Update README.md

README.md

@@ -1,109 +1,11 @@
-# 🧬 Bioinformatics AI Agent - Heart Failure Risk Prediction
-
-A Gradio-based web interface for predicting heart failure risk from gene expression data.
-
-## 🚀 Quick Start
-
-### Local Development
-
-1. **Install dependencies:**
-```bash
-pip install -r requirements.txt
-```
-
-2. **Run the application:**
-```bash
-python app.py
-```
-
-3. **Open your browser:**
-The app will automatically open at `http://localhost:7860`
-
-## 📁 Input File Format
-
-Your input file should be structured as follows:
-
-| Sample_ID (or Unnamed: 0) | Gene_1 | Gene_2 | Gene_3 | ... |
-|---------------------------|--------|--------|--------|-----|
-| Sample_001                | 0.234  | 1.567  | 0.891  | ... |
-| Sample_002                | 0.456  | 1.234  | 0.678  | ... |
-| Sample_003                | 0.789  | 1.890  | 0.345  | ... |
-
-- **First column:** Sample identifiers (can be named or unnamed)
-- **Remaining columns:** Numeric gene expression values
-
-Supported formats: `.csv`, `.xlsx`
-
-## 📊 Output
-
-The application returns a DataFrame with:
-- **Sample_ID:** Original sample identifier
-- **Age:** Predicted age (20-90 years)
-- **Heart_Failure_Risk:** Risk score (0-1, where 1 indicates highest risk)
-
-## 🔧 Customization
-
-### Adding Your Model
-
-Replace the placeholder prediction logic in `app.py`:
-
-```python
-# Current placeholder (lines ~35-40):
-Age = np.random.randint(20, 91, size=num_samples)
-Heart_Failure_Risk = np.random.uniform(0, 1, size=num_samples)
-
-# Replace with your model:
-from transformers import AutoModel, AutoTokenizer
-# or
-import joblib
-model = joblib.load('your_model.pkl')
-
-# Then use:
-predictions = model.predict(Model_Features)
-```
-
-## 🌐 Deploy to Hugging Face Spaces
-
-1. **Create a new Space:**
-   - Go to https://huggingface.co/spaces
-   - Click "Create new Space"
-   - Choose "Gradio" as the SDK
-   - Name your Space
-
-2. **Upload files:**
-   - Upload `app.py`
-   - Upload `requirements.txt`
-   - Upload your model files (if any)
-
-3. **Your Space will automatically build and deploy!**
-
-## 📦 Project Structure
-
-```
-bioinformatics-space/
-├── app.py              # Main Gradio application
-├── requirements.txt    # Python dependencies
-└── README.md          # This file
-```
-
-## 🛠️ Technologies Used
-
-- **Gradio:** Web interface framework
-- **Pandas:** Data manipulation
-- **NumPy:** Numerical operations
-- **OpenPyXL:** Excel file support
-
-## 📝 Notes
-
-- Current predictions are **placeholder values** for demonstration
-- Replace the prediction logic with your trained model
-- Ensure your model accepts the same feature format as your input data
-- Consider adding data preprocessing steps if needed
-
-## 🤝 Contributing
-
-Feel free to customize this application for your specific bioinformatics use case!
-
-## 📄 License
-
-MIT License - Feel free to use and modify as needed.
+---
+title: Heart Failure Predictor
+emoji: 🫀
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+license: mit
+---