reference_encodings)} reference encodings", file=sys.stderr)
-    
-    matches = await asyncio.to_thread(
-        matcher.find_matches,
-        reference_encodings,
-        unknown_faces_dir,
-        progress_callback
-    )
-    
-    match_data = []
-    for match in matches:
-        face_id = matcher.get_face_id_from_json(match.json_file, unknown_faces_dir)
-        match_data.append({
-            "json_file": match.json_file,
-            "image_file": match.image_file,
-            "confidence": match.confidence,
-            "face_id": face_id,
-            "face_data": match.face_data
-        })
-    
-    return {
-        "success": True,
-        "person_name": person_name,
-        "matches_found": len(match_data),
-        "matches": match_data,
-        "reference_images_used": len(reference_encodings),
-        "error": None,
-    }
\ No newline at end of file
diff --git a/python/face_recognizer.py b/python/face_recognizer.py
index 3accffa4..d4161cbb 100644
--- a/python/face_recognizer.py
+++ b/python/face_recognizer.py
@@ -1,245 +1,183 @@
-import face_recognition
+from deepface import DeepFace
 import numpy as np
 import json
 from collections import defaultdict
-import os
 from typing import List, Dict
+import os
 from dotenv import load_dotenv
+import cv2
 
 load_dotenv()
+
 class FaceRecognizer:
-    def __init__(self, known_faces_file: str ='.faces.json', tolerance: float =0.40, model: str ='cnn'):
+    def __init__(self, known_faces_folder: str = '.faces', tolerance: float = 0.40, model: str = 'VGG-Face'):
         """
-        Initialize the face recognizer.
+        Initialize the face recognizer using DeepFace.
         
         Args:
-            known_faces_file: Path to JSON file storing known faces
-            tolerance: Lower is more strict (default 0.6, range 0.0-1.0)
-            model: 'cnn' for accuracy or 'hog' for speed
+            known_faces_folder: Path to folder where we have faces labeled 
+            tolerance: Distance threshold for face matching (lower = stricter)
+            model: DeepFace model ('VGG-Face', 'Facenet', 'Facenet512', 'OpenFace', 'DeepFace', 'DeepID', 'ArcFace', 'Dlib', 'SFace')
         """
-        self.known_faces_file = known_faces_file
+        self.known_faces_folder = known_faces_folder
         self.tolerance = tolerance
-        self.model = model
-        self.known_face_encodings: List[np.ndarray] = []
-        self.known_face_names: List[str] = []
+        self.model = model 
+        self.detector_backend = 'opencv' 
         self.unknown_face_encodings: Dict[str, List[np.ndarray]] = defaultdict(list)
         self.unknown_face_counter = 0
-        self.load_known_faces()
-
-
-                
-    def load_known_faces(self):
-        """Load known faces from JSON ensuring float arrays."""
-        self.known_face_encodings = []
-        self.known_face_names = []
-
-        try:
-            with open(self.known_faces_file, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-        except FileNotFoundError:
-            # File doesn't exist yet, start with empty lists
-            return
-        except json.JSONDecodeError as e:
-            raise ValueError(f"Invalid JSON in {self.known_faces_file}: {e}")
-
-        if isinstance(data, dict):
-            for name, enc_list in data.items():
-                # Skip if enc_list is not a list
-                if not isinstance(enc_list, list):
-                    continue
-                    
-                for enc in enc_list:
-                    # Skip if enc is not a list/array (e.g., if it's a string path)
-                    if not isinstance(enc, (list, np.ndarray)):
-                        continue
-                    
-                    # Skip if enc is empty or doesn't look like an encoding (should be 128 floats)
-                    if not enc or len(enc) != 128:
-                        continue
-                    
-                    try:
-                        # Ensure numeric array
-                        encoding_array = np.array(enc, dtype=np.float64)
-                        self.known_face_encodings.append(encoding_array)
-                        self.known_face_names.append(name)
-                    except (ValueError, TypeError) as e:
-                        # Skip invalid encodings
-                        print(f"Warning: Skipping invalid encoding for {name}: {e}")
-                        continue
-
-        elif isinstance(data, list):
-            for entry in data:
-                if not isinstance(entry, dict):
-                    continue
-                    
-                name = entry.get("name")
-                enc = entry.get("encoding") or entry.get("encodings")
                 
-                if not name or not enc:
-                    continue
-                
-                # Handle multiple encodings per person
-                if isinstance(enc[0], list):
-                    for e in enc:
-                        if len(e) != 128:
-                            continue
-                        try:
-                            self.known_face_encodings.append(np.array(e, dtype=np.float64))
-                            self.known_face_names.append(name)
-                        except (ValueError, TypeError):
-                            continue
-                else:
-                    if len(enc) == 128:
-                        try:
-                            self.known_face_encodings.append(np.array(enc, dtype=np.float64))
-                            self.known_face_names.append(name)
-                        except (ValueError, TypeError):
-                            continue
-
-
-    def recognize_faces(self, frame: np.ndarray, upsample: int =1) -> List[Dict[str, str]]:
+    def recognize_faces(self, frame: np.ndarray, upsample: int = 1) -> List[Dict[str, str]]:
         """
-        Recognize faces in a frame with improved accuracy.
+        Recognize faces in a frame using DeepFace.
         
         Args:
-            frame: Image array (RGB format recommended)
-            upsample: Number of times to upsample image for detection (higher = more accurate but slower)
+            frame: Image array (BGR format from OpenCV)
+            upsample: Not used with DeepFace but kept for compatibility
         """
-        # Convert to RGB if needed (face_recognition uses RGB)
-        if len(frame.shape) == 2:  # Grayscale
-            frame = np.stack([frame] * 3, axis=-1)
-        
-        # Detect faces with upsampling for better detection
-        face_locations = face_recognition.face_locations(
-            frame, 
-            number_of_times_to_upsample=upsample,
-            model=self.model
-        )
-        
-        # Get 128-dimensional face encodings
-        face_encodings = face_recognition.face_encodings(
-            frame, 
-            face_locations,
-            num_jitters=10  # More jitters = more accurate but slower
-        )
-        def distance_to_confidence(face_distance: float, tolerance: float =self.tolerance) -> float:
-            """Convert face distance to confidence (0-1 scale)."""
-            if face_distance > tolerance:
-                return 0.0
-            # Smooth mapping: closer distance -> higher confidence
-            confidence = 1 / (1 + np.exp(15 * (face_distance - tolerance)))
-            return confidence
+        # Convert BGR to RGB if needed
+        if len(frame.shape) == 3 and frame.shape[2] == 3:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        else:
+            frame_rgb = frame
         
         recognized_faces = []
-        for face_location, face_encoding in zip(face_locations, face_encodings):
-            name = "Unknown"
-            confidence = 0.0
+        
+        try:
 
-            if len(self.known_face_encodings) > 0:
-                # Calculate distances to all known faces
-                face_distances = face_recognition.face_distance(
-                    self.known_face_encodings, 
-                    face_encoding,
-                )
+            analysis_results = DeepFace.analyze(
+                img_path=frame_rgb,
+                actions=['emotion'],
+                enforce_detection=False,
+                detector_backend=self.detector_backend,
+            )
+            if not isinstance(analysis_results, list):
+                analysis_results = [analysis_results]
+            
+            face_objs = DeepFace.extract_faces(
+                img_path=frame_rgb,
+                detector_backend=self.detector_backend,
+                enforce_detection=False,
+                align=True
+            )
+            
+            
+            for i, (face_obj, analysis) in enumerate(zip(face_objs, analysis_results)):
+                if face_obj['confidence'] == 0:
+                    continue
+                    
+                facial_area = face_obj['facial_area']
                 
-                # Find best match
-                best_match_index = np.argmin(face_distances)
-                best_distance = face_distances[best_match_index]
+                top = facial_area['y']
+                left = facial_area['x']
+                bottom = facial_area['y'] + facial_area['h']
+                right = facial_area['x'] + facial_area['w']
+                face_location = (top, right, bottom, left)
+            
                 
+                emotion_probs = analysis.get('emotion', {})
+                emotion_data = None
                 
