MediScan-AI_Smart-Report-Analyzer

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MediScan-AI_Smart-Report-Analyzer

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AI-powered medical report analyzer that extracts, interprets, and summarizes clinical reports using OCR, NLP, and intelligent health insights. Built with FastAPI, modern frontend technologies, and automated report processing pipelines for smarter healthcare assistance.

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# 🩺 MediScan AI
### Smart Medical Report Analyzer

**Upload any blood test or lab report → Get AI-powered health insights in seconds.**

[![FastAPI](https://img.shields.io/badge/FastAPI-0.136-009688?style=flat-square&logo=fastapi&logoColor=white)](https://fastapi.tiangolo.com/)
[![Next.js](https://img.shields.io/badge/Next.js-16.2-000000?style=flat-square&logo=nextdotjs&logoColor=white)](https://nextjs.org/)
[![Python](https://img.shields.io/badge/Python-3.11+-3776AB?style=flat-square&logo=python&logoColor=white)](https://python.org/)
[![Groq](https://img.shields.io/badge/Groq-LLM-F55036?style=flat-square)](https://groq.com/)
[![OCR.space](https://img.shields.io/badge/OCR.space-API-blueviolet?style=flat-square)](https://ocr.space/)
[![SQLite](https://img.shields.io/badge/SQLite-3-003B57?style=flat-square&logo=sqlite&logoColor=white)](https://sqlite.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow?style=flat-square)](https://raw.githubusercontent.com/SYS-SharedBuilds/MediScan-AI_Smart-Report-Analyzer/master/LICENSE)

> ⚠️ **Demo purposes only.** Not a substitute for professional medical advice.

</div>

---

## 📖 Table of Contents

- [⚡ How to Run](#-how-to-run)
- [Overview](#-overview)
- [Live Demo Flow](#-live-demo-flow)
- [Features](#-features)
- [Architecture](#-architecture)
- [Tech Stack](#-tech-stack)
- [Project Structure](#-project-structure)
- [Getting Started](#-getting-started)
  - [Prerequisites](#prerequisites)
  - [Backend Setup](#backend-setup)
  - [Frontend Setup](#frontend-setup)
  - [Environment Variables](#environment-variables)
- [API Reference](#-api-reference)
- [Analysis Pipeline](#-analysis-pipeline)
- [Database Schema](#-database-schema)
- [Health Scoring](#-health-scoring)
- [Known Issues & Limitations](#-known-issues--limitations)
- [Roadmap](#-roadmap)

---

## ⚡ How to Run

> **TL;DR** — Two terminals, four commands, and you're live.

### Step 1 — Clone & configure secrets

```bash
git clone https://github.com/Autonomous-Drone-Target-Tracking-System/MediScan-AI_Smart-Report-Analyzer.git
cd MediScan-AI_Smart-Report-Analyzer

# Copy the env template and fill in your API keys
copy .env.example .env
```

Open `.env` and set:
```env
GROQ_API_KEY=<your Groq key>        # https://console.groq.com/keys
OCR_SPACE_API_KEY=<your OCR key>    # https://ocr.space/ocrapi  (free tier OK)
FRONTEND_URL=http://localhost:3000
NEXT_PUBLIC_API_URL=http://localhost:8000
```

---

### Step 2 — Start the Backend (Terminal 1)

```bash
cd backend

# Create & activate virtual environment
python -m venv venv
venv\Scripts\activate          # Windows
# source venv/bin/activate     # macOS / Linux

# Install dependencies
pip install -r requirements.txt

# Run the API server
uvicorn main:app --reload --host 0.0.0.0 --port 8000
```

✅ Backend ready at **http://localhost:8000** · Swagger docs at **http://localhost:8000/docs**

---

### Step 3 — Start the Frontend (Terminal 2)

```bash
cd frontend

# Copy frontend env
copy .env.local.example .env.local

# Install dependencies
npm install

# Start the dev server
npm run dev
```

✅ Frontend ready at **http://localhost:3000**

---

### Step 4 — Use the App

```
1. Open http://localhost:3000
2. Click "Analyze My Report"
3. Drag & drop a PDF or image of a blood/lab report
4. Click "Analyze Report"
5. Wait ~10–15 seconds → dashboard with your health score & AI insights
```

> 💡 **No Tesseract?** The app works fine without it — OCR.space handles scanned documents, and pdfplumber handles digital PDFs. Tesseract is only a local fallback.

