🚀 PromptPal

PromptPal is a full-stack AI-powered web app to save, organize, and share your favorite ChatGPT prompts — with built-in intelligent AI suggestions powered by Google Gemini.

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🌟 Features

• 💾 Save and tag your prompts
• 🔒 Mark prompts public or private
• 🤖 Get intelligent AI-powered prompt suggestions (Google Gemini API + Smart Engine)
• 🌍 Explore community prompts
• 🎨 Clean modern UI (Tailwind CSS)
• 🔐 User authentication (JWT)
• ⚡ Fast, context-aware suggestions with gemini-2.5-flash-lite

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🧠 Tech Stack

Layer	Tech
Frontend	React, Vite, Tailwind CSS, React Router
Backend	Node.js, Express.js, MongoDB, Mongoose
AI Service	Flask, Python, Google Gemini API + Smart Engine
Auth	JWT (Firebase Auth planned)
Deployment	Vercel, Render, MongoDB Atlas

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🚀 Setup Instructions

🐳 Docker Setup (Recommended - Easy!)

One-command deployment with Docker:

# 1. Copy environment file
cp .env.example .env

# 2. Edit .env and add your API keys
nano .env

# 3. Start everything with Docker
./docker-start.sh
# OR manually: docker-compose up -d

That's it! Access at http://localhost:3000

📖 Read the complete Docker guide for troubleshooting and advanced usage.

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📦 Manual Setup (Alternative)

Each service runs in its own terminal window:

1. Get Google Gemini API Key

# Visit Google AI Studio and create a free API key
# https://aistudio.google.com/app/apikey

# Copy your API key for the next step

2. Frontend Setup

cd client
npm install
npm run dev

3. Backend (API server) Setup

cd server
npm install

# Create .env file
cp .env.example .env
# Edit .env with your MongoDB URI and JWT secret

npm start

4. Flask AI Service Setup

cd flask-backend
pip install -r requirements.txt

# Copy environment variables
cp .env.example .env

# Edit .env with your settings:
# FLASK_PORT=5001
# CORS_ORIGIN=http://localhost:5173
# GEMINI_API_KEY=your_gemini_api_key_here

python app.py

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🤖 AI Engine Features

PromptPal uses a hybrid AI approach with Google Gemini:

🌟 Google Gemini API (Primary AI Engine)

• Powered by gemini-2.5-flash-lite: Fast, efficient, and cost-effective
• High-quality suggestions: Advanced language understanding
• Context-aware: Analyzes your prompt and tags intelligently
• Rate limiting: Smart caching and quota management
• Reliable: Production-ready with automatic retry logic

🧠 Smart Suggestion Engine (Built-in Fallback)

• Context-aware: Analyzes prompt type and tags
• Intelligent enhancement: Category-specific improvements for:
  • ✍️ Writing & Content
  • 💼 Business & Marketing
  • 💻 Technical & Development
  • 🎨 Creative & Design
  • 📚 Educational & Learning
  • 📊 Analysis & Research
  • 💪 Health & Fitness
• Always available: Works even without API key
• Zero-cost: No API charges for fallback mode

🔄 Automatic Fallback System

User Request → Gemini API (if quota available) → Smart Engine (if needed) → Enhanced Suggestions

Benefits:

• ✅ Never fails to provide suggestions
• ✅ Optimized for quota efficiency
• ✅ 1-hour caching reduces duplicate requests
• ✅ 15 requests/minute rate limiting
• ✅ Graceful degradation on quota limits

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📁 Project Structure

PromptPal/
├── client/          # React frontend
├── server/          # Node.js API backend
├── flask-backend/   # Python AI service
└── README.md

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🌐 Live Demo

• Frontend: https://prompt-pal-murex.vercel.app
• Backend API: https://promptpal-umwk.onrender.com/
• AI Service: Google Gemini API (production) + Smart Engine (fallback)

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🔧 Environment Variables

Flask Backend (.env)

FLASK_PORT=5001
CORS_ORIGIN=http://localhost:5173
GEMINI_API_KEY=your_gemini_api_key_here

Get your Gemini API key:

1. Visit Google AI Studio
2. Create a new API key
3. Copy and paste into your .env file

Node.js Backend (.env)

PORT=3001
NODE_ENV=development
MONGODB_URI=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret_key
CORS_ORIGIN=http://localhost:5173

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🚀 Deployment

Production Architecture

Vercel (Frontend) → Render (Flask AI + Gemini API) → Smart Engine Fallback
                 → Render (Node.js API) → MongoDB Atlas

Deployment Steps:

1. Frontend (Vercel)

   • Connect your GitHub repository
   • Deploy from client/ directory
   • Set build command: npm run build
   • Set output directory: dist

2. Flask AI Service (Render)

   • Deploy from flask-backend/ directory
   • Add environment variable: GEMINI_API_KEY
   • Start command: python app.py

3. Node.js Backend (Render)

   • Deploy from server/ directory
   • Add environment variables: MONGODB_URI, JWT_SECRET
   • Start command: npm start

Note: Gemini API provides free tier with generous quotas. Smart Engine automatically activates if quota is exceeded.

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you'd like to change.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Test locally with Gemini API and Smart Engine fallback
4. Commit your changes (git commit -m 'Add some AmazingFeature')
5. Push to the branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

Testing AI Suggestions

# Test Gemini API directly
cd flask-backend
python test_model_switch.py

# Test full suggestion endpoint
curl -X POST http://localhost:5001/suggest \
  -H "Content-Type: application/json" \
  -d '{"promptText":"Write a blog post","tags":["writing"]}'

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📊 API Quotas & Limits

Google Gemini API Free Tier:

• Generous monthly quota
• 15 requests per minute (enforced by Flask backend)
• Smart caching reduces duplicate requests
• Automatic fallback to Smart Engine on quota limits

Monitor your usage:

• Google AI Studio Usage Dashboard

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🔒 Security Notes

• Never commit .env files to version control
• API keys are sensitive - store them securely
• Use environment variables for all secrets
• The .gitignore is configured to exclude .env files
• For production, use platform-specific secret management

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📬 Contact

For questions or feedback, open an issue or reach out to vishalm8656@gmail.com.

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📄 License

This project is open source and available under the MIT License.