--- title: StockPredictor emoji: 📈 colorFrom: blue colorTo: indigo sdk: gradio sdk_version: "5.29.0" app_file: app.py pinned: false license: mit --- --- title: StockPredictor emoji: 📈 colorFrom: blue colorTo: indigo sdk: gradio sdk_version: "5.29.0" app_file: app.py pinned: false license: mit 📈 Stock Price Predictor AI-powered stock price predictions using LSTM neural networks, technical analysis, and market sentiment analysis. 🚀 Features ⚡ Fast predictions 🧠 LSTM Deep Learning model 📊 Technical indicators (RSI, MACD, SMA, ROC) 💭 News sentiment analysis 💾 Prediction caching 📱 Gradio web interface 🌍 Supports US and Indian stocks --- 🛠️ Local Setup 1. Clone Repository git clone https://github.com/your-username/stock-predictor.git cd stock-predictor 2. Create Virtual Environment Windows python -m venv venv venv\Scripts\activate macOS/Linux python3 -m venv venv source venv/bin/activate --- 3. Install Dependencies pip install -r requirements.txt --- 4. Add Environment Variables Create a .env file: FINNHUB_API_KEY=your_api_key_here Get a free API key from: https://finnhub.io/register --- 5. Run Application python app.py The application will start at: http://localhost:7860 --- 🌐 Deploy on Hugging Face Spaces Step 1: Create Space 1. Open: https://huggingface.co/spaces 2. Click: Create New Space 3. Configure: SDK: Gradio License: MIT Visibility: Public or Private --- Step 2: Upload Project Files Upload the following files: app.py model.py requirements.txt README.md .env.example .gitignore --- Step 3: Add Repository Secrets Go to: Settings → Repository secrets Add the following secret: Name: FINNHUB_API_KEY Value: your_api_key --- Step 4: Automatic Deployment Hugging Face Spaces will automatically: Install dependencies Run app.py Create a public deployment URL --- 📊 Supported Stock Symbols US Stocks AAPL MSFT GOOGL AMZN TSLA NVDA META NFLX Indian Stocks SBIN.NS INFY.NS RELIANCE.NS TCS.NS HDFCBANK.NS --- 🧠 Model Architecture Input Features ↓ LSTM Layer (32 Units) ↓ Dense Layer ↓ Predicted Price --- 📈 Technical Indicators Used RSI MACD SMA20 Volatility ROC Closing Price --- ⚡ Performance Metric Value Prediction Speed 1-2 seconds Cached Predictions 50-100ms Training Time ~2 minutes Memory Usage 2-3 GB Accuracy (MAPE) ~4.8% Directional Accuracy 76-77% --- ⚠️ Disclaimer This project is for educational purposes only. Not financial advice Markets are unpredictable Always conduct your own research Invest responsibly --- 🔐 Security Notes Never upload .env Use HF Spaces Secrets for API keys No user data stored Uses public Yahoo Finance data --- 🐛 Common Issues Configuration Error on HF Spaces Use this exact YAML block at the top of README.md: Important: YAML must start at line 1 No spaces before --- app_file is required Use supported Gradio versions only --- ModuleNotFoundError pip install -r requirements.txt --- No Data Found Verify stock symbol format. Example: SBIN.NS --- 📁 Project Structure stock-predictor/ │ ├── app.py ├── model.py ├── requirements.txt ├── README.md ├── .env.example └── .gitignore --- 📄 License MIT License --- ❤️ Built With Gradio PyTorch yfinance Finnhub API Python