Update app.py

app.py

@@ -2,9 +2,9 @@ import gradio as gr
 import pandas as pd
 import os
 
-# Load your labeled dataset with flexible column names
+# Load your labeled dataset with exact column names
 def load_drug_interaction_dataset():
-    """Load your labeled drug interaction dataset with flexible column names"""
+    """Load your labeled drug interaction dataset with exact column names"""
     try:
         # Your exact dataset filename
         dataset_path = 'merged_cleaned_dataset.csv'
@@ -16,43 +16,28 @@ def load_drug_interaction_dataset():
             print(f"Dataset file {dataset_path} not found!")
             return create_fallback_database()
         
-        # Load the dataset
+        # Load the dataset with your exact column names
         print(f"Loading dataset from: {dataset_path}")
         df = pd.read_csv(dataset_path)
         print(f"Dataset columns: {df.columns.tolist()}")
         print(f"Dataset shape: {df.shape}")
         print(f"First few rows:\n{df.head()}")
         
-        # Attempt to detect column names (flexible mapping)
-        possible_drug1_cols = ['Drug 1_normalized', 'Drug1', 'drug_1']
-        possible_drug2_cols = ['Drug 2_normalized', 'Drug2', 'drug_2']
-        possible_severity_cols = ['severity', 'Severity', 'SEVERITY']
-        
-        drug1_col = next((col for col in possible_drug1_cols if col in df.columns), None)
-        drug2_col = next((col for col in possible_drug2_cols if col in df.columns), None)
-        severity_col = next((col for col in possible_severity_cols if col in df.columns), None)
-        
-        if not all([drug1_col, drug2_col, severity_col]):
-            print(f"Required columns not found. Detected: drug1={drug1_col}, drug2={drug2_col}, severity={severity_col}")
-            return create_fallback_database()
-        
-        print(f"Using columns: drug1={drug1_col}, drug2={drug2_col}, severity={severity_col}")
-        
-        # Create interaction dictionary
+        # Create interaction dictionary using your exact column names
         interaction_db = {}
         count = 0
         
-        for index, row in df.iterrows():
+        for _, row in df.iterrows():
             try:
-                drug1 = str(row[drug1_col]).lower().strip()
-                drug2 = str(row[drug2_col]).lower().strip()
-                severity = str(row[severity_col]).strip()
+                # Use your exact column names
+                drug1 = str(row['Drug 1_normalized']).lower().strip()
+                drug2 = str(row['Drug 2_normalized']).lower().strip()
+                severity = str(row['severity']).strip()
                 
-                # Skip empty or invalid entries
+                # Skip empty entries or invalid data
                 if (not all([drug1, drug2, severity]) or 
                     drug1 == 'nan' or drug2 == 'nan' or 
                     severity == 'nan' or severity.lower() == 'none'):
-                    print(f"Skipping invalid row {index}: {drug1}, {drug2}, {severity}")
                     continue
                 
                 # Clean up severity labels
@@ -60,19 +45,13 @@ def load_drug_interaction_dataset():
                 if severity == 'No interaction':
                     severity = 'No Interaction'
                 
-                # Add both orders to the dictionary
+                # Add both orders to the dictionary - STORE ONLY THE SEVERITY STRING
                 interaction_db[(drug1, drug2)] = severity
-                interaction_db[(drug2, drug1)] = severity
+                interaction_db[(drug2, drug1)] = severity  # Add reverse order
                 count += 1
-                if count % 100 == 0:  # Log progress
-                    print(f"Processed {count} interactions")
-                
-                # Verify known pair
-                if drug1 == 'warfarin' and drug2 == 'aspirin':
-                    print(f"Found Warfarin, Aspirin at row {index} with severity: {severity}")
                 
             except Exception as e:
-                print(f"Error processing row {index}: {e}")
+                print(f"Error processing row {_}: {e}")
                 continue
         
         print(f"✅ Successfully loaded {count} drug interactions from dataset")
@@ -84,18 +63,96 @@ def load_drug_interaction_dataset():
         return create_fallback_database()
 
 def create_fallback_database():
-    """Fallback database if dataset loading fails"""
+    """Fallback database if dataset loading fails - FIXED STRUCTURE"""
     print("Using fallback database")
     return {
+        # Severe interactions (Life-threatening) - STORE ONLY SEVERITY STRINGS
         ('warfarin', 'aspirin'): 'Severe',
+        ('aspirin', 'warfarin'): 'Severe',
         ('warfarin', 'ibuprofen'): 'Severe',
+        ('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 (Requires monitoring)
         ('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 (Minimal clinical significance)
         ('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 (Clinically safe)
         ('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',
     }
 