-                # Check if match is within tolerance
-                if best_distance <= self.tolerance:
-                    name = self.known_face_names[best_match_index]
-                    # Convert distance to confidence score (0-1)
-                    confidence = distance_to_confidence(best_distance, self.tolerance)
-
+                if emotion_probs:
+                    dominant_emotion = max(emotion_probs.items(), key=lambda x: x[1])
+                    emotion_data = {
+                        'emotion': dominant_emotion[0],
+                        'confidence': dominant_emotion[1] / 100.0
+                    }
             
-            if name == "Unknown":
-                # Check if this unknown face has been seen before
-                found_existing_unknown = False
-                for unknown_name, encodings in self.unknown_face_encodings.items():
-                    if encodings:
-                        distances = face_recognition.face_distance(encodings, face_encoding)
-                        if np.min(distances) <= self.tolerance:
-                            name = unknown_name
-                            found_existing_unknown = True
-                            confidence = distance_to_confidence(np.min(distances), self.tolerance)
-                            break
                 
-                if not found_existing_unknown:
-                    name = f"Unknown_{self.unknown_face_counter:03d}"
-                    self.unknown_face_counter += 1
-                    confidence = 0.0 
-                self.unknown_face_encodings[name].append(face_encoding)
-
-            recognized_faces.append({
-                "name": name,
-                "confidence": confidence,
-                "encoding": face_encoding.tolist(),
-                "location": face_location
-            })
+                name = "Unknown"
+                confidence = 0.0
+                
+                try:
+                    os.makedirs(self.known_faces_folder, exist_ok=True)
+                    face_analysis_results = DeepFace.find(
+                        img_path=frame_rgb,
+                        enforce_detection=False,
+                        detector_backend=self.detector_backend,
+                        silent=True,
+                        db_path=self.known_faces_folder,
+                    )
+                    
+                    if len(face_analysis_results) > 0 and len(face_analysis_results[0]) > 0:
+                        best_match_path = face_analysis_results[0].iloc[0]
+                        name = os.path.basename(os.path.dirname(best_match_path["identity"]))
+                        confidence = best_match_path["confidence"]
+                except Exception as e:
+                    pass
+
+                face_encoding = None
+                try:
+                    face_img = face_obj['face']
+                    embedding_objs = DeepFace.represent(
+                        img_path=face_img,
+                        model_name=self.model,
+                        enforce_detection=False
+                    )
+                    
+                    if not embedding_objs:
+                        continue
+                        
+                    face_encoding = np.array(embedding_objs[0]['embedding'])
+                    
+                except Exception as e:
+                    print(f"Error getting embedding: {e}")
+                    continue
+                
+                if name == "Unknown":
+                    found_existing_unknown = False
+                    for unknown_name, encodings in self.unknown_face_encodings.items():
+                        if encodings:
+                            distances = [self._cosine_distance(enc, face_encoding) for enc in encodings]
+                            if np.min(distances) <= self.tolerance:
+                                name = unknown_name
+                                found_existing_unknown = True
+                                confidence = self._distance_to_confidence(np.min(distances), self.tolerance)
+                                break
+                    
+                    if not found_existing_unknown:
+                        name = f"Unknown_{self.unknown_face_counter:03d}"
+                        self.unknown_face_counter += 1
+                        confidence = 0.0
+                    
+                    self.unknown_face_encodings[name].append(face_encoding)
+                
+                recognized_faces.append({
+                    "name": name,
+                    "confidence": confidence,
+                    "encoding": face_encoding.tolist(),
+                    "location": face_location,
+                    "emotion_label": emotion_data['emotion'] if emotion_data else None,
+                    "emotion_confidence": emotion_data['confidence'] if emotion_data else None
+                })
+        
+        except Exception as e:
+            print(f"Error in face recognition: {e}")
         
         return recognized_faces
 
-    def add_known_face(self, name: str, encoding: np.ndarray) -> None:
-        """Add a known face encoding."""
-        self.known_face_encodings.append(np.array(encoding))
-        self.known_face_names.append(name)
-
-    def save_known_faces(self) -> None:
-        """Save known faces to JSON file."""
-        known_faces_data: Dict[str, List[List[float]]] = {}
-        for name, encoding in zip(self.known_face_names, self.known_face_encodings):
-            if name not in known_faces_data:
-                known_faces_data[name] = []
-            known_faces_data[name].append(encoding.tolist())
+    @staticmethod
+    def _cosine_distance(encoding1: np.ndarray, encoding2: np.ndarray) -> float:
+        """Calculate cosine distance between two embeddings."""
+        dot_product = np.dot(encoding1, encoding2)
+        norm1 = np.linalg.norm(encoding1)
+        norm2 = np.linalg.norm(encoding2)
         
-        with open(self.known_faces_file, 'w') as f:
-            json.dump(known_faces_data, f, indent=2)
-
-    def get_all_faces(self) -> List[Dict[str, str]]:
-        """
-        Returns a list of all known and unknown faces with their representative encodings and counts.
-        """
-        all_faces: Dict[str, List[np.ndarray]] = defaultdict(list)
-        for name, encoding in zip(self.known_face_names, self.known_face_encodings):
-            all_faces[name].append(encoding)
+        if norm1 == 0 or norm2 == 0:
+            return 1.0
         
-        for name, encodings in self.unknown_face_encodings.items():
-            all_faces[name].extend(encodings)
+        cosine_similarity = dot_product / (norm1 * norm2)
+        return 1 - cosine_similarity
 
-        result = []
-        for name, encodings in all_faces.items():
-            # For simplicity, use the first encoding as representative and count all occurrences
-            if encodings:
-                result.append({
-                    "name": name,
-                    "encoding": encodings[0].tolist(),
-                    "count": len(encodings)
-                })
-        return result
+    def _distance_to_confidence(self, face_distance: float, tolerance: float) -> float:
+        """Convert face distance to confidence (0-1 scale)."""
+        if face_distance > tolerance:
+            return 0.0
+        confidence = 1 / (1 + np.exp(15 * (face_distance - tolerance)))
+        return confidence
 
-    def label_face(self, old_name: str, new_name: str) -> None:
-        """
-        Labels an existing face (known or unknown) with a new name.
-        If old_name was unknown, it becomes known.
-        """
-        # Move encodings from unknown to known if applicable
-        if old_name.startswith("Unknown_") and old_name in self.unknown_face_encodings:
-            for encoding in self.unknown_face_encodings[old_name]:
-                self.add_known_face(new_name, encoding)
-            del self.unknown_face_encodings[old_name]
-        else:
-            # Update existing known face names
-            for i, name in enumerate(self.known_face_names):
-                if name == old_name:
-                    self.known_face_names[i] = new_name
-        self.save_known_faces()
 
     def merge_faces(self, names_to_merge: List[str], new_name: str) -> None:
-        """
-        Merges multiple faces (known or unknown) under a single new name.
-        """
+        """Merges multiple faces under a single new name."""
         merged_encodings: List[np.ndarray] = []
         for name in names_to_merge:
             if name.startswith("Unknown_") and name in self.unknown_face_encodings:
@@ -247,20 +185,16 @@ def merge_faces(self, names_to_merge: List[str], new_name: str) -> None:
                     merged_encodings.append(encoding)
                 del self.unknown_face_encodings[name]
             else:
-                # Collect encodings of known faces to merge
                 for i, known_name in enumerate(self.known_face_names):
                     if known_name == name:
                         merged_encodings.append(self.known_face_encodings[i])
-                        # Remove old entry to avoid duplicates
-                        self.known_face_encodings[i] = None # Mark for deletion
-                        self.known_face_names[i] = None # Mark for deletion
+                        self.known_face_encodings[i] = None
+                        self.known_face_names[i] = None
         
-        # Clean up marked for deletion
         self.known_face_encodings = [e for e in self.known_face_encodings if e is not None]
         self.known_face_names = [n for n in self.known_face_names if n is not None]
 
-        # Add merged encodings under the new name
         for encoding in merged_encodings:
             self.add_known_face(new_name, encoding)
         
