Create app.py

app.py

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+# First, install required packages
+#!pip install gradio pandas numpy scikit-learn
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import json
+from datetime import datetime, timedelta
+
+class SimplePrecisionMedicineSystem:
+    def __init__(self):
+        self.scaler = StandardScaler()
+        
+    def calculate_risk_score(self, genetic_score, lifestyle_score, age, symptom_severity):
+        """Calculate overall risk score"""
+        try:
+            # Normalize age to 0-1 range (assuming max age of 100)
+            age_normalized = age / 100.0
+            
+            # Weight factors (can be adjusted)
+            weights = {
+                'genetic': 0.4,
+                'lifestyle': 0.3,
+                'age': 0.2,
+                'symptoms': 0.1
+            }
+            
+            # Calculate weighted risk score
+            risk_score = (
+                genetic_score * weights['genetic'] +
+                (1 - lifestyle_score) * weights['lifestyle'] +  # Invert lifestyle score
+                age_normalized * weights['age'] +
+                (symptom_severity / 10) * weights['symptoms']  # Normalize to 0-1
+            )
+            
+            return round(risk_score, 2)
+        except Exception as e:
+            print(f"Error in risk calculation: {str(e)}")
+            return 0.5  # Return middle value if error occurs
+
+    def get_treatment_recommendation(self, risk_score, current_medication, symptom_severity):
+        """Generate treatment recommendations based on risk score and symptoms"""
+        try:
+            base_recommendations = []
+            
+            # Risk-based recommendations
+            if risk_score < 0.3:
+                base_recommendations.append("Low risk profile - Continue monitoring")
+            elif risk_score < 0.6:
+                base_recommendations.append("Moderate risk - Regular check-ups recommended")
+            else:
+                base_recommendations.append("High risk - Intensive monitoring required")
+
+            # Symptom-based recommendations
+            if symptom_severity <= 3:
+                base_recommendations.append("Mild symptoms - Maintain current treatment")
+            elif symptom_severity <= 7:
+                base_recommendations.append("Moderate symptoms - Consider treatment adjustment")
+            else:
+                base_recommendations.append("Severe symptoms - Immediate medical review required")
+
+            # Medication-based recommendations
+            if current_medication.lower().strip() in ['none', 'no', '']:
+                if risk_score > 0.5:
+                    base_recommendations.append("Consider initiating preventive treatment")
+            else:
+                base_recommendations.append("Review current medications at next appointment")
+
+            return base_recommendations
+        except Exception as e:
+            print(f"Error in treatment recommendation: {str(e)}")
+            return ["Unable to generate recommendations due to error"]
+
+    def generate_report(self, patient_data):
+        """Generate patient report"""
+        try:
+            # Calculate risk score
+            risk_score = self.calculate_risk_score(
+                patient_data['genetic_score'],
+                patient_data['lifestyle_score'],
+                patient_data['age'],
+                patient_data['symptom_severity']
+            )
+            
+            # Get treatment recommendations
+            recommendations = self.get_treatment_recommendation(
+                risk_score,
+                patient_data['current_medication'],
+                patient_data['symptom_severity']
+            )
+            
+            # Create report
+            report = {
+                "Patient Information": {
+                    "Age": patient_data['age'],
+                    "Gender": patient_data['gender'],
+                    "Current Medications": patient_data['current_medication']
+                },
+                "Risk Assessment": {
+                    "Overall Risk Score": risk_score,
+                    "Risk Category": "High" if risk_score > 0.6 else "Moderate" if risk_score > 0.3 else "Low",
+                    "Genetic Risk": patient_data['genetic_score'],
+                    "Lifestyle Score": patient_data['lifestyle_score']
+                },
+                "Current Status": {
+                    "Symptom Severity": f"{patient_data['symptom_severity']}/10",
+                },
+                "Recommendations": recommendations,
+                "Next Steps": {
+                    "Follow-up": "Within 1 week" if risk_score > 0.6 else "Within 1 month" if risk_score > 0.3 else "Within 3 months",
+                    "Monitoring": "Daily" if patient_data['symptom_severity'] > 7 else "Weekly" if patient_data['symptom_severity'] > 4 else "Monthly"
+                },
+                "Report Generated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+            
+            return json.dumps(report, indent=2)
+        except Exception as e:
+            print(f"Error in report generation: {str(e)}")
+            return json.dumps({"error": "Failed to generate report", "message": str(e)})
+
+def create_gradio_interface():
+    """Create Gradio interface"""
+    system = SimplePrecisionMedicineSystem()
+    
+    def process_patient(age, gender, genetic_score, lifestyle_score, current_medication, symptom_severity):
+        try:
+            # Create patient data dictionary
+            patient_data = {
+                'age': age,
+                'gender': gender,
+                'genetic_score': genetic_score,
+                'lifestyle_score': lifestyle_score,
+                'current_medication': current_medication,
+                'symptom_severity': symptom_severity
+            }
+            
+            # Generate report
+            return system.generate_report(patient_data)
+            
+        except Exception as e:
+            return json.dumps({
+                "error": "An error occurred while processing the patient data",
+                "details": str(e)
+            }, indent=2)
+
+    # Create interface
+    iface = gr.Interface(
+        fn=process_patient,
+        inputs=[
+            gr.Number(label="Age", minimum=0, maximum=120),
+            gr.Dropdown(choices=["Male", "Female", "Other"], label="Gender"),
+            gr.Slider(minimum=0, maximum=1, step=0.1, label="Genetic Risk Score (0-1)"),
+            gr.Slider(minimum=0, maximum=1, step=0.1, label="Lifestyle Score (0-1)"),
+            gr.Textbox(label="Current Medications (comma-separated or 'None')"),
+            gr.Slider(minimum=0, maximum=10, step=1, label="Symptom Severity (0-10)")
+        ],
+        outputs=gr.JSON(label="Medical Report"),
+        title="Precision Medicine System",
+        description="Enter patient information to generate personalized medical recommendations",
+        examples=[
+            [45, "Female", 0.6, 0.7, "Metformin 500mg, Lisinopril 10mg", 4],
+            [72, "Male", 0.8, 0.4, "Warfarin 5mg, Atorvastatin 40mg", 7],
+            [28, "Other", 0.2, 0.9, "None", 2]
+        ]
+    )
+    return iface
+
+# Launch the interface
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch()