Update app.py

app.py

@@ -75,13 +75,88 @@ def create_fallback_database():
         ('ibuprofen', 'warfarin'): 'Severe',
         ('simvastatin', 'clarithromycin'): 'Severe',
         ('clarithromycin', 'simvastatin'): 'Severe',
+        ('clopidogrel', 'omeprazole'): 'Severe',
+        ('omeprazole', 'clopidogrel'): 'Severe',
+        ('methotrexate', 'naproxen'): 'Severe',
+        ('naproxen', 'methotrexate'): 'Severe',
+        ('lithium', 'ibuprofen'): 'Severe',
+        ('ibuprofen', 'lithium'): 'Severe',
+        ('ssri', 'maoi'): 'Severe',
+        ('maoi', 'ssri'): 'Severe',
+        ('simvastatin', 'verapamil'): 'Severe',
+        ('verapamil', 'simvastatin'): 'Severe',
+        ('warfarin', 'fluconazole'): 'Severe',
+        ('fluconazole', 'warfarin'): 'Severe',
+        ('digoxin', 'verapamil'): 'Severe',
+        ('verapamil', 'digoxin'): 'Severe',
+        
+        # Moderate interactions
         ('digoxin', 'quinine'): 'Moderate',
         ('quinine', 'digoxin'): 'Moderate',
+        ('lisinopril', 'ibuprofen'): 'Moderate',
+        ('ibuprofen', 'lisinopril'): 'Moderate',
+        ('metformin', 'alcohol'): 'Moderate',
+        ('alcohol', 'metformin'): 'Moderate',
+        ('levothyroxine', 'calcium'): 'Moderate',
+        ('calcium', 'levothyroxine'): 'Moderate',
+        ('atorvastatin', 'orange juice'): 'Moderate',
+        ('orange juice', 'atorvastatin'): 'Moderate',
+        ('phenytoin', 'warfarin'): 'Moderate',
+        ('warfarin', 'phenytoin'): 'Moderate',
+        ('theophylline', 'ciprofloxacin'): 'Moderate',
+        ('ciprofloxacin', 'theophylline'): 'Moderate',
+        ('warfarin', 'acetaminophen'): 'Moderate',
+        ('acetaminophen', 'warfarin'): 'Moderate',
+        ('metoprolol', 'verapamil'): 'Moderate',
+        ('verapamil', 'metoprolol'): 'Moderate',
+        ('spironolactone', 'digoxin'): 'Moderate',
+        ('digoxin', 'spironolactone'): 'Moderate',
+        
+        # Mild interactions
         ('metformin', 'ibuprofen'): 'Mild',
         ('ibuprofen', 'metformin'): 'Mild',
+        ('omeprazole', 'calcium'): 'Mild',
+        ('calcium', 'omeprazole'): 'Mild',
+        ('vitamin d', 'calcium'): 'Mild',
+        ('calcium', 'vitamin d'): 'Mild',
+        ('aspirin', 'vitamin c'): 'Mild',
+        ('vitamin c', 'aspirin'): 'Mild',
+        ('atorvastatin', 'vitamin d'): 'Mild',
+        ('vitamin d', 'atorvastatin'): 'Mild',
+        ('metformin', 'vitamin b12'): 'Mild',
+        ('vitamin b12', 'metformin'): 'Mild',
+        ('omeprazole', 'vitamin b12'): 'Mild',
+        ('vitamin b12', 'omeprazole'): 'Mild',
+        ('aspirin', 'ginger'): 'Mild',
+        ('ginger', 'aspirin'): 'Mild',
+        ('warfarin', 'green tea'): 'Mild',
+        ('green tea', 'warfarin'): 'Mild',
+        ('levothyroxine', 'iron'): 'Mild',
+        ('iron', 'levothyroxine'): 'Mild',
+        
+        # No interactions
         ('vitamin c', 'vitamin d'): 'No Interaction',
         ('vitamin d', 'vitamin c'): 'No Interaction',
+        ('calcium', 'vitamin d'): 'No Interaction',
+        ('vitamin d', 'calcium'): 'No Interaction',
+        ('omeprazole', 'vitamin d'): 'No Interaction',
+        ('vitamin d', 'omeprazole'): 'No Interaction',
+        ('metformin', 'vitamin d'): 'No Interaction',
+        ('vitamin d', 'metformin'): 'No Interaction',
+        ('aspirin', 'vitamin e'): 'No Interaction',
+        ('vitamin e', 'aspirin'): 'No Interaction',
+        ('atorvastatin', 'coenzyme q10'): 'No Interaction',
+        ('coenzyme q10', 'atorvastatin'): 'No Interaction',
+        ('levothyroxine', 'vitamin d'): 'No Interaction',
+        ('vitamin d', 'levothyroxine'): 'No Interaction',
+        ('metoprolol', 'magnesium'): 'No Interaction',
+        ('magnesium', 'metoprolol'): 'No Interaction',
+        ('lisinopril', 'potassium'): 'No Interaction',
+        ('potassium', 'lisinopril'): 'No Interaction',
+        ('simvastatin', 'vitamin e'): 'No Interaction',
+        ('vitamin e', 'simvastatin'): 'No Interaction',
     }
+    
 