-        self.save_known_faces()
+        self.save_known_faces()
\ No newline at end of file
diff --git a/python/plugins/dominant_color.py b/python/plugins/dominant_color.py
index 380b475b..15b069ae 100644
--- a/python/plugins/dominant_color.py
+++ b/python/plugins/dominant_color.py
@@ -30,7 +30,6 @@ def to_json_dict(self) -> Dict[str, Union[str, float, bool]]:
 class SceneColorAnalysis:
     """Scene-level color analysis"""
     dominant_color: Optional[Dict[str, Union[str, float, bool]]]
-    color_palette: List[Dict[str, Union[str, float, bool]]]
     overall_brightness: float
     overall_saturation: float
     overall_warmth: float
@@ -168,7 +167,7 @@ def _extract_dominant_colors(self, frame: np.ndarray, num_colors: int) -> List[C
                 random_state=42,
                 n_init=5,
                 max_iter=100,
-                tol=0.01
+                tol=0.01,
             )
             kmeans.fit(pixels)
             
@@ -322,7 +321,6 @@ def get_results(self) -> Optional[SceneColorAnalysis]:
         
         return SceneColorAnalysis(
             dominant_color=dominant_color_json,
-            color_palette=json_color_palette,
             overall_brightness=round(overall_brightness, 2),
             overall_saturation=round(overall_saturation, 2),
             overall_warmth=round(overall_warmth, 2),
diff --git a/python/plugins/emotion_detection.py b/python/plugins/emotion_detection.py
deleted file mode 100644
index 2a0d10a9..00000000
--- a/python/plugins/emotion_detection.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import warnings
-from typing import Dict, Optional, Union, List
-import numpy as np
-import cv2
-
-from plugins.base import AnalyzerPlugin, FrameAnalysis, PluginResult
-
-try:
-    from fer import FER
-    FER_AVAILABLE = True
-except ImportError:
-    FER_AVAILABLE = False
-    print("Warning: FER (Facial Emotion Recognition) package not available")
-
-
-class EmotionDetectionPlugin(AnalyzerPlugin):
-    """A plugin for detecting emotions in faces."""
-
-    def __init__(self, config: Dict[str, Union[str, bool, int, float]]):
-        super().__init__(config)
-        self.emotion_detector: Optional[FER] = None
-        self.emotion_scale = float(config.get('emotion_scale', 0.5))
-        self.use_mtcnn = bool(config.get('use_mtcnn', False))
-        self.min_face_size = int(config.get('min_face_size', 30))
-        self.iou_threshold = float(config.get('emotion_iou_threshold', 0.3))
-        self.enabled = FER_AVAILABLE
-
-    def setup(self) -> None:
-        """Initialize the FER emotion detector."""
-        if not FER_AVAILABLE:
-            print("  ✗ Emotion Detection: FER package not available, skipping")
-            self.enabled = False
-            return
-        
-        try:
-            self.emotion_detector = FER(mtcnn=self.use_mtcnn)
-            print(f"  ✓ Emotion Detection: FER initialized (MTCNN: {self.use_mtcnn}, Scale: {self.emotion_scale})")
-        except Exception as e:
-            print(f"  ✗ Emotion Detection: Failed to initialize FER: {e}")
-            self.enabled = False
-
-    def analyze_frame(self, frame: np.ndarray, frame_analysis: FrameAnalysis, video_path: str) -> FrameAnalysis:
-        """Analyze a single frame for emotions."""
-        if not self.enabled or self.emotion_detector is None:
-            return frame_analysis
-            
-        if 'faces' in frame_analysis and frame_analysis['faces']:
-            self._add_emotions(frame, frame_analysis)
-        return frame_analysis
-
-    def _add_emotions(self, frame: np.ndarray, frame_analysis: FrameAnalysis) -> None:
-        """Add emotion data to recognized faces."""
-        if not self.enabled or self.emotion_detector is None:
-            return
-            
-        faces = frame_analysis.get('faces', [])
-        if not faces or not isinstance(faces, list):
-            return
-
-        valid_faces: List[Dict[str, Union[str, List[int], Optional[Dict[str, float]], float, List[float], Dict[str, float], Dict[str, int]]]] = []
-        for face in faces:
-            if not isinstance(face, dict) or 'location' not in face:
-                if isinstance(face, dict):
-                    face['emotion'] = None
-                continue
-            
-            location = face['location']
-            if not isinstance(location, list) or len(location) != 4:
-                face['emotion'] = None
-                continue
-                
-            ft, fr, fb, fl = location
-            face_width = fr - fl
-            face_height = fb - ft
-            
-            if face_width < self.min_face_size or face_height < self.min_face_size:
-                face['emotion'] = None
-                continue
-            
-            valid_faces.append(face)
-
-
-        if not valid_faces:
-            return
-
-        # Resize frame for faster emotion detection
-        original_height, original_width = frame.shape[:2]
-        
-        if self.emotion_scale != 1.0:
-            small_frame = cv2.resize(
-                frame,
-                (0, 0),
-                fx=self.emotion_scale,
-                fy=self.emotion_scale,
-                interpolation=cv2.INTER_LINEAR
-            )
-        else:
-            small_frame = frame
-
-        # Detect emotions on smaller frame
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            try:
-                emotions_results = self.emotion_detector.detect_emotions(small_frame)
-            except Exception as e:
-                # Silently handle errors and continue
-                for face in faces:
-                    face['emotion'] = None
-                return
-
-        if not emotions_results:
-            for face in faces:
-                face['emotion'] = None
-            return
-
-        # Match emotions to faces using IoU
-        scale_inverse = 1.0 / self.emotion_scale
-        
-        for face in valid_faces:
-            ft, fr, fb, fl = face['location']
-            face_area = (fr - fl) * (fb - ft)
-
-            best_match_emotion = None
-            max_iou = 0.0
-
-            for emotion_res in emotions_results:
-                # Scale emotion box back to original frame coordinates
-                el, et, ew, eh = emotion_res['box']
-                
-                if self.emotion_scale != 1.0:
-                    el = int(el * scale_inverse)
-                    et = int(et * scale_inverse)
-                    ew = int(ew * scale_inverse)
-                    eh = int(eh * scale_inverse)
-                
-                er = el + ew
-                eb = et + eh
-                emotion_area = ew * eh
-
-                # Calculate IoU
-                x_overlap = max(0, min(fr, er) - max(fl, el))
-                y_overlap = max(0, min(fb, eb) - max(ft, et))
-                intersection_area = x_overlap * y_overlap
-
-                union_area = face_area + emotion_area - intersection_area
-                iou = intersection_area / union_area if union_area > 0 else 0
-
-                if iou > max_iou:
-                    max_iou = iou
-                    best_match_emotion = emotion_res['emotions']
-
-            # Assign emotion if IoU is above threshold
-            if max_iou > self.iou_threshold:
-                face['emotion'] = best_match_emotion
-            else:
-                face['emotion'] = None
-
-    def get_results(self) -> PluginResult:
-        return None
-
-    def get_summary(self) -> PluginResult:
-        return None
\ No newline at end of file
diff --git a/python/plugins/face_recognition.py b/python/plugins/face_recognition.py
index 003c592e..fddf097b 100644
--- a/python/plugins/face_recognition.py
+++ b/python/plugins/face_recognition.py
@@ -27,101 +27,27 @@ def __init__(self, config: Dict[str, Union[str, bool, int, float]]):
         self.face_recognizer: Optional[FaceRecognizer] = None
         self.all_faces: List[Dict[str, Union[float, str, int]]] = []
         self.unknown_faces_output_path: Optional[Path] = None
-        self.known_faces_file = os.getenv("KNOWN_FACES_FILE_LOADED", ".known_faces.json")
-        
-        self.detection_model = str(config.get('detection_model', 'hog'))
+        self.known_faces_folder = os.getenv("FACES_DIR") or '.faces'
+        self.detection_model = str(config.get('detection_model', 'VGG-Face'))
         self.face_scale = float(config.get('face_scale', 0.5))
         self.save_unknown_faces = bool(config.get('save_unknown_faces', True))
-        self.unknown_save_interval = int(config.get('unknown_save_interval', 5))
+        
+        self.saved_unknown_faces: Dict[str, Dict[str, Union[str, List]]] = {}
 
