AURALEX — FIR Generator & Legal Analyzer

A compact tool that converts spoken incident descriptions into structured First Information Reports (FIR) and suggests relevant Indian legal sections using a Retrieval-Augmented Generation (RAG) approach.

What's in this repo

• Next.js frontend (app/)
• Python audio backend (backend/audio.py) — Fast-Whisper + FastAPI
• IPC/CrPC prediction and analysis code - AURALEX BACKEND

Quick goals

• Fast local dev: run the frontend and the audio backend separately
• Transcribe audio via the backend POST /transcribe (multipart file field: file)

Prerequisites

• Node.js 18+ and npm
• Python 3.10+ and virtualenv
• ffmpeg (recommended) for some audio formats

Frontend (quick start)

1. From the project root:

npm install
npm run dev

2. Open the app in your browser:

• https://localhost:3000 (the project may run with HTTPS enabled) or http://localhost:3000

Firebase Authentication (frontend setup)

1. Create a Firebase project at https://console.firebase.google.com and add a Web app.
2. Copy the web app config values and create a .env.local file in the project root with the keys below (restart the dev server after adding env vars):

NEXT_PUBLIC_FIREBASE_API_KEY=your_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your-project.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your_project_id
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=your_project.appspot.com
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=your_messaging_sender_id
NEXT_PUBLIC_FIREBASE_APP_ID=your_app_id

Audio backend (quick start)

1. Create and activate a Python virtual environment (PowerShell):

python -m venv .venv; .\.venv\Scripts\Activate.ps1

2. Install the minimal backend requirements (this project includes a small requirements.txt):

pip install -r requirements.txt

3. Run the backend from the backend folder:

cd backend; uvicorn audio:app --reload --host 0.0.0.0 --port 8000

4. Health-check (browser or curl):

curl http://127.0.0.1:8000/
# expected: {"status":"Fast-Whisper backend running"}

Transcription endpoint (usage example)

• POST a multipart/form-data with the audio file using field file to /transcribe.

Example curl (local):

curl -X POST "http://127.0.0.1:8000/transcribe" -F "file=@sample.webm"

Notes & tips

• The backend uses faster_whisper and will download / use an internal model. Ensure the machine has enough disk space and required runtime dependencies.
• If you see audio decoding errors, install ffmpeg and retry.
• For production deployments, run the frontend and backend behind proper HTTPS and add CORS/security rules accordingly.

Minimal project structure

• app/ — Next.js app (frontend)
• backend/audio.py — FastAPI service that transcribes audio using Faster-Whisper
• components/, lib/, styles/ — frontend helpers

Project contributors

• R Harikrishnan
• Prabhav Narayanan
• Dipesh V Sabu
• Muhammed Jaseel T A

User Interface

Desktop	Mobile

AURALEX.mp4