README.md

metadata

title: Sanjeevani AI API
emoji: 🏥
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

Sanjeevani AI API

AI-powered healthcare backend API for the Sanjeevani platform.

SanjeevaniAI — Industry-Grade Healthcare AI Platform

Clinical Notice & Positioning: SanjeevaniAI provides AI-assisted healthcare information and clinical decision-support insights. It is not an autonomous diagnostic system and is not a substitute for direct clinical examination, laboratory diagnosis, or emergency medical care.

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🏥 Architecture Overview

SanjeevaniAI Platform
├── Frontend (Next.js 14 + Tailwind CSS + Lucide Icons)
│   ├── /                -> Landing page with medical disclaimer & capabilities
│   ├── /dashboard       -> Clinical intelligence overview & metrics
│   ├── /ner             -> Real-time local RoBERTa BC5CDR NER visualizer
│   ├── /reports         -> Document upload, SHA-256 integrity & analysis
│   ├── /reports/[id]    -> Structured clinical findings & raw text
│   ├── /assistant       -> Conversational AI with emergency triage
│   ├── /profile         -> Patient physiological profile & BMI
│   ├── /history         -> Traceable medical timeline
│   ├── /admin           -> System telemetry & security audit logs
│   └── /demo            -> Guided 5-step mentor presentation walkthrough
│
├── Backend (FastAPI + Async SQLAlchemy + PyTorch)
│   ├── Core & Security  -> JWT, bcrypt hashing, RBAC, Request ID, CSP headers
│   ├── ML Engine        -> Local RoBERTa-large BC5CDR (355M params, CUDA/CPU)
│   ├── Document Pipeline-> PDF/DOCX/TXT parser, chunker, entity aggregator
│   ├── AI Assistant     -> Multi-provider LLM (Gemini + Mock) & triage engine
│   └── Persistence      -> Async SQLite / PostgreSQL with audit trail

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⚡ Quick Start

1. Prerequisites

• Python: 3.10+ (Recommended: Python 3.11)
• Node.js: 18.x or 20+ (with npm)
• NVIDIA GPU (Optional): CUDA 11.8+ for accelerated inference (CPU fallback automatic)
• Pretrained NER Model: Stored locally in models/bc5cdr-ner

2. Backend Setup

# Activate virtual environment
python -m venv .venv
.\.venv\Scripts\Activate.ps1  # Windows PowerShell
# source .venv/bin/activate    # Linux / macOS

# Install dependencies
pip install -r requirements.txt

# Run synthetic demo database seeder
python scripts/seed_demo_data.py

# Launch FastAPI backend
uvicorn backend.app.main:app --host 0.0.0.0 --port 8000 --reload

API Swagger UI will be live at http://localhost:8000/docs.

3. Frontend Setup

cd frontend

# Install npm dependencies
npm install

# Run Next.js development server
npm run dev

Application UI will be live at http://localhost:3000.

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🐳 Docker Deployment

To launch the full containerized stack:

docker-compose up --build

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• Health Check: http://localhost:8000/api/v1/health

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🧪 Test Suite & Verification

The platform includes a comprehensive automated test suite in backend/tests/:

pytest backend/tests -v

Verified Test Results (16/16 Passing):

• test_auth.py: User registration, login, JWT validation, and invalid credential handling.
• test_ner.py: BC5CDR model info, entity extraction, character span alignment, and 422 validation.
• test_documents.py: File upload, SHA-256 fingerprinting, raw text extraction, and entity linking.
• test_chat.py: Multi-turn consultation, patient profile injection, and heuristic red-flag emergency detection.
• test_profile.py: Patient profile updates, vitals calculations, and RBAC admin permission enforcement.
• test_health.py: Liveness and readiness probes.

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👨‍🏫 Mentor Demonstration Credentials

Use the "Demo Roles" dropdown in the navigation bar or log in with these pre-seeded accounts:

Account	Email	Password	Role / Access Level
Patient	demo.patient@sanjeevani.ai	DemoPatient2026!	Health profile, documents, AI consultation
Doctor	demo.doctor@sanjeevani.ai	DemoDoctor2026!	Clinical decision support & document review
Administrator	demo.admin@sanjeevani.ai	DemoAdmin2026!	Platform telemetry, model status, security audit logs

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📚 Technical Documentation

• 📐 System Architecture & Mermaid Diagrams
• 🔌 REST API Catalog & OpenAPI Specification
• 🧠 Machine Learning & Local RoBERTa BC5CDR Specs
• 🎯 10-Minute Mentor Presentation Script
• 🔍 Initial Project Audit & Gap Analysis

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🛡️ Medical Safety & Privacy Disclaimers

1. AI-Assisted Decision Support: SanjeevaniAI generates educational insights to assist healthcare providers and patients. It does not issue binding clinical diagnoses or prescribe treatment plans.
2. Emergency Triage Protocol: If severe acute symptoms (e.g. crushing chest pain, difficulty breathing, stroke symptoms) are entered, the platform immediately presents an emergency escalation alert advising immediate contact with emergency medical services (911 / 112 / 108).
3. Data Security: Uploaded files are fingerprinted with SHA-256, sensitive credentials hashed with direct bcrypt, and all administrative events logged in an immutable audit table.

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📄 License

MIT License. Developed for healthcare AI intelligence and clinical decision-support research.