PharmaLens

An intelligent pharmaceutical analysis platform with AI-powered insights and real-time data processing.

Project Structure

PharmaLens/
├── ai_engine/          # Python AI/ML backend
├── server/             # Node.js API server
├── client/             # React frontend
└── docs/              # Documentation

Prerequisites

• Python 3.8+ (for AI engine)
• Node.js 16+ (for server and client)
• Git

Setup Instructions

1. Clone the Repository

git clone https://github.com/ritik0506/PharmaLens.git
cd PharmaLens

2. Set Up AI Engine (Python Backend)

cd ai_engine

# Install dependencies (globally or in venv)
pip install -r requirements.txt

# Copy environment file
copy .env.example .env
# Edit .env and add your OpenAI API key for LLM features
# Note: App works in deterministic mode without API key

3. Set Up Server (Node.js Backend)

cd ../server

# Install dependencies
npm install

# Copy environment file
copy .env.example .env
# Edit .env if needed

4. Set Up Client (React Frontend)

cd ../client

# Install dependencies
npm install

# Copy environment file
copy .env.example .env
# Edit .env if needed

Running the Application

Option 1: Quick Start (Windows - Recommended)

# Run the startup script (starts all services automatically)
.\start.ps1

Option 2: Manual Start

Terminal 1: Start AI Engine

cd ai_engine
python -m uvicorn app.main:app --reload --port 8000

Terminal 2: Start Server

cd server
npm start

Terminal 3: Start Client

cd client
npm run dev

Note: No virtual environment is required if Python dependencies are installed globally.

Access the Application

• Frontend: http://localhost:5173 (or 5174 if 5173 is in use)
• API Server: http://localhost:3001
• AI Engine: http://localhost:8000

Environment Variables

AI Engine (.env)

• OPENAI_API_KEY: Your OpenAI API key (optional, enables GPT-4 powered agents)
• CLOUD_ENABLED: Enable/disable cloud LLM
• LOCAL_ENABLED: Enable/disable local model
• LOCAL_MODEL_PATH: Path to local Llama model file (.gguf)

Note: Application works perfectly without LLM configuration using deterministic responses.

To enable AI-powered insights:

• Quick Setup: Run .\setup-llm.ps1 (interactive configuration)
• Detailed Guide: See LLM_SETUP_GUIDE.md

Server (.env)

• PORT: Server port (default: 3001)
• CLIENT_URL: Frontend URL for CORS
• AI_ENGINE_URL: AI engine URL

Client (.env)

• VITE_API_URL: Backend API URL

Features

• 🤖 AI-powered pharmaceutical analysis (Cloud GPT-4 or Local Llama)
• ⚡ Real-time data processing with 12 specialized agents
• 📊 Interactive dashboard with ROI calculations
• 🎯 Multi-agent system (IQVIA, Clinical, Patent, Market, etc.)
• 🔒 Dual LLM support: Cloud (OpenAI) or Local (HIPAA-compliant)
• 🧪 Deterministic mode for testing without LLM

LLM Configuration

The application supports three modes:

1. Cloud Mode - OpenAI GPT-4 (best quality, requires API key)
2. Local Mode - Llama models (HIPAA-compliant, requires model download)
3. Deterministic Mode - Pre-programmed responses (no setup required)

See LLM_SETUP_GUIDE.md for configuration instructions.

Documentation

Detailed documentation is available in the docs/ directory:

• Quick Start Guide
• Architecture Overview
• LLM Integration

Contributing

When working on this project:

1. Create a new branch for your feature
2. Make your changes
3. Test thoroughly
4. Submit a pull request

Common Issues

Port Already in Use

If you get a port conflict error, change the port in the respective .env file.

Dependencies Not Installing

Make sure you're using compatible versions of Python and Node.js.

Virtual Environment Issues

Ensure you've activated the virtual environment before installing Python packages or running the AI engine.

Team Collaboration

Each team member should:

1. Clone the repository
2. Set up all three components (ai_engine, server, client)
3. Copy .env.example to .env in each directory
4. Install dependencies
5. Create a new branch for their work

License

MIT License - See LICENSE file for details