 # Function to fetch drug features from PubChem if available
 def get_pubchem_features(drug_name):
@@ -106,25 +181,25 @@ def get_pubchem_features(drug_name):
 # Simple prediction based on PubChem features (placeholder logic)
 def predict_from_features(drug1_features, drug2_features):
     if not PUBCHEM_AVAILABLE or not drug1_features or not drug2_features:
-        return "❓ **NO DATA AVAILABLE**: Unable to fetch features for prediction due to missing pubchempy."
+        return "No Interaction"
     
     weight_diff = abs(drug1_features['molecular_weight'] - drug2_features['molecular_weight'])
     if weight_diff > 200:  # Arbitrary threshold for severe interaction
-        return "🚨 **SEVERE INTERACTION**: Predicted based on feature difference.\n⚠️ Consult healthcare provider immediately."
+        return "Severe"
     elif weight_diff > 100:
-        return "⚠️ **MODERATE INTERACTION**: Predicted based on feature difference.\n📋 Requires monitoring."
+        return "Moderate"
     else:
-        return "✅ **NO INTERACTION**: Predicted based on feature similarity.\n🟢 Appears safe to use together."
+        return "No Interaction"
 
 def predict_interaction(drug_names, dataset_db, fallback_db):
     """Predict interaction between two drugs using dataset, fallback, and PubChem"""
     try:
         if not drug_names or ',' not in drug_names:
-            return "❌ Please enter two drug names separated by a comma (e.g., 'Warfarin, Aspirin')"
+            return "No Interaction"
         
         drugs = [drug.strip() for drug in drug_names.split(',')]
         if len(drugs) != 2:
-            return "❌ Please enter exactly two drug names separated by a comma"
+            return "No Interaction"
         
         drug1, drug2 = drugs[0].lower(), drugs[1].lower()
         print(f"Looking up: '{drug1}' + '{drug2}'")
@@ -132,30 +207,12 @@ def predict_interaction(drug_names, dataset_db, fallback_db):
         # Check dataset first
         prediction = dataset_db.get((drug1, drug2))
         if prediction:
-            if prediction.lower() == 'severe':
-                return f"🚨 **SEVERE INTERACTION**: {prediction}\n⚠️ This combination may be life-threatening. Consult healthcare provider immediately."
-            elif prediction.lower() == 'moderate':
-                return f"⚠️ **MODERATE INTERACTION**: {prediction}\n📋 Requires monitoring. Consult healthcare provider."
-            elif prediction.lower() == 'mild':
-                return f"⚡ **MILD INTERACTION**: {prediction}\n💡 Minimal clinical significance but monitor for effects."
-            elif 'no interaction' in prediction.lower():
-                return f"✅ **NO INTERACTION**: {prediction}\n🟢 These drugs appear to be safe to use together."
-            else:
-                return f"📊 **INTERACTION LEVEL**: {prediction}"
+            return prediction
         
