Upload 4 files

README.md

@@ -1,12 +1,109 @@
----
-title: Demo Cardio Safe
-emoji: 🔥
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 6.0.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧬 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.

app.py

@@ -0,0 +1,134 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+def predict_risk(file):
+    """
+    Process uploaded gene expression data and predict heart failure risk.
+    
+    Args:
+        file: Uploaded CSV or XLSX file
+        
+    Returns:
+        DataFrame with Sample IDs, Age, and Heart Failure Risk predictions
+    """
+    try:
+        # Read the uploaded file
+        if file.name.endswith('.csv'):
+            df = pd.read_csv(file.name)
+        elif file.name.endswith('.xlsx'):
+            df = pd.read_excel(file.name)
+        else:
+            return pd.DataFrame({"Error": ["Unsupported file format. Please upload .csv or .xlsx"]})
+        
+        # Step A: Extract the first column as Sample_IDs
+        # Handle both named and unnamed first columns
+        first_col_name = df.columns[0]
+        Sample_IDs = df.iloc[:, 0].values
+        
+        # Step B: Extract all other columns as Model_Features (the floats)
+        Model_Features = df.iloc[:, 1:].values
+        
+        # ---------------------------------------------------------
+        # REAL MODEL LOADING LOGIC (Add this part)
+        # ---------------------------------------------------------
+        import joblib
+        import os
+
+        # Load your model (ensure 'my_model.pkl' is in your Space's files)
+        # If your model is named differently, change this filename!
+        model_path = "my_model.pkl" 
+        
+        if os.path.exists(model_path):
+            model = joblib.load(model_path)
+            
+            # Run the prediction on the extracted features
+            # This assumes your model outputs a list of lists like [[Age, Risk], [Age, Risk]]
+            predictions = model.predict(Model_Features)
+            
+            # Split the results
+            # If your model outputs a different shape, you might need to adjust index [:, 0] or [:, 1]
+            Age = predictions[:, 0]
+            Heart_Failure_Risk = predictions[:, 1]
+            
+        else:
+            # Fallback if model file is missing (prevents crashing during setup)
+            return pd.DataFrame({"Error": ["Model file not found. Please upload 'my_model.pkl'."]})
+        
+        # ---------------------------------------------------------
+        
+        # Step 4: Combine results into a new DataFrame
+        results_df = pd.DataFrame({
+            'Sample_ID': Sample_IDs,
+            'Age': Age,
+            'Heart_Failure_Risk': np.round(Heart_Failure_Risk, 4)
+        })
+        
+        return results_df
+        
+    except Exception as e:
+        # Return error message as DataFrame
+        return pd.DataFrame({"Error": [f"An error occurred: {str(e)}"]})
+
+
+# Create Gradio Interface
+with gr.Blocks(title="Bioinformatics AI Agent - Heart Failure Risk Prediction") as demo:
+    gr.Markdown(
+        """
+        # 🧬 Bioinformatics AI Agent
+        ## Heart Failure Risk Prediction from Gene Expression Data
+        
+        Upload your gene expression data file (.csv or .xlsx) to predict heart failure risk.
+        
+        **Expected Format:**
+        - First column: Sample IDs (can be named or unnamed)
+        - Remaining columns: Gene expression values (numeric features)
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            file_input = gr.File(
+                label="Upload Gene Expression Data",
+                file_types=[".csv", ".xlsx"],
+                type="filepath"
+            )
+            predict_btn = gr.Button("Predict Risk", variant="primary")
+        
+        with gr.Column():
+            output_dataframe = gr.Dataframe(
+                label="Prediction Results",
+                headers=["Sample_ID", "Age", "Heart_Failure_Risk"],
+                datatype=["str", "number", "number"],
+                row_count=10
+            )
+    
+    gr.Markdown(
+        """
+        ### 📊 Output Columns:
+        - **Sample_ID**: Identifier from your input file
+        - **Age**: Predicted age (20-90 years)
+        - **Heart_Failure_Risk**: Risk score (0-1, where 1 is highest risk)
+        
+        ---
+        *Note: Current predictions are placeholder values. Replace the prediction logic in `app.py` with your trained model.*
+        """
+    )
+    
+    # Connect the button to the prediction function
+    predict_btn.click(
+        fn=predict_risk,
+        inputs=file_input,
+        outputs=output_dataframe
+    )
+    
+    # Also allow prediction on file upload
+    file_input.change(
+        fn=predict_risk,
+        inputs=file_input,
+        outputs=output_dataframe
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.44.0
+pandas==2.2.0
+openpyxl==3.1.2
+numpy==1.26.4
+scikit-learn==1.4.0
+joblib==1.3.2

sample_data.csv

@@ -0,0 +1,11 @@
+Unnamed: 0,Gene_BRCA1,Gene_TP53,Gene_EGFR,Gene_KRAS,Gene_MYC,Gene_PTEN,Gene_RB1,Gene_APC,Gene_VHL,Gene_CDH1
+Sample_001,0.234,1.567,0.891,2.345,0.678,1.234,0.456,1.890,0.345,1.123
+Sample_002,0.456,1.234,0.678,2.123,0.890,1.456,0.234,1.678,0.567,1.345
+Sample_003,0.789,1.890,0.345,2.567,0.123,1.678,0.890,1.456,0.789,1.567
+Sample_004,0.123,1.456,0.567,2.890,0.345,1.890,0.123,1.234,0.901,1.789
+Sample_005,0.567,1.678,0.789,2.234,0.567,1.123,0.567,1.890,0.234,1.901
+Sample_006,0.890,1.123,0.901,2.456,0.789,1.345,0.789,1.567,0.456,1.234
+Sample_007,0.234,1.345,0.234,2.678,0.901,1.567,0.901,1.345,0.678,1.456
+Sample_008,0.678,1.567,0.456,2.901,0.234,1.789,0.234,1.123,0.890,1.678
+Sample_009,0.901,1.789,0.678,2.345,0.456,1.901,0.456,1.901,0.123,1.890
+Sample_010,0.345,1.901,0.890,2.567,0.678,1.234,0.678,1.678,0.345,1.123