---
    
## 🌟 Overview

**MediScan AI** is a full-stack web application built for a hackathon that transforms raw medical lab reports (PDFs or images) into clear, actionable health insights — powered by OCR and large language models.

A user uploads their blood test or lab report, and within seconds receives:
- A **health score** (0–100)
- Color-coded **risk classification** for each biomarker (Normal / Moderate / Critical)
- **Plain-English AI explanations** for every marker
- **Personalized recommendations** generated by Groq LLM
- A persistent **dashboard** they can revisit at any time

No sign-up. No medical degree required. Just clarity.

---

## 🎬 Live Demo Flow

```
1. Visit http://localhost:3000
2. Click "Analyze My Report" → Upload page
3. Drag & drop a PDF or image of a lab report
4. Click "Analyze Report"
5. ⏱️  ~10–15 seconds later → redirected to your personal Dashboard
6. See your health score, biomarker table, AI summary, and recommendations
```

---

## ✨ Features

| Feature | Description |
|---------|-------------|
| 📄 **Smart OCR Extraction** | pdfplumber (text PDFs) → OCR.space API (scanned) → Tesseract (local fallback) |
| 🧠 **AI Interpretation** | Groq LLM generates plain-English explanations for every biomarker |
| 📊 **Risk Dashboard** | Health score gauge, color-coded biomarker cards, risk badges |
| 🛡️ **Rule-Based Validation** | Clinical reference ranges engine — grounds AI output in facts |
| ⚡ **Results in Seconds** | Upload to full dashboard in under 15 seconds |
| 🔬 **30+ Biomarkers** | Hemoglobin, LDL, Blood Sugar, TSH, Vitamin D, Creatinine, and more |
| 💾 **Persistent Reports** | All analyses stored in SQLite — revisit any report via dashboard URL |
| 📱 **Fully Responsive** | Mobile-first design, works on phones, tablets, and desktops |
| 🔒 **No Login Required** | Zero friction — upload and analyze immediately |

