Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import pandas as pd
+import pickle
+import numpy as np
+
+def load_model():
+    """Load the trained sklearn model from disk"""
+    with open('model.pkl', 'rb') as f:
+        model = pickle.load(f)
+    return model
+
+def process_input(df):
+    """Process input dataframe to match model requirements"""
+    # Add any necessary preprocessing steps here
+    # For example: handling missing values, scaling, encoding
+    return df
+
+def predict_mortality(csv_file):
+    try:
+        # Read the CSV file
+        df = pd.read_csv(csv_file.name)
+        
+        # Load the model
+        model = load_model()
+        
+        # Process the input data
+        processed_df = process_input(df)
+        
+        # Make predictions
+        predictions = model.predict(processed_df)
+        probabilities = model.predict_proba(processed_df)
+        
+        # Add predictions to the original dataframe
+        df['Mortality_Risk'] = predictions
+        df['Death_Probability'] = probabilities[:, 1]  # Assuming 1 is the positive class
+        
+        # Format the results
+        results_df = df.copy()
+        results_df['Mortality_Risk'] = results_df['Mortality_Risk'].map({1: 'High Risk', 0: 'Low Risk'})
+        results_df['Death_Probability'] = results_df['Death_Probability'].round(3)
+        
+        return results_df
+    
+    except Exception as e:
+        return f"Error processing file: {str(e)}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_mortality,
+    inputs=gr.File(label="Upload Patient Data (CSV)"),
+    outputs=gr.Dataframe(label="Prediction Results"),
+    title="Patient Mortality Risk Prediction",
+    description="""
+    Upload a CSV file containing patient data to predict mortality risk.
+    The model will return the original data with two additional columns:
+    - Mortality_Risk: High Risk or Low Risk classification
+    - Death_Probability: Probability of death (0-1)
+    """,
+    examples=[["sample_data.csv"]],  # Add example files if available
+    cache_examples=True
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)