     def setup(self) -> None:
-        """Initialize face recognizer and load known faces."""
-        logger.info("=" * 70)
-        logger.info("FACE RECOGNITION PLUGIN SETUP")
-        logger.info("=" * 70)
-        
-        logger.info(f"Detection model: {self.detection_model} (HOG=fast, CNN=accurate)")
-        logger.info(f"Processing scale: {self.face_scale*100}%")
-        logger.info(f"Loading known faces from: {self.known_faces_file}")
-        
-        if not os.path.exists(self.known_faces_file):
-            with open(self.known_faces_file, 'w', encoding='utf-8') as f:
-                json.dump([], f)
-        else:
-            self._log_file_metadata()
-        
+        """Initialize face recognizer and load known faces."""        
         self.face_recognizer = FaceRecognizer(
-            known_faces_file=self.known_faces_file,
+            known_faces_folder=self.known_faces_folder,
             model=self.detection_model
         )
-        
-        self._log_loaded_faces()
+        if self.known_faces_folder:
+            os.makedirs(self.known_faces_folder, exist_ok=True)
         
         if self.save_unknown_faces:
             path_str = os.getenv("UNKNOWN_FACES_DIR", str(self.config.get('unknown_faces_dir', 'unknown_faces')))
             self.unknown_faces_output_path = Path(path_str)
             self.unknown_faces_output_path.mkdir(parents=True, exist_ok=True)
-            logger.info(f"Unknown faces directory: {self.unknown_faces_output_path}")
-            logger.info(f"Saving every {self.unknown_save_interval}th unknown face detection")
-        
-        logger.info("=" * 70 + "\n")
-
-    def _log_file_metadata(self) -> None:
-        """Log known faces file metadata."""
-        file_stat = os.stat(self.known_faces_file)
-        import time
-        modified_str = time.strftime(
-            '%Y-%m-%d %H:%M:%S', 
-            time.localtime(file_stat.st_mtime)
-        )
-        
-        logger.info(f"File size: {file_stat.st_size} bytes")
-        logger.info(f"Last modified: {modified_str}")
-        
-        try:
-            with open(self.known_faces_file, 'r') as f:
-                known_faces_data = json.load(f)
-                logger.info(f"JSON structure: {len(known_faces_data)} entries")
-        except Exception as e:
-            logger.error(f"Error reading known faces file: {e}")
-
-    def _log_loaded_faces(self) -> None:
-        """Log detailed information about loaded known faces."""
-        if not hasattr(self.face_recognizer, 'known_face_encodings') or \
-           not hasattr(self.face_recognizer, 'known_face_names'):
-            logger.warning("Face recognizer not properly initialized")
-            return
-        
-        known_encodings = self.face_recognizer.known_face_encodings
-        known_names = self.face_recognizer.known_face_names
-        
-        if not known_encodings or not known_names:
-            logger.warning("No known faces loaded!")
-            return
-        
-        face_counts: Dict[str, int] = {}
-        for name in known_names:
-            face_counts[name] = face_counts.get(name, 0) + 1
-        
-        total_encodings = len(known_encodings)
-        total_people = len(face_counts)
-        
-        logger.info(
-            f"✓ Loaded {total_encodings} encoding(s) for {total_people} person/people"
-        )
-        logger.info("")
-        logger.info("Loaded faces breakdown:")
-        logger.info("-" * 50)
-        
-        sorted_faces = sorted(face_counts.items(), key=lambda x: x[1], reverse=True)
-        
-        for name, count in sorted_faces:
-            plural = "image" if count == 1 else "images"
-            logger.info(f"  • {name}: {count} {plural}")
-        
-        logger.info("-" * 50)
-        logger.info(f"Total: {total_people} unique face(s), {total_encodings} encoding(s)")
-        logger.info("")
+            logger.info(f"Unknown faces directory: {self.unknown_faces_output_path}") 
 
     def analyze_frame(
         self, 
@@ -149,8 +75,8 @@ def analyze_frame(
         
         frame_scale_inverse = 1.0 / self.face_scale
         ui_scale = float(frame_analysis.get('scale_factor', 1.0))
+
         output_faces = []
-        
         for face in recognized_faces:
             top, right, bottom, left = face['location']
 
@@ -173,17 +99,11 @@ def analyze_frame(
             ui_y = abs_y * ui_scale
             ui_w = abs_w * ui_scale
             ui_h = abs_h * ui_scale
-                
+            
             output_face: Dict[str, Union[str, List[int], Optional[Dict[str, float]], float, List[float], Dict[str, float], Dict[str, int]]] = {
                 "name": face['name'],
                 "location": [top, right, bottom, left],
-                "emotion": face.get('emotion'),
                 "confidence": face.get("confidence"),
-                "encoding": (
-                    face['encoding'].tolist() 
-                    if isinstance(face['encoding'], np.ndarray) 
-                    else face['encoding']
-                ),
                 "bbox": {
                     "x": ui_x,
                     "y": ui_y,
@@ -193,6 +113,10 @@ def analyze_frame(
                 "frame_dimensions": {
                     "width": original_width,
                     "height": original_height
+                },
+                "emotion":{
+                    "label": face.get('emotion_label'),
+                    "confidence": face.get('emotion_confidence')
                 }
             }
             output_faces.append(output_face)
@@ -204,36 +128,34 @@ def analyze_frame(
             })
 
             if face['name'].startswith("Unknown_") and self.save_unknown_faces:
-                unknown_id = face['name'].split('_')[1] if '_' in face['name'] else '0'
-                unknown_num = int(unknown_id) if unknown_id.isdigit() else 0
+                face_original = face.copy()
+                face_original['location'] = [top, right, bottom, left]
                 
-                if unknown_num % self.unknown_save_interval == 0:
-                    face_original = face.copy()
-                    face_original['location'] = [top, right, bottom, left]
-                    
-                    self._save_unknown_face(
-                        frame,
-                        int(frame_analysis['start_time_ms']),
-                        int(frame_analysis.get('frame_idx', 0)),
-                        face_original,
-                        frame_analysis,
-                        video_path
-                    )
-
+                self._track_unknown_face(
+                    frame,
+                    int(frame_analysis['start_time_ms']),
+                    int(frame_analysis.get('frame_idx', 0)),
+                    face_original,
+                    video_path,
+                    original_width,
+                    original_height
+                )
         frame_analysis['faces'] = output_faces
         return frame_analysis
 
-    def _save_unknown_face(
+    def _track_unknown_face(
         self,
         frame: np.ndarray,
         timestamp_ms: int,
         frame_idx: int,
         face: Dict[str, Union[str, List[int], np.ndarray, List[float]]],
-        frame_analysis: FrameAnalysis,
-        video_path: str
+        video_path: str,
+        frame_width: int,
+        frame_height: int
     ) -> None:
-        """Crop and save unknown face with metadata."""
-        h, w = frame.shape[:2]
+        """Track unknown face appearances and save only the first occurrence."""
+        face_id = face['name']
+        
         location = face['location']
         if not isinstance(location, list) or len(location) != 4:
             return
@@ -242,8 +164,8 @@ def _save_unknown_face(
 
         left = max(0, int(left))
         top = max(0, int(top))
-        right = min(w, int(right))
-        bottom = min(h, int(bottom))
+        right = min(frame_width, int(right))
+        bottom = min(frame_height, int(bottom))
 
         original_bbox = {
             'top': top,
@@ -259,8 +181,8 @@ def _save_unknown_face(
         
         padded_left = max(0, left - padding_w)
         padded_top = max(0, top - padding_h)
-        padded_right = min(w, right + padding_w)
-        padded_bottom = min(h, bottom + padding_h)
+        padded_right = min(frame_width, right + padding_w)
+        padded_bottom = min(frame_height, bottom + padding_h)
 
         padded_bbox = {
             'top': padded_top,
@@ -271,12 +193,68 @@ def _save_unknown_face(
             'height': padded_bottom - padded_top
         }
 