---

## 🏗️ Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                        USER BROWSER                             │
│                    Next.js 16 Frontend                          │
│         Landing → Upload → Dashboard (per report_id)            │
└──────────────────────┬──────────────────────────────────────────┘
                       │ HTTP (axios)
                       ▼
┌────────────────────────────────────────────────────────────────┐
│                    FastAPI Backend :8000                       │
│                                                                │
│  POST /api/upload          POST /api/analyze/{id}              │
│  GET  /api/report/{id}     GET  /docs (Swagger)                │
│                                                                │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                  Analysis Pipeline                      │   │
│  │                                                         │   │
│  │  1. OCR Router ──────────────────────────────────────┐  │   │
│  │     ├── pdfplumber (text-based PDFs)                 │  │   │
│  │     ├── OCR.space API (scanned PDFs & images)        │  │   │
│  │     └── Tesseract + OpenCV (local fallback)          │  │   │
│  │                                                      │  │   │
│  │  2. Medical Parser ──── regex + keyword matching     │  │   │
│  │     └── Extracts: name, value, unit, ref range       │  │   │
│  │                                                      │  │   │
│  │  3. Risk Engine ───────────────────────────────────┐ │  │   │
│  │     ├── Classifies: Normal / Moderate / Critical   │ │  │   │
│  │     └── Calculates: Health Score (0–100)           │ │  │   │
│  │                                                    │ │  │   │
│  │  4. Groq LLM ──────────────────────────────────── ◄┘ │  │   │
│  │     ├── Per-biomarker plain-English explanations     │  │   │
│  │     ├── Overall health summary                       │  │   │
│  │     └── Personalized recommendations                 │  │   │
│  │                                                      │  │   │
│  │  5. SQLite Persistence ──────────────────────────────┘  │   │
│  └─────────────────────────────────────────────────────────┘   │
└────────────────────────────────────────────────────────────────┘
                       │
                       ▼
              SQLite (medical.db)
         reports + biomarkers tables
```

---

## 🛠️ Tech Stack

### Backend
| Technology | Version | Purpose |
|------------|---------|---------|
| **FastAPI** | 0.136 | REST API framework |
| **Uvicorn** | 0.47 | ASGI server |
| **pdfplumber** | 0.11.9 | Text extraction from digital PDFs |
| **OCR.space API** | — | Cloud OCR for scanned documents |
| **pytesseract** | 0.3.13 | Local OCR fallback |
| **OpenCV** | 4.13 | Image preprocessing for OCR |
| **Groq SDK** | 1.2.0 | LLM inference (llama3 models) |
| **SQLite3** | built-in | Lightweight persistence |
| **Pydantic** | 2.13 | Data validation & schemas |
| **python-multipart** | 0.0.29 | File upload handling |
| **python-dotenv** | 1.2.2 | Environment variable loading |

### Frontend
| Technology | Version | Purpose |
|------------|---------|---------|
| **Next.js** | 16.2.6 (Turbopack) | React framework with SSR/CSR |
| **TypeScript** | 5 | Type safety |
| **framer-motion** | — | Animations & micro-interactions |
| **Recharts** | — | Health score gauge & charts |
| **axios** | — | HTTP client for API calls |
| **Lucide React** | — | Icon library |
| **Poppins + Inter** | Google Fonts | Typography |
| **Vanilla CSS** | — | Custom design system with CSS tokens |

---

## 📁 Project Structure

```
Hackethon/
├── .env                          # Root env file (shared API keys)
├── README.md
│
├── backend/
│   ├── main.py                   # FastAPI app entry point, CORS, lifespan
│   ├── requirements.txt          # Python dependencies
│   ├── medical.db                # SQLite database (auto-created)
│   │
│   ├── routes/
│   │   ├── upload.py             # POST /api/upload
│   │   └── analyze.py            # POST /api/analyze/{id}, GET /api/report/{id}
│   │
│   ├── services/
│   │   ├── pipeline.py           # Main orchestrator: OCR→Parse→Classify→AI→DB
│   │   ├── ocr_router.py         # Routes to PDF or image extractor
│   │   ├── pdf_service.py        # pdfplumber + OCR.space PDF fallback
│   │   ├── ocr_service.py        # OCR.space API + Tesseract local fallback
│   │   ├── medical_parser.py     # Regex-based biomarker extraction
│   │   ├── risk_engine.py        # Clinical range classification + health score
│   │   └── ai_service.py        # Groq LLM: explanations, summary, recommendations
│   │
│   ├── db/
│   │   ├── database.py           # SQLite connection factory
│   │   ├── init_db.py            # Schema creation & migrations
│   │   └── crud.py               # All DB read/write operations
│   │
│   ├── models/
│   │   └── schemas.py            # Pydantic request/response models
│   │
│   └── uploads/                  # Uploaded files (gitignored)
│
└── frontend/
    ├── .env.local                 # NEXT_PUBLIC_API_URL
    ├── app/
    │   ├── layout.tsx             # Root layout (fonts, metadata)
    │   ├── globals.css            # Design system (CSS tokens, utilities)
    │   ├── page.tsx               # Landing page
    │   ├── upload/
    │   │   └── page.tsx           # Upload page (drag-and-drop)
    │   └── dashboard/
    │       └── [reportId]/
    │           └── page.tsx       # Results dashboard (dynamic route)
    │
    └── components/
        └── Navbar.tsx             # Sticky responsive navbar
```

---

## 🚀 Getting Started

### Prerequisites

| Requirement | Notes |
|-------------|-------|
| **Python 3.11+** | Backend runtime |
| **Node.js 18+** | Frontend runtime |
| **Tesseract OCR** | [Download for Windows](https://github.com/UB-Mannheim/tesseract/wiki) — optional fallback |
| **Groq API Key** | Free at [console.groq.com](https://console.groq.com/) |
| **OCR.space API Key** | Free at [ocr.space](https://ocr.space/OCRAPI) — optional |

---

### Backend Setup

