Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import sklearn
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans
+from sklearn.ensemble import RandomForestClassifier
+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Create sample training data
+def create_sample_data(n_samples=100):
+    np.random.seed(42)
+    
+    sample_data = pd.DataFrame({
+        'genetic_marker_1': np.random.uniform(0, 1, n_samples),
+        'genetic_marker_2': np.random.uniform(0, 1, n_samples),
+        'biomarker_1': np.random.uniform(4.0, 14.0, n_samples),
+        'biomarker_2': np.random.uniform(70, 400, n_samples),
+        'age': np.random.uniform(18, 90, n_samples),
+        'bmi': np.random.uniform(16, 45, n_samples)
+    })
+    
+    # Create treatment response based on data patterns
+    sample_data['treatment_response'] = np.where(
+        (sample_data['genetic_marker_1'] > 0.7) & 
+        (sample_data['biomarker_1'] > 6.5), 1, 0
+    )
+    
+    return sample_data
+
+# Initialize and train models
+def initialize_models(data):
+    X = data.drop('treatment_response', axis=1)
+    y = data['treatment_response']
+    
+    # Train KMeans model
+    kmeans = KMeans(n_clusters=4, random_state=42)
+    kmeans.fit(X)
+    
+    # Train RandomForest model
+    rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
+    rf_model.fit(X, y)
+    
+    return kmeans, rf_model
+
+def process_patient(genetic_marker_1, genetic_marker_2, biomarker_1, biomarker_2, age, bmi):
+    try:
+        # Create patient data
+        patient_data = pd.DataFrame({
+            'genetic_marker_1': [float(genetic_marker_1)],
+            'genetic_marker_2': [float(genetic_marker_2)],
+            'biomarker_1': [float(biomarker_1)],
+            'biomarker_2': [float(biomarker_2)],
+            'age': [float(age)],
+            'bmi': [float(bmi)]
+        })
+        
+        # Get predictions
+        profile = PROFILE_MODEL.predict(patient_data)[0]
+        treatment_prob = TREATMENT_MODEL.predict_proba(patient_data)[0]
+        
+        # Define treatment recommendations
+        treatment_recommendations = {
+            0: "Standard medication with regular monitoring",
+            1: "Intensive lifestyle intervention with medication",
+            2: "Alternative medication combination",
+            3: "Close monitoring with adjusted medication doses"
+        }
+        
+        # Risk assessment
+        risk_level = "High Risk" if treatment_prob[1] > 0.7 else "Moderate Risk" if treatment_prob[1] > 0.3 else "Low Risk"
+        
+        # Create visualization
+        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+        
+        # Marker profile plot
+        values = [genetic_marker_1, genetic_marker_2, biomarker_1/14, biomarker_2/400]  # Normalize values
+        labels = ['Genetic 1', 'Genetic 2', 'HbA1c/14', 'Glucose/400']
+        sns.barplot(x=labels, y=values, ax=ax1)
+        ax1.set_title('Patient Markers Profile (Normalized)')
+        ax1.set_ylim(0, 1)
+        
+        # Risk assessment plot
+        risk_values = [treatment_prob[0], treatment_prob[1]]
+        risk_labels = ['Low Risk', 'High Risk']
+        sns.barplot(x=risk_labels, y=risk_values, ax=ax2)
+        ax2.set_title('Risk Assessment')
+        ax2.set_ylim(0, 1)
+        
+        plt.tight_layout()
+        
+        # Prepare detailed report
+        report = f"""
+Patient Risk Assessment Report
+-----------------------------
+Risk Level: {risk_level}
+Profile Group: {profile}
+
+Recommended Treatment Plan:
+{treatment_recommendations[profile]}
+
+Key Metrics Analysis:
+- Genetic Risk Score: {(genetic_marker_1 + genetic_marker_2)/2:.2f}/1.00
+- HbA1c Level: {biomarker_1:.1f} (Normal: 4.0-5.7, Pre-diabetes: 5.7-6.4, Diabetes: >6.5)
+- Blood Glucose: {biomarker_2:.0f} mg/dL (Normal: <100, Pre-diabetes: 100-125, Diabetes: >126)
+- BMI Status: {
+    'Underweight' if bmi < 18.5 
+    else 'Normal' if bmi < 25 
+    else 'Overweight' if bmi < 30 
+    else 'Obese'} ({bmi:.1f})
+
+Confidence Score: {max(treatment_prob):.2%}
+        """
+        
+        return report, fig
+        
+    except Exception as e:
+        return f"Error processing data: {str(e)}", None
+
+# Initialize Gradio interface with sample data
+sample_data = create_sample_data()
+PROFILE_MODEL, TREATMENT_MODEL = initialize_models(sample_data)
+
+# Create Gradio interface
+with gr.Blocks(title="Genetic and Biomarker Analysis System") as demo:
+    gr.Markdown("# Personalized Diabetes Treatment Recommendation System")
+    gr.Markdown("Enter patient genetic markers and biomarkers to receive personalized treatment recommendations.")
+    
+    with gr.Row():
+        with gr.Column():
+            genetic_marker_1 = gr.Number(label="Genetic Marker 1 (0-1)", value=0.85)
+            genetic_marker_2 = gr.Number(label="Genetic Marker 2 (0-1)", value=0.42)
+            biomarker_1 = gr.Number(label="HbA1c Level (4.0-14.0)", value=6.8)
+            biomarker_2 = gr.Number(label="Fasting Glucose (70-400)", value=145)
+            age = gr.Number(label="Age (18-90)", value=45)
+            bmi = gr.Number(label="BMI (16-45)", value=28.5)
+            submit_btn = gr.Button("Generate Recommendations")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Analysis Report", lines=10)
+            output_plot = gr.Plot(label="Patient Profile Visualization")
+    
+    submit_btn.click(
+        fn=process_patient,
+        inputs=[genetic_marker_1, genetic_marker_2, biomarker_1, biomarker_2, age, bmi],
+        outputs=[output_text, output_plot]
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(share=True)