 # Load your dataset
@@ -106,12 +163,12 @@ def predict_interaction(drug_names):
     """Predict interaction between two drugs using your labeled dataset"""
     try:
         if not drug_names or ',' not in drug_names:
-            return "Please enter two drug names separated by a comma"
+            return "❌ Please enter two drug names separated by a comma (e.g., 'Warfarin, Aspirin')"
         
         # Split the input
         drugs = [drug.strip() for drug in drug_names.split(',')]
         if len(drugs) != 2:
-            return "Please enter exactly two drug names"
+            return "❌ Please enter exactly two drug names separated by a comma"
         
         drug1, drug2 = drugs[0].lower(), drugs[1].lower()
         print(f"Looking up: '{drug1}' + '{drug2}'")
@@ -120,46 +177,88 @@ def predict_interaction(drug_names):
         prediction = interaction_db.get((drug1, drug2))
         
         if prediction:
-            return f"Predicted Interaction: {prediction}"
+            # Add appropriate emoji and formatting based on severity
+            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}"
         else:
-            # Log all available keys for debugging
-            print(f"Keys in interaction_db: {list(interaction_db.keys())[:10]}... (total {len(interaction_db)})")
-            return f"Predicted Interaction: Moderate"  # Default to Moderate if not found
+            # Try to find similar drugs for debugging
+            found_drugs = set()
+            for d1, d2 in interaction_db.keys():
+                found_drugs.add(d1)
+                found_drugs.add(d2)
+            
+            print(f"Not found. Available drugs: {sorted(list(found_drugs))[:20]}...")
+            
+            # Check if either drug exists in the database
+            drug1_exists = any(d1 == drug1 or d2 == drug1 for d1, d2 in interaction_db.keys())
+            drug2_exists = any(d1 == drug2 or d2 == drug2 for d1, d2 in interaction_db.keys())
+            
+            if not drug1_exists and not drug2_exists:
+                return f"❓ **UNKNOWN DRUGS**: Neither '{drugs[0]}' nor '{drugs[1]}' found in database.\n💡 Try checking spelling or use generic names."
+            elif not drug1_exists:
+                return f"❓ **UNKNOWN DRUG**: '{drugs[0]}' not found in database.\n💡 Try checking spelling or use generic name."
+            elif not drug2_exists:
+                return f"❓ **UNKNOWN DRUG**: '{drugs[1]}' not found in database.\n💡 Try checking spelling or use generic name."
+            else:
+                return f"❓ **NO DATA AVAILABLE**: No interaction data found for '{drugs[0]}' and '{drugs[1]}'.\n💡 Consult healthcare provider for guidance."
         
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"❌ Error: {str(e)}"
 
 # Create interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Drug Interaction Predictor")
-    gr.Markdown("**Using your merged_cleaned_dataset.csv**")
-    
-    drug_input = gr.Textbox(
-        label="Enter drug names",
-        placeholder="e.g., Warfarin, Aspirin",
-        value="Warfarin, Aspirin"
-    )
+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**")
     
-    predict_btn = gr.Button("Predict")
-    output = gr.Textbox(label="Prediction")
+    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")
+        
+        with gr.Column():
+            output = gr.Textbox(
+                label="Interaction Prediction", 
+                lines=4,
+                interactive=False
+            )
     
     # Show dataset info
-    gr.Markdown(f"*Dataset: merged_cleaned_dataset.csv*")
-    gr.Markdown(f"*Loaded {len(interaction_db)//2} interactions*")
+    gr.Markdown(f"*📊 Dataset loaded with {len(interaction_db)} drug pair interactions*")
     
     # Examples from your dataset
     gr.Examples(
         examples=[
-            "warfarin, aspirin",
-            "simvastatin, clarithromycin",
-            "digoxin, quinine",
-            "metformin, alcohol"
+            "Warfarin, Aspirin",
+            "Simvastatin, Clarithromycin", 
+            "Digoxin, Quinine",
+            "Metformin, Alcohol",
+            "Vitamin C, Vitamin D"
         ],
         inputs=drug_input,
-        label="Try these examples:"
+        label="🧪 Try these examples:"
     )
     
     predict_btn.click(predict_interaction, drug_input, output)
+    
+    # Add disclaimer
+    gr.Markdown("""
+    ---
+    **⚠️ Disclaimer**: This tool is for educational purposes only. Always consult with healthcare professionals before making any medical decisions.
+    """)
 
 if __name__ == "__main__":
     demo.launch()