```bash
# 1. Navigate to backend
cd backend

# 2. Create and activate virtual environment
python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Start the development server
uvicorn main:app --reload --host 0.0.0.0 --port 8000
```

The API will be available at:
- **Base URL:** http://localhost:8000
- **Swagger UI:** http://localhost:8000/docs
- **ReDoc:** http://localhost:8000/redoc

---

### Frontend Setup

```bash
# 1. Navigate to frontend
cd frontend

# 2. Install dependencies
npm install

# 3. Start the development server
npm run dev
```

The frontend will be available at **http://localhost:3000**

---

### Environment Variables

Create a `.env` file in the **project root** (`Hackethon/.env`):

```env
# ── AI (Groq) ──────────────────────────────────────────────────────────────────
GROQ_API_KEY=your_groq_api_key_here

# ── OCR (OCR.space) ────────────────────────────────────────────────────────────
# Get a free key at https://ocr.space/OCRAPI
# Leave empty to use Tesseract-only fallback
OCR_SPACE_API_KEY=your_ocrspace_key_here

# ── CORS ───────────────────────────────────────────────────────────────────────
FRONTEND_URL=http://localhost:3000
```

Create a `.env.local` file inside `frontend/`:

```env
# ── API URL ─────────────────────────────────────────────────────────────────────
NEXT_PUBLIC_API_URL=http://localhost:8000
```

> 💡 **Groq Model Note:** Ensure you use a currently supported model in `ai_service.py`.  
> As of 2026, use `llama-3.3-70b-versatile` or `llama3-70b-8192`. The `llama3-8b-8192` model has been decommissioned.

---

## 📡 API Reference

### `POST /api/upload`
Upload a medical report file.

**Request:** `multipart/form-data`
| Field | Type | Description |
|-------|------|-------------|
| `file` | File | PDF, JPG, or PNG — max 20 MB |

**Response:**
```json
{
  "report_id": 5,
  "document_url": "/uploads/abc123.pdf",
  "message": "File uploaded successfully"
}
```

---

### `POST /api/analyze/{report_id}`
Run the full analysis pipeline on an uploaded report.

**Response:** `AnalysisResult`
```json
{
  "report_id": 5,
  "health_score": 70,
  "biomarkers": [
    {
      "marker_id": 12,
      "marker_name": "Hemoglobin",
      "extracted_value": 10.2,
      "unit": "g/dL",
      "risk_category": "Moderate",
      "ai_explanation": "Your hemoglobin is slightly below the normal range..."
    }
  ],
  "ai_summary": "Your report shows mild anemia with otherwise normal metabolic markers...",
  "recommendations": [
    "Consult a hematologist about your hemoglobin levels",
    "Consider iron-rich foods like spinach and lentils"
  ]
}
```

---

### `GET /api/report/{report_id}`
Retrieve a previously analyzed report from the database.

**Response:** Same `AnalysisResult` schema as above.

**Error (404):** Report not found or analysis not yet run.

---

## 🔬 Analysis Pipeline

The pipeline in `services/pipeline.py` runs 7 sequential steps:

```
Step 1: OCR Extraction
    ├── pdfplumber  → text-based PDFs (fastest, most accurate)
    ├── OCR.space   → scanned PDFs and images (cloud, handles tables well)
    └── Tesseract   → local fallback with OpenCV preprocessing

Step 2: Biomarker Parsing  (medical_parser.py)
    └── Regex + keyword matching to extract:
        name, numeric value, unit, reference range

Step 3: Risk Classification  (risk_engine.py)
    ├── Normal   → within reference range (or <10% deviation)
    ├── Moderate → 10–25% outside reference range
    └── Critical → >25% outside reference range

Step 4: Health Score Calculation  (risk_engine.py)
    └── Start at 100, deduct 15 per Critical, 7 per Moderate (min: 0)