+        appearance_data = {
+            "frame_index": frame_idx,
+            "timestamp_ms": timestamp_ms,
+            "timestamp_seconds": timestamp_ms / 1000,
+            "formatted_timestamp": self._format_timestamp(timestamp_ms),
+            "bounding_box": original_bbox,
+            "padded_bounding_box": padded_bbox
+        }
+
+        # Check if this unknown face has been saved before
+        if face_id not in self.saved_unknown_faces:
+            # First time seeing this face - save the image
+            self._save_unknown_face_first_occurrence(
+                frame,
+                timestamp_ms,
+                frame_idx,
+                face,
+                video_path,
+                frame_width,
+                frame_height,
+                appearance_data
+            )
+        else:
+            # Face already saved - just update the JSON with new appearance
+            self._update_unknown_face_appearances(face_id, appearance_data)
+   
+
+    def _save_unknown_face_first_occurrence(
+        self,
+        frame: np.ndarray,
+        timestamp_ms: int,
+        frame_idx: int,
+        face: Dict[str, Union[str, List[int], np.ndarray, List[float]]],
+        video_path: str,
+        frame_width: int,
+        frame_height: int,
+        appearance_data: Dict
+    ) -> None:
+        """Save the first occurrence of an unknown face."""
+        face_id = face['name']
+        location = face['location']
+        top, right, bottom, left = location
+
+        left = max(0, int(left))
+        top = max(0, int(top))
+        right = min(frame_width, int(right))
+        bottom = min(frame_height, int(bottom))
+
+        padding_w = int((right - left) * 0.1)
+        padding_h = int((bottom - top) * 0.1)
+        
+        padded_left = max(0, left - padding_w)
+        padded_top = max(0, top - padding_h)
+        padded_right = min(frame_width, right + padding_w)
+        padded_bottom = min(frame_height, bottom + padding_h)
+
         face_image = frame[padded_top:padded_bottom, padded_left:padded_right]
 
         if face_image.size == 0:
             return
 
-        base_filename = f"{face['name']}_{timestamp_ms}ms_frame{frame_idx}"
+        base_filename = f"{face_id}_first_{timestamp_ms}ms"
         image_filename = f"{base_filename}.jpg"
         json_filename = f"{base_filename}.json"
         
@@ -288,16 +266,37 @@ def _save_unknown_face(
         
         try:
             self.unknown_faces_output_path.mkdir(parents=True, exist_ok=True)
-            
+            h, w = frame.shape[:2]
             cv2.imwrite(
                 str(image_filepath), 
                 face_image,
                 [cv2.IMWRITE_JPEG_QUALITY, 85]
             )
+            original_bbox = {
+            'top': top,
+            'right': right,
+            'bottom': bottom,
+            'left': left,
+            'width': right - left,
+            'height': bottom - top
+            }
+            padding_w = int((right - left) * 0.1)
+            padding_h = int((bottom - top) * 0.1)
             
-            encoding = face['encoding']
-            encoding_list = encoding.tolist() if isinstance(encoding, np.ndarray) else encoding
-            
+            padded_left = max(0, left - padding_w)
+            padded_top = max(0, top - padding_h)
+            padded_right = min(w, right + padding_w)
+            padded_bottom = min(h, bottom + padding_h)
+
+            padded_bbox = {
+                'top': padded_top,
+                'right': padded_right,
+                'bottom': padded_bottom,
+                'left': padded_left,
+                'width': padded_right - padded_left,
+                'height': padded_bottom - padded_top
+            }
+
             metadata = {
                 "image_file": image_filename,
                 "json_file": json_filename,
@@ -313,7 +312,9 @@ def _save_unknown_face(
                 "face_id": face['name'],
                 "bounding_box": original_bbox,
                 "padded_bounding_box": padded_bbox,
-                "face_encoding": encoding_list,
+                "first_appearance": appearance_data,
+                "all_appearances": [appearance_data],
+                "total_appearances": 1,
                 "label": {
                     "name": None,
                     "labeled_by": None,
@@ -326,11 +327,45 @@ def _save_unknown_face(
             with open(json_filepath, 'w', encoding='utf-8') as json_file:
                 json.dump(metadata, json_file, indent=2, ensure_ascii=False)
             
-            logger.debug(f"Saved unknown face: {image_filename}")
+            self.saved_unknown_faces[face_id] = {
+                "json_path": str(json_filepath),
+                "image_path": str(image_filepath),
+                "appearances": [appearance_data]
+            }
+            
             
         except Exception as e:
             logger.error(f"Error saving unknown face {image_filepath}: {e}")
+
+    def _update_unknown_face_appearances(
+        self,
+        face_id: str,
+        appearance_data: Dict
+    ) -> None:
+        """Update the JSON file with a new appearance of an already-saved unknown face."""
+        if face_id not in self.saved_unknown_faces:
+            return
+        
+        json_path = self.saved_unknown_faces[face_id]["json_path"]
+        
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                metadata = json.load(f)
             
+            metadata["all_appearances"].append(appearance_data)
+            metadata["total_appearances"] = len(metadata["all_appearances"])
+            metadata["last_updated"] = datetime.now().isoformat()
+            metadata["last_appearance"] = appearance_data
+            
+            with open(json_path, 'w', encoding='utf-8') as f:
+                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            
+            self.saved_unknown_faces[face_id]["appearances"].append(appearance_data)
+            
+            
+        except Exception as e:
+            logger.error(f"Error updating unknown face {face_id}: {e}")
+
     @staticmethod
     def _format_timestamp(timestamp_ms: int) -> str:
         """Format timestamp in HH:MM:SS.mmm format."""
@@ -376,35 +411,24 @@ def get_summary(self) -> PluginResult:
                     'last_seen': max(timestamps) if timestamps else 0.0
                 }
 
-        logger.info("\n" + "=" * 70)
-        logger.info("FACE RECOGNITION SUMMARY")
-        logger.info("=" * 70)
-        logger.info(f"Total faces detected: {len(self.all_faces)}")
         logger.info(f"Known people identified: {len(known_people)}")
+        logger.info(f"Unique unknown faces: {unique_unknown}")
         
-        if known_people:
-            logger.info("")
-            logger.info("Known people appearances:")
-            for person in sorted(known_people):
-                info = known_appearances[person]
-                logger.info(f"  • {person}:")
-                logger.info(f"      Appearances: {info['count']}")
-                logger.info(f"      First seen: {info['first_seen']:.1f}s")
-                logger.info(f"      Last seen: {info['last_seen']:.1f}s")
-        
-        logger.info("")
-        logger.info(f"Unknown faces: {unknown_count} ({unique_unknown} unique)")
-        
-        if unknown_count > 0 and self.save_unknown_faces:
-            logger.info(f"Unknown faces saved to: {self.unknown_faces_output_path}")
-        
-        logger.info("=" * 70 + "\n")
 
         return {
             "known_people_identified": known_people,
             "known_appearances": known_appearances,
             "unknown_faces_detected": unknown_count,
             "unique_unknown_faces": unique_unknown,
+            "unknown_faces_saved": len(self.saved_unknown_faces),
             "total_faces_detected": len(self.all_faces),
-            "unknown_faces_directory": str(self.unknown_faces_output_path) if self.save_unknown_faces else None
+            "unknown_faces_directory": str(self.unknown_faces_output_path) if self.save_unknown_faces else None,
+            "unknown_faces_details": {
+                face_id: {
+                    "image_path": data["image_path"],
+                    "json_path": data["json_path"],
+                    "total_appearances": len(data["appearances"])
+                }
+                for face_id, data in self.saved_unknown_faces.items()
+            }
         }
\ No newline at end of file
diff --git a/python/requirements-dev.txt b/python/requirements-dev.txt
new file mode 100644
index 00000000..2177852b
--- /dev/null
+++ b/python/requirements-dev.txt
@@ -0,0 +1,3 @@
+-r requirements.txt
+pytest
+coverage>=7.11.0
diff --git a/python/requirements.txt b/python/requirements.txt
index 9062e353..04846eff 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -1,11 +1,7 @@
 # Core dependencies
-opencv-python>=4.8.0
 numpy>=1.24.0,<2.0.0
 tqdm>=4.66.0
 
-# Emotion detection (optional - can be heavy)
-fer==22.5.1
-tensorflow>=2.13.0
 
 # Audio transcription
 faster-whisper>=1.2.1
@@ -20,24 +16,26 @@ Pillow>=9.3.0
 # Monitoring
 psutil>=5.9.0
 
-# Video processing with audio support
-ffmpeg-python>=0.2.0
-moviepy>=1.0.3
-
-# Websockets 
+# Websockets
 websockets>=12.0
 python-multipart>=0.0.6
 
