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43 changes: 43 additions & 0 deletions health-lens/.gitignore
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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.

# dependencies
/node_modules
/.pnp
.pnp.*
.yarn/*
!.yarn/patches
!.yarn/plugins
!.yarn/releases
!.yarn/versions

# testing
/coverage

# next.js
/.next/
/out/

# production
/build

# misc
.DS_Store
*.pem

# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*

# env files (can opt-in for committing if needed)
.env*

# vercel
.vercel

# typescript
*.tsbuildinfo
next-env.d.ts

/app/generated/prisma
53 changes: 53 additions & 0 deletions health-lens/README.md
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**Built with [Hyperbrowser](https://hyperbrowser.ai)**

# HealthLens

Upload lab reports, chat with AI, get research-backed health insights.

## Setup

**Get an API key** at https://hyperbrowser.ai

Install and configure:

```bash
npm install
```

Create `.env`:

```env
HYPERBROWSER_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
```

Run:

```bash
npm run dev
```

Open http://localhost:3000

## How It Works

1. Upload PDF lab report
2. AI extracts health markers (cholesterol, glucose, etc.)
3. Searches PubMed API for relevant research
4. Chat about your results with research context

## Tech

- Next.js 16 + TypeScript
- Anthropic Claude Sonnet 4.5
- PubMed E-utilities API
- Hyperbrowser SDK (for article scraping)
- localStorage (no database)

## Disclaimer

Informational only. Not medical advice. Consult your doctor.

---

Follow @hyperbrowser for updates.
120 changes: 120 additions & 0 deletions health-lens/app/api/chat/route.ts
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import { streamText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { openai } from "@ai-sdk/openai";
import type { NextRequest } from "next/server";

// Using Node.js runtime for better compatibility

function getTextFromParts(parts: Array<{ type: string; text?: string }>): string {
return parts
.filter((p) => p.type === "text" && typeof p.text === "string")
.map((p) => p.text)
.join("");
}

const SYSTEM_PROMPT = `You are a knowledgeable health AI assistant. Analyze health data and provide insights based on medical research.

IMPORTANT: You have access to the user's uploaded medical files (lab reports, health records, etc.) in the context below. When a user asks about their lab results or health data, USE THE FILE CONTENT provided in the context to give specific, personalized answers based on their actual data.

When responding:
1. Reference specific values from their uploaded files when relevant
2. Cite evidence from medical literature when relevant
3. Note limitations and uncertainties
4. Suggest questions for their doctor
5. Provide actionable next steps

Format your response with clear sections using markdown:
- Use **bold** for emphasis
- Use bullet points for lists
- DO NOT use emojis in your response. Keep the tone professional, clean, and clinical.

Always include a short **Data references** section at the end:
- List the specific uploaded file names and values you used
- List research sources with URLs when available
- If no external sources were used, state "Data references: None"

CRITICAL: Always remind users this is informational only and not medical advice. Lab results should be discussed with their healthcare provider.`;

export async function POST(req: NextRequest) {
try {
const body = await req.json();
const uiMessages = body?.messages;
const researchContext = body?.researchContext;
const memoryContext = body?.memoryContext;

if (!Array.isArray(uiMessages)) {
return new Response("messages must be an array", { status: 400 });
}

const provider =
process.env.ANTHROPIC_API_KEY ? "anthropic" : process.env.OPENAI_API_KEY ? "openai" : null;

if (!provider) {
return new Response("API key not configured", { status: 500 });
}

const coreMessages = uiMessages
.filter((m: any) => m && (m.role === "user" || m.role === "assistant"))
.map((m: any) => ({
role: m.role,
content:
typeof m.content === "string"
? m.content
: Array.isArray(m.parts)
? getTextFromParts(m.parts)
: "",
}))
.filter((m: any) => m.content && m.content.trim().length > 0);

// Build enhanced system prompt with research context and memory
let systemPrompt = SYSTEM_PROMPT;

// Add memory context (files + conversation history)
if (memoryContext && typeof memoryContext === "string" && memoryContext.trim().length > 0) {
systemPrompt += `\n\n## Context from User's Health History:\n${memoryContext}\n\nUse this context to provide personalized responses and remember previous discussions.`;
}

// Add research context
if (researchContext && Array.isArray(researchContext) && researchContext.length > 0) {
const researchSummary = researchContext
.map((result: any) => {
const studies = result.studies || [];
return `\n**${result.source}:**\n${studies
.map((s: any) => {
let details = `- **${s.title || "Study"}** ${s.year ? `(${s.year})` : ""}`;
if (s.abstract) details += `\n _Abstract/Snippet:_ ${s.abstract}`;
if (s.keyOutcomes) details += `\n _Key Outcomes:_ ${s.keyOutcomes}`;
return details;
})
.join("\n")}`;
})
.join("\n");

// Debug: Log what research is being passed to LLM
const totalStudies = researchContext.reduce((acc: number, r: any) => acc + (r.studies?.length || 0), 0);
const studiesWithAbstracts = researchContext.reduce((acc: number, r: any) =>
acc + (r.studies?.filter((s: any) => s.abstract)?.length || 0), 0);
console.log(`📚 Passing ${totalStudies} studies to LLM (${studiesWithAbstracts} with abstracts)`);

systemPrompt += `\n\n## Recent Research Found:\n${researchSummary}\n\nPlease reference these sources in your response when relevant. Use the abstract/outcomes to provide specific details.`;
}

const model =
provider === "anthropic"
? anthropic("claude-sonnet-4-5-20250929")
: openai("gpt-4o-mini");

const result = await streamText({
model,
system: systemPrompt,
messages: coreMessages,
temperature: 0.5,
maxOutputTokens: 1500,
});

return result.toTextStreamResponse();
} catch (error: any) {
console.error("Chat API error:", error);
return new Response(error?.message || "Internal server error", { status: 500 });
}
}
102 changes: 102 additions & 0 deletions health-lens/app/api/classify/route.ts
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import { NextRequest, NextResponse } from "next/server";
import { anthropic } from "@ai-sdk/anthropic";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

// Using Node.js runtime for better compatibility

const CLASSIFICATION_PROMPT = `You are a medical question classifier. Analyze if a health question needs research backing from medical databases.

Questions that NEED research:
- Specific medical conditions, treatments, or medications
- Health claims about supplements, diet, exercise
- "What does research say about..."
- "Is there evidence for..."
- Questions about effectiveness, safety, or outcomes

Questions that DON'T need research:
- General symptom descriptions without specific questions
- Personal health tracking ("I feel tired today")
- Appointment scheduling or logistics
- Simple clarifications

Respond ONLY with valid JSON in this exact format:
{
"needsSearch": true/false,
"searchTerms": ["term1", "term2"],
"confidence": 0.0-1.0
}`;

interface ClassificationResult {
needsSearch: boolean;
searchTerms: string[];
confidence: number;
}

export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { question } = body;

if (!question || typeof question !== "string") {
return NextResponse.json(
{ error: "Question is required" },
{ status: 400 }
);
}

const provider = process.env.ANTHROPIC_API_KEY
? "anthropic"
: process.env.OPENAI_API_KEY
? "openai"
: null;

if (!provider) {
return NextResponse.json(
{ error: "API key not configured" },
{ status: 500 }
);
}

const model =
provider === "anthropic"
? anthropic("claude-sonnet-4-5-20250929")
: openai("gpt-4o-mini");

const result = await generateText({
model,
messages: [
{ role: "system", content: CLASSIFICATION_PROMPT },
{ role: "user", content: question },
],
temperature: 0.3,
maxOutputTokens: 200,
});

// Parse the JSON response
const text = result.text.trim();
const jsonMatch = text.match(/\{[\s\S]*\}/);

if (!jsonMatch) {
// Default to not searching if parsing fails
return NextResponse.json({
needsSearch: false,
searchTerms: [],
confidence: 0,
});
}

const classification: ClassificationResult = JSON.parse(jsonMatch[0]);

return NextResponse.json(classification);
} catch (error) {
console.error("Classification error:", error);
// Default to not searching on error
return NextResponse.json({
needsSearch: false,
searchTerms: [],
confidence: 0,
});
}
}

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