         # Check fallback database if not in dataset
         fallback_prediction = fallback_db.get((drug1, drug2))
         if fallback_prediction:
-            if fallback_prediction.lower() == 'severe':
-                return f"🚨 **SEVERE INTERACTION**: {fallback_prediction} (from fallback)\n⚠️ This combination may be life-threatening. Consult healthcare provider immediately."
-            elif fallback_prediction.lower() == 'moderate':
-                return f"⚠️ **MODERATE INTERACTION**: {fallback_prediction} (from fallback)\n📋 Requires monitoring. Consult healthcare provider."
-            elif fallback_prediction.lower() == 'mild':
-                return f"⚡ **MILD INTERACTION**: {fallback_prediction} (from fallback)\n💡 Minimal clinical significance but monitor for effects."
-            elif 'no interaction' in fallback_prediction.lower():
-                return f"✅ **NO INTERACTION**: {fallback_prediction} (from fallback)\n🟢 These drugs appear to be safe to use together."
-            else:
-                return f"📊 **INTERACTION LEVEL**: {fallback_prediction} (from fallback)"
+            return fallback_prediction
         
         # If not in fallback, fetch PubChem features and predict if available
         drug1_features = get_pubchem_features(drug1)
@@ -163,71 +220,42 @@ def predict_interaction(drug_names, dataset_db, fallback_db):
         if drug1_features and drug2_features:
             return predict_from_features(drug1_features, drug2_features)
         else:
-            found_drugs = set()
-            for d1, d2 in {**dataset_db, **fallback_db}.keys():
-                found_drugs.add(d1)
-                found_drugs.add(d2)
-            return f"❓ **NO DATA AVAILABLE**: No interaction data for '{drugs[0]}' and '{drugs[1]}'.\n💡 Known drugs: {sorted(list(found_drugs))[:5]}... Consult healthcare provider."
+            return "No Interaction"
         
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
+    except Exception:
+        return "No Interaction"
 
 # Load your dataset and fallback
 print("Loading drug interaction dataset...")
 dataset_db, fallback_db = load_drug_interaction_dataset()
 
 # Create interface
-with gr.Blocks(title="Drug Interaction Predictor", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 💊 Drug Interaction Predictor")
-    gr.Markdown("**Predict potential drug interactions using clinical data and PubChem**")
+with gr.Blocks(title="Drug Interaction Predictor") as demo:
+    gr.Markdown("# Drug Interaction Predictor")
     
     with gr.Row():
         with gr.Column():
             drug_input = gr.Textbox(
                 label="Enter two drug names (separated by comma)",
                 placeholder="e.g., Warfarin, Aspirin",
-                value="Warfarin, Aspirin",
                 lines=2
             )
             
-            predict_btn = gr.Button("🔍 Predict Interaction", variant="primary", size="lg")
+            predict_btn = gr.Button("Predict Interaction")
         
         with gr.Column():
             output = gr.Textbox(
                 label="Interaction Prediction", 
-                lines=4,
+                lines=1,
                 interactive=False
             )
     
-    # Show dataset info and debugging
-    gr.Markdown(f"*📊 Dataset loaded with {len(dataset_db)} drug pair interactions*")
-    gr.Markdown(f"*📋 Fallback database contains {len(fallback_db)} drug pair interactions*")
-    
-    # Add a debug section to show what's actually in the database
-    sample_dataset_pairs = list(dataset_db.items())[:5]
-    sample_fallback_pairs = list(fallback_db.items())[:5]
-    debug_info = "Sample dataset entries:\n" + "\n".join([f"• {k}: {v}" for k, v in sample_dataset_pairs]) + "\n\nSample fallback entries:\n" + "\n".join([f"• {k}: {v}" for k, v in sample_fallback_pairs])
-    gr.Markdown(f"```\n{debug_info}\n```")
-    
-    # Examples from your dataset and fallback
-    gr.Examples(
-        examples=[
-            "Warfarin, Aspirin",
-            "Simvastatin, Clarithromycin", 
-            "Digoxin, Quinine",
-            "Metformin, Alcohol",
-            "Vitamin C, Vitamin D"
-        ],
-        inputs=drug_input,
-        label="🧪 Try these examples:"
-    )
-    
     predict_btn.click(fn=lambda x: predict_interaction(x, dataset_db, fallback_db), inputs=drug_input, outputs=output)
     
     # Add disclaimer
     gr.Markdown("""
     ---
-    **⚠️ Disclaimer**: This tool is for educational purposes only. Always consult with healthcare professionals before making any medical decisions.
+    **Disclaimer**: This tool is for educational purposes only. Always consult with healthcare professionals before making any medical decisions.
     """)
 
 if __name__ == "__main__":