-# Testing
-pytest>=7.4.0
-coverage>=7.11.0
-
+# Environment
 python-dotenv>=1.2.1
 
 # ML/AI frameworks (CPU ONLY)
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch>=2.6.0
 torchvision>=0.16.0
+
+# If x86 (AMD64), use the tiny CPU-only version
+tensorflow-cpu==2.15.0; platform_machine == "x86_64"
+tensorflow==2.15.0; platform_machine == "aarch64"
+
+# Face recognition (PyTorch backend)
+deepface>=0.0.96
+tf-keras
+
 ultralytics>=8.0.0
 transformers>=4.35.0
 --index-url https://pypi.org/simple
\ No newline at end of file
diff --git a/python/tests/test_analyze.py b/python/tests/test_analyze.py
new file mode 100644
index 00000000..96379bb0
--- /dev/null
+++ b/python/tests/test_analyze.py
@@ -0,0 +1,103 @@
+import unittest
+import sys
+import os
+import json
+import tempfile
+import shutil
+from unittest.mock import patch
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from analyze import VideoAnalyzer, AnalysisConfig
+
+class TestVideoAnalysis(unittest.TestCase):
+
+    def setUp(self):
+        self.video_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'test_video.mp4'))
+        
+        if not os.path.exists(self.video_path):
+            with open(self.video_path, 'w') as f:
+                f.write("dummy video content")
+            self.dummy_video_created = True
+        else:
+            self.dummy_video_created = False
+
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.output_json_path = os.path.join(self.tmp_dir.name, 'analysis_output.json')
+        self.unknown_faces_dir = os.path.join(os.path.dirname(__file__), 'unknown_faces')
+
+        self.config = AnalysisConfig(
+            sample_interval_seconds=1.0,
+            enable_performance_report=False,
+            lazy_plugin_init=False,
+            unknown_faces_dir=self.unknown_faces_dir
+        )
+        
+        reference_json_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'test_video_analysis.json'))
+        with open(reference_json_path, 'r') as f:
+            self.reference_analysis = json.load(f)
+
+    def tearDown(self):
+        if self.dummy_video_created and os.path.exists(self.video_path):
+            os.remove(self.video_path)
+        self.tmp_dir.cleanup()
+        if os.path.exists(self.unknown_faces_dir):
+            shutil.rmtree(self.unknown_faces_dir)
+
+    @patch('analyze.load_plugins')
+    def test_full_analysis_pipeline(self, mock_load_plugins):
+        """Test full analysis pipeline with mocked plugins"""
+        
+        mock_plugins = []
+        
+        class MockPlugin:
+            def __init__(self, config):
+                self.config = config
+            
+            def setup(self):
+                pass
+            
+            def analyze_frame(self, frame, frame_analysis, video_path):
+                return frame_analysis
+            
+            def analyze_scene(self, frame_analyses):
+                pass
+            
+            def get_results(self):
+                return {}
+            
+            def analyze_activities(self, frame_analyses, scene_analysis):
+                return []
+            
+            def get_summary(self):
+                return {}
+        
+        mock_load_plugins.return_value = [MockPlugin({})]
+        
+        analyzer = VideoAnalyzer(self.video_path, self.config)
+        result = analyzer.analyze()
+        
+        result_dict = result.to_dict()
+        
+        with open(self.output_json_path, "w", encoding="utf-8") as f:
+            json.dump(result_dict, f, indent=4, ensure_ascii=False)
+
+        self.assertIsNone(result.error)
+        
+        self.assertIn('scene_analysis', result_dict)
+        self.assertIn('frame_analysis', result_dict)
+        self.assertIn('detected_activities', result_dict)
+        self.assertIn('face_recognition_summary', result_dict)
+
+    def test_file_not_found_error(self):
+        non_existent_path = '/path/to/nonexistent/video.mp4'
+        analyzer = VideoAnalyzer(non_existent_path, self.config)
+        result = analyzer.analyze()
+
+        self.assertIsNotNone(result.error)
+        self.assertIn("Video file not found", result.error)
+        self.assertEqual(result.video_file, non_existent_path)
+        self.assertEqual(len(result.frame_analysis), 0)
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/python/tests/test_transcribe.py b/python/tests/test_transcribe.py
index deda4a57..71a399a5 100644
--- a/python/tests/test_transcribe.py
+++ b/python/tests/test_transcribe.py
@@ -10,7 +10,7 @@
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-from transcribe import run_transcription
+from transcribe import run_transcription, TranscriptionService
 
 
 class MockWhisperSegment:
@@ -86,11 +86,13 @@ def tearDown(self):
         if os.path.exists(self.output_json_path):
             os.remove(self.output_json_path)
 
+    @patch('transcribe.TranscriptionService.is_model_cached')
     @patch('transcribe.WhisperModel')
     @patch('transcribe.Path.exists')
-    def test_transcribe_video(self, mock_path_exists, mock_whisper_model):
+    def test_transcribe_video(self, mock_path_exists, mock_whisper_model, mock_is_cached):
         """Test transcription with mocked Whisper model"""
         mock_path_exists.return_value = True
+        mock_is_cached.return_value = True
         
         # Create mock model instance
         mock_model_instance = MagicMock()
@@ -165,11 +167,13 @@ def test_transcribe_video(self, mock_path_exists, mock_whisper_model):
                 self.assertIn('word', word, 
                             f"Segment {i}, word {j} missing word text")
 
+    @patch('transcribe.TranscriptionService.is_model_cached')
     @patch('transcribe.WhisperModel')
     @patch('transcribe.Path.exists')
-    def test_transcribe_empty_audio(self, mock_path_exists, mock_whisper_model):
+    def test_transcribe_empty_audio(self, mock_path_exists, mock_whisper_model, mock_is_cached):
         """Test transcription with video that has no audio"""
         mock_path_exists.return_value = True
+        mock_is_cached.return_value = True  # Pretend model is already cached
         
         # Mock model to raise RuntimeError for no audio
         mock_model_instance = MagicMock()
@@ -184,11 +188,13 @@ def test_transcribe_empty_audio(self, mock_path_exists, mock_whisper_model):
         self.assertEqual(len(result['segments']), 0, "Expected no segments for no audio")
         self.assertEqual(result['language'], 'N/A', "Expected N/A language for no audio")
 
+    @patch('transcribe.TranscriptionService.is_model_cached')
     @patch('transcribe.WhisperModel')
     @patch('transcribe.Path.exists')
-    def test_transcribe_with_progress_callback(self, mock_path_exists, mock_whisper_model):
+    def test_transcribe_with_progress_callback(self, mock_path_exists, mock_whisper_model, mock_is_cached):
         """Test transcription with progress callback"""
         mock_path_exists.return_value = True
+        mock_is_cached.return_value = True  # Pretend model is already cached
         
         mock_model_instance = MagicMock()
         mock_model_instance.transcribe.return_value = (
diff --git a/python/tests/test_video.mp4 b/python/tests/test_video.mp4
index d62b6d50..587079e0 100644
Binary files a/python/tests/test_video.mp4 and b/python/tests/test_video.mp4 differ
diff --git a/python/tests/test_video_analysis.json b/python/tests/test_video_analysis.json
new file mode 100644
index 00000000..9d1e0c17
--- /dev/null
+++ b/python/tests/test_video_analysis.json
@@ -0,0 +1,3660 @@
+{
+    "scene_analysis": {
+        "dominant_color": {
+            "name": "Black",
+            "hex": "#000000",
+            "percentage": 50.4,
+            "is_vibrant": false,
+            "is_muted": true
+        },
+        "overall_brightness": 29.27,
+        "overall_saturation": 21.67,
+        "overall_warmth": 3.7,
+        "color_mood": "dark",
+        "color_harmony": "analogous",
+        "environment": "snow",
+        "environment_confidence": 0.5,
+        "object_distribution": {
+            "person": 52,
+            "car": 4,
+            "laptop": 12,
+            "potted plant": 11,
+            "tv": 11,
+            "vase": 11,
+            "keyboard": 8,
+            "cell phone": 4,
+            "mouse": 7,
+            "apple": 4,
+            "orange": 4,
+            "refrigerator": 4,
+            "oven": 4
+        },
+        "total_frames": 60
+    },
+    "detected_activities": [],
+    "face_recognition_summary": {
+        "known_people_identified": [
+            "Aiony Haust",
+            "Ilias"
+        ],
+        "known_appearances": {
+            "Aiony Haust": {
+                "count": 4,
+                "first_seen": 0.0,
+                "last_seen": 3.0
+            },
+            "Ilias": {
+                "count": 4,
+                "first_seen": 20.0,
+                "last_seen": 23.0
+            }
+        },
+        "unknown_faces_detected": 20,
+        "unique_unknown_faces": 5,
+        "unknown_faces_saved": 5,
+        "total_faces_detected": 28,
+        "unknown_faces_directory": "unknown_faces",
+        "unknown_faces_details": {
+            "Unknown_000": {
+                "image_path": "unknown_faces/Unknown_000_first_4000ms.jpg",
+                "json_path": "unknown_faces/Unknown_000_first_4000ms.json",
+                "total_appearances": 4
+            },
+            "Unknown_001": {
+                "image_path": "unknown_faces/Unknown_001_first_8000ms.jpg",
+                "json_path": "unknown_faces/Unknown_001_first_8000ms.json",
+                "total_appearances": 4
+            },
+            "Unknown_002": {
+                "image_path": "unknown_faces/Unknown_002_first_12000ms.jpg",
+                "json_path": "unknown_faces/Unknown_002_first_12000ms.json",
+                "total_appearances": 4
+            },
+            "Unknown_003": {
+                "image_path": "unknown_faces/Unknown_003_first_37000ms.jpg",
+                "json_path": "unknown_faces/Unknown_003_first_37000ms.json",
+                "total_appearances": 4
+            },
+            "Unknown_004": {
+                "image_path": "unknown_faces/Unknown_004_first_37000ms.jpg",
+                "json_path": "unknown_faces/Unknown_004_first_37000ms.json",
+                "total_appearances": 4
+            }
+        }
+    },
+    "frame_analysis": [
+        {
+            "start_time_ms": 0,
+            "end_time_ms": 1000,
+            "duration_ms": 1000,
+            "frame_idx": 0,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9628351926803589,
+                    "bbox": {
+                        "x": 352.805908203125,
+                        "y": 105.13653564453125,
+                        "width": 574.772705078125,
+                        "height": 607.6312866210938
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Aiony Haust",
+                    "location": [
+                        168,
+                        832,
+                        456,
+                        544
+                    ],
+                    "confidence": 100.0,
+                    "bbox": {
+                        "x": 544.0,
+                        "y": 168.0,
+                        "width": 288.0,
+                        "height": 288.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "neutral",
+                        "confidence": 0.5970541834831238
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "environment_caption": "a woman with a black shirt and a pink background"
+        },
+        {
+            "start_time_ms": 1000,
+            "end_time_ms": 2000,
+            "duration_ms": 1000,
+            "frame_idx": 30,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9628399014472961,
+                    "bbox": {
+                        "x": 352.805419921875,
+                        "y": 105.13812255859375,
+                        "width": 574.77197265625,
+                        "height": 607.6322631835938
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Aiony Haust",
+                    "location": [
+                        168,
+                        830,
+                        456,
+                        542
+                    ],
+                    "confidence": 100.0,
+                    "bbox": {
+                        "x": 542.0,
+                        "y": 168.0,
+                        "width": 288.0,
+                        "height": 288.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "neutral",
+                        "confidence": 0.6164354681968689
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "dominant_color": {
+                "name": "Black",
+                "hex": "#000000",
+                "percentage": 50.4,
+                "is_vibrant": false,
+                "is_muted": true
+            },
+            "color_palette": [
+                {
+                    "name": "Black",
+                    "hex": "#000000",
+                    "percentage": 50.4,
+                    "is_vibrant": false,
+                    "is_muted": true
+                },
+                {
+                    "name": "Medium Purple",
+                    "hex": "#8481be",
+                    "percentage": 29.8,
+                    "is_vibrant": false,
+                    "is_muted": false
+                }
+            ],
+            "brightness": 17.48,
+            "saturation": 19.12,
+            "color_temperature": "neutral",
+            "environment_caption": "a woman with a black shirt and a pink background"
+        },
+        {
+            "start_time_ms": 2000,
+            "end_time_ms": 3000,
+            "duration_ms": 1000,
+            "frame_idx": 60,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9628399014472961,
+                    "bbox": {
+                        "x": 352.805419921875,
+                        "y": 105.13812255859375,
+                        "width": 574.77197265625,
+                        "height": 607.6322631835938
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Aiony Haust",
+                    "location": [
+                        168,
+                        830,
+                        456,
+                        542
+                    ],
+                    "confidence": 100.0,
+                    "bbox": {
+                        "x": 542.0,
+                        "y": 168.0,
+                        "width": 288.0,
+                        "height": 288.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "neutral",
+                        "confidence": 0.6164354681968689
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "environment_caption": "a woman with a black shirt and a pink background",
+            "detected_text": []
+        },
+        {
+            "start_time_ms": 3000,
+            "end_time_ms": 4000,
+            "duration_ms": 1000,
+            "frame_idx": 90,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9628399014472961,
+                    "bbox": {
+                        "x": 352.805419921875,
+                        "y": 105.13812255859375,
+                        "width": 574.77197265625,
+                        "height": 607.6322631835938
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Aiony Haust",
+                    "location": [
+                        168,
+                        830,
+                        456,
+                        542
+                    ],
+                    "confidence": 100.0,
+                    "bbox": {
+                        "x": 542.0,
+                        "y": 168.0,
+                        "width": 288.0,
+                        "height": 288.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "neutral",
+                        "confidence": 0.6164354681968689
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "dominant_color": {
+                "name": "Black",
+                "hex": "#03050a",
+                "percentage": 66.8,
+                "is_vibrant": false,
+                "is_muted": false
+            },
+            "color_palette": [
+                {
+                    "name": "Black",
+                    "hex": "#03050a",
+                    "percentage": 66.8,
+                    "is_vibrant": false,
+                    "is_muted": false
+                },
+                {
+                    "name": "Gray",
+                    "hex": "#6f6da2",
+                    "percentage": 17.2,
+                    "is_vibrant": false,
+                    "is_muted": false
+                }
+            ],
+            "brightness": 17.48,
+            "saturation": 19.12,
+            "color_temperature": "neutral",
+            "environment_caption": "a woman with a black shirt and a pink background"
+        },
+        {
+            "start_time_ms": 4000,
+            "end_time_ms": 5000,
+            "duration_ms": 1000,
+            "frame_idx": 120,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9587202072143555,
+                    "bbox": {
+                        "x": 462.8580322265625,
+                        "y": 15.13702392578125,
+                        "width": 712.801513671875,
+                        "height": 699.4501342773438
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Unknown_000",
+                    "location": [
+                        108,
+                        984,
+                        416,
+                        676
+                    ],
+                    "confidence": 0.0,
+                    "bbox": {
+                        "x": 676.0,
+                        "y": 108.0,
+                        "width": 308.0,
+                        "height": 308.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "happy",
+                        "confidence": 0.9712995256542594
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "environment_caption": "a man with a beard and glasses on"
+        },
+        {
+            "start_time_ms": 5000,
+            "end_time_ms": 6000,
+            "duration_ms": 1000,
+            "frame_idx": 150,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9580745100975037,
+                    "bbox": {
+                        "x": 462.9479675292969,
+                        "y": 14.6707763671875,
+                        "width": 712.7727355957031,
+                        "height": 699.81494140625
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Unknown_000",