Step 5: AI Explanations  (ai_service.py → Groq)
    └── Per-biomarker plain-English explanation

Step 6: AI Summary & Recommendations  (ai_service.py → Groq)
    ├── Overall health narrative
    └── Prioritized, actionable recommendations

Step 7: Persist to Database  (db/crud.py)
    └── health_score, biomarkers, ai_summary, recommendations → SQLite
```

---

## 🗄️ Database Schema

```sql
-- Users (placeholder, no auth currently)
CREATE TABLE users (
    user_id    INTEGER PRIMARY KEY AUTOINCREMENT,
    created_at TEXT    DEFAULT (datetime('now'))
);

-- Medical Reports
CREATE TABLE reports (
    report_id            INTEGER PRIMARY KEY AUTOINCREMENT,
    user_id              INTEGER REFERENCES users(user_id),
    upload_timestamp     TEXT    DEFAULT (datetime('now')),
    document_url         TEXT    NOT NULL,
    overall_health_score INTEGER DEFAULT 100,
    ai_summary           TEXT,
    recommendations      TEXT    -- JSON array stored as string
);

-- Biomarker Results
CREATE TABLE biomarkers (
    marker_id       INTEGER PRIMARY KEY AUTOINCREMENT,
    report_id       INTEGER NOT NULL REFERENCES reports(report_id) ON DELETE CASCADE,
    marker_name     TEXT    NOT NULL,
    extracted_value REAL,
    unit            TEXT,
    risk_category   TEXT    CHECK(risk_category IN ('Normal','Moderate','Critical')) DEFAULT 'Normal',
    ai_explanation  TEXT
);
```

---

## 📊 Health Scoring

The health score (0–100) is calculated deterministically from biomarker risk classifications:

| Risk Level | Deduction | Trigger Condition |
|------------|-----------|-------------------|
| **Critical** | −15 points | Value >25% outside reference range |
| **Moderate** | −7 points | Value 10–25% outside reference range |
| **Normal** | −0 points | Value within range (or <10% deviation) |

**Example:**
```
13 biomarkers found:
  → 3 Critical  = 3 × 15 = 45 points deducted
  → 2 Moderate  = 2 ×  7 = 14 points deducted
  → 8 Normal    = 0 deducted

Health Score = max(0, 100 - 59) = 41
```

---

## ⚠️ Known Issues & Limitations

| Issue | Impact | Fix / Workaround |
|-------|--------|-----------------|
| `llama3-8b-8192` model decommissioned | AI explanations fail silently | Update model in `ai_service.py` to `llama-3.3-70b-versatile` |
| No user authentication | All reports are public by `report_id` | Add JWT auth for production |
| SQLite not suitable for production | Single-writer lock, no concurrent writes | Migrate to PostgreSQL for deployment |
| OCR.space free tier limits | 500 API calls/month | Use Tesseract fallback or upgrade plan |
| Report history not shown in UI | Users must know their `report_id` | Add a History page |

---

## 🗺️ Roadmap

- [ ] **Fix Groq model** → update to `llama-3.3-70b-versatile`
- [ ] **Report History page** — list all past uploads with timestamps
- [ ] **Export to PDF** — print-friendly dashboard report
- [ ] **Authentication** — user accounts via NextAuth / Supabase
- [ ] **Trend Analysis** — compare reports over time (same user)
- [ ] **Deploy** — Vercel (frontend) + Railway/Render (backend)
- [ ] **PostgreSQL migration** — replace SQLite for production
- [ ] **More biomarkers** — expand the reference range database

---

## 👥 Team

Built for **HackXcelarate 2K26** by the Optimus Devs.

---

## 📄 License

MIT License — see [LICENSE](LICENSE) for details.

---

Made with ❤️ and ☕ for the  **HackXcelarate 2K26**

**[🚀 Upload Your Report](http://localhost:3000/upload)** · **[📖 API Docs](http://localhost:8000/docs)**

</div>

MediScan-AI_Smart-Report-Analyzer

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