+                    "location": [
+                        108,
+                        984,
+                        418,
+                        674
+                    ],
+                    "confidence": 0.9974060524685332,
+                    "bbox": {
+                        "x": 674.0,
+                        "y": 108.0,
+                        "width": 310.0,
+                        "height": 310.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "happy",
+                        "confidence": 0.9703518152236938
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "dominant_color": {
+                "name": "Gainsboro",
+                "hex": "#d8dddf",
+                "percentage": 65.6,
+                "is_vibrant": false,
+                "is_muted": true
+            },
+            "color_palette": [
+                {
+                    "name": "Gainsboro",
+                    "hex": "#d8dddf",
+                    "percentage": 65.6,
+                    "is_vibrant": false,
+                    "is_muted": true
+                },
+                {
+                    "name": "Dark Gray",
+                    "hex": "#a29f9a",
+                    "percentage": 22.4,
+                    "is_vibrant": false,
+                    "is_muted": true
+                }
+            ],
+            "brightness": 72.48,
+            "saturation": 13.26,
+            "color_temperature": "neutral",
+            "environment_caption": "a man with glasses and a green shirt",
+            "detected_text": []
+        },
+        {
+            "start_time_ms": 6000,
+            "end_time_ms": 7000,
+            "duration_ms": 1000,
+            "frame_idx": 180,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9580745100975037,
+                    "bbox": {
+                        "x": 462.9479675292969,
+                        "y": 14.6707763671875,
+                        "width": 712.7727355957031,
+                        "height": 699.81494140625
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Unknown_000",
+                    "location": [
+                        108,
+                        984,
+                        418,
+                        674
+                    ],
+                    "confidence": 0.9975273768433653,
+                    "bbox": {
+                        "x": 674.0,
+                        "y": 108.0,
+                        "width": 310.0,
+                        "height": 310.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "happy",
+                        "confidence": 0.9703518152236938
+                    }
+                }
+            ],
+            "shot_type": "long-shot",
+            "environment_caption": "a man with glasses and a green shirt"
+        },
+        {
+            "start_time_ms": 7000,
+            "end_time_ms": 8000,
+            "duration_ms": 1000,
+            "frame_idx": 210,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9580745100975037,
+                    "bbox": {
+                        "x": 462.9479675292969,
+                        "y": 14.6707763671875,
+                        "width": 712.7727355957031,
+                        "height": 699.81494140625
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Unknown_000",
+                    "location": [
+                        108,
+                        984,
+                        418,
+                        674
+                    ],
+                    "confidence": 0.9975273768433653,
+                    "bbox": {
+                        "x": 674.0,
+                        "y": 108.0,
+                        "width": 310.0,
+                        "height": 310.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "happy",
+                        "confidence": 0.9703518152236938
+                    }
+                }
+            ],
+            "shot_type": "medium-shot",
+            "dominant_color": {
+                "name": "Gainsboro",
+                "hex": "#d4d9db",
+                "percentage": 71.0,
+                "is_vibrant": false,
+                "is_muted": true
+            },
+            "color_palette": [
+                {
+                    "name": "Gainsboro",
+                    "hex": "#d4d9db",
+                    "percentage": 71.0,
+                    "is_vibrant": false,
+                    "is_muted": true
+                },
+                {
+                    "name": "Gray",
+                    "hex": "#8b908a",
+                    "percentage": 18.0,
+                    "is_vibrant": false,
+                    "is_muted": true
+                }
+            ],
+            "brightness": 72.48,
+            "saturation": 13.26,
+            "color_temperature": "neutral",
+            "environment_caption": "a man with glasses and a green shirt"
+        },
+        {
+            "start_time_ms": 8000,
+            "end_time_ms": 9000,
+            "duration_ms": 1000,
+            "frame_idx": 240,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9500717520713806,
+                    "bbox": {
+                        "x": 392.6436767578125,
+                        "y": 1.4569091796875,
+                        "width": 763.62890625,
+                        "height": 710.5550537109375
+                    }
+                }
+            ],
+            "faces": [
+                {
+                    "name": "Unknown_001",
+                    "location": [
+                        124,
+                        976,
+                        430,
+                        670
+                    ],
+                    "confidence": 0.0,
+                    "bbox": {
+                        "x": 670.0,
+                        "y": 124.0,
+                        "width": 306.0,
+                        "height": 306.0
+                    },
+                    "frame_dimensions": {
+                        "width": 1280,
+                        "height": 720
+                    },
+                    "emotion": {
+                        "label": "neutral",
+                        "confidence": 0.6319472193717957
+                    }
+                }
+            ],
+            "shot_type": "medium-shot",
+            "environment_caption": "a woman with long hair and a black shirt",
+            "detected_text": []
+        },
+        {
+            "start_time_ms": 9000,
+            "end_time_ms": 10000,
+            "duration_ms": 1000,
+            "frame_idx": 270,
+            "objects": [
+                {
+                    "label": "person",
+                    "confidence": 0.9472954273223877,
+                    "bbox": {
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+            "shot_type": "long-shot",
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+            "detected_text": []
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+                }
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+            "saturation": 52.75,
+            "color_temperature": "warm",
+            "environment_caption": "a woman sitting on a couch"
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+            "frame_idx": 1680,
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+            "environment_caption": "a man in a suit and sunglasses is dancing",
+            "detected_text": []
+        },
+        {
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+            "end_time_ms": 58000,
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+            "objects": [
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+            ],
+            "faces": [],
+            "shot_type": "long-shot",
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+                "name": "Black",
+                "hex": "#010302",
+                "percentage": 65.8,
+                "is_vibrant": false,
+                "is_muted": false
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+                    "hex": "#010302",
+                    "percentage": 65.8,
+                    "is_vibrant": false,
+                    "is_muted": false
+                },
+                {
+                    "name": "Dim Gray",
+                    "hex": "#766d53",
+                    "percentage": 20.2,
+                    "is_vibrant": false,
+                    "is_muted": true
+                }
+            ],
+            "brightness": 16.73,
+            "saturation": 12.41,
+            "color_temperature": "neutral",
+            "environment_caption": "a man in a suit and sunglasses is dancing"
+        },
+        {
+            "start_time_ms": 58000,
+            "end_time_ms": 59000,
+            "duration_ms": 1000,
+            "frame_idx": 1740,
+            "objects": [
+                {
+                    "label": "person",
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+            ],
+            "faces": [],
+            "shot_type": "long-shot",
+            "environment_caption": "a man in a suit and sunglasses is dancing"
+        },
+        {
+            "start_time_ms": 59000,
+            "end_time_ms": 59967,
+            "duration_ms": 967,
+            "frame_idx": 1770,
+            "objects": [
+                {
+                    "label": "person",
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+                        "y": 177.9107666015625,
+                        "width": 477.246337890625,
+                        "height": 534.54296875
+                    }
+                }
+            ],
+            "faces": [],
+            "shot_type": "long-shot",
+            "dominant_color": {
+                "name": "Black",
+                "hex": "#010202",
+                "percentage": 70.8,
+                "is_vibrant": false,
+                "is_muted": false
+            },
+            "color_palette": [
+                {
+                    "name": "Black",
+                    "hex": "#010202",
+                    "percentage": 70.8,
+                    "is_vibrant": false,
+                    "is_muted": false
+                },
+                {
+                    "name": "Dark Gray",
+                    "hex": "#b6b09a",
+                    "percentage": 14.8,
+                    "is_vibrant": false,
+                    "is_muted": true
+                }
+            ],
+            "brightness": 16.73,
+            "saturation": 12.41,
+            "color_temperature": "neutral",
+            "environment_caption": "a man in a suit and sunglasses is dancing",
+            "detected_text": []
+        }
+    ] 
+}
\ No newline at end of file
diff --git a/python/transcribe.py b/python/transcribe.py
index 87ce312b..d7c2ebda 100644
--- a/python/transcribe.py
+++ b/python/transcribe.py
@@ -189,7 +189,15 @@ def is_model_cached(self) -> bool:
         """Check if model is already downloaded"""
         model_repo = self._get_model_path()
         cache_path = Path(self.cache_dir) / model_repo.replace("/", "--")
-        return cache_path.exists() and any(cache_path.iterdir())
+        
+        # Check if path exists first before calling iterdir()
+        if not cache_path.exists():
+            return False
+        
+        try:
+            return any(cache_path.iterdir())
+        except (OSError, FileNotFoundError):
+            return False
 
     def download_model_async(self) -> None:
         """Download model in background thread"""