Update app.py

app.py

@@ -1,16 +1,15 @@
 import gradio as gr
 import requests
 import time
-from inference import DDIPredictor
 
-# Initialize predictor with your Hugging Face repository
+# Try to import the predictor
 try:
-    predictor = DDIPredictor("Fredaaaaaa/drug_interaction_severity")
-    MODEL_LOADED = True
-    print("✅ Model loaded successfully from Fredaaaaaa/drug_interaction_severity")
-except Exception as e:
-    print(f"❌ Model loading failed: {e}")
+    from inference import predictor, MODEL_LOADED
+    print("✅ Inference module imported successfully")
+except ImportError as e:
+    print(f"❌ Failed to import inference: {e}")
     MODEL_LOADED = False
+    predictor = None
 
 def fetch_pubchem_data(drug_name):
     """Fetch drug data from PubChem by name"""
@@ -18,188 +17,108 @@ def fetch_pubchem_data(drug_name):
         if not drug_name or not drug_name.strip():
             return None, "Please enter a valid drug name"
         
-        # First, search for the compound ID
+        # Search for compound ID
         search_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{drug_name}/cids/JSON"
-        search_response = requests.get(search_url, timeout=15)
+        search_response = requests.get(search_url, timeout=10)
         
         if search_response.status_code != 200:
             return None, f"Drug '{drug_name}' not found in PubChem"
         
         cid = search_response.json()['IdentifierList']['CID'][0]
         
-        # Fetch compound data
-        properties = ['CanonicalSMILES', 'MolecularWeight', 'IUPACName', 'XLogP', 'TPSA']
-        compound_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/property/{','.join(properties)}/JSON"
-        compound_response = requests.get(compound_url, timeout=15)
+        # Fetch basic compound data
+        compound_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/property/CanonicalSMILES,MolecularWeight,IUPACName/JSON"
+        compound_response = requests.get(compound_url, timeout=10)
         
         if compound_response.status_code != 200:
-            return None, "Failed to fetch compound data from PubChem"
+            return None, "Failed to fetch compound data"
         
         data = compound_response.json()['PropertyTable']['Properties'][0]
         data['CID'] = cid
         
         return data, None
         
-    except requests.exceptions.Timeout:
-        return None, "PubChem API timeout - please try again"
-    except requests.exceptions.RequestException:
-        return None, "Network error - please check your connection"
     except Exception as e:
-        return None, f"Error fetching data: {str(e)}"
+        return None, f"Error: {str(e)}"
 
 def generate_interaction_description(drug1_data, drug2_data):
-    """Generate clinical interaction description based on molecular properties"""
+    """Generate interaction description"""
     try:
         descriptions = []
         
-        # Molecular weight analysis
         mw1 = drug1_data.get('MolecularWeight', 0)
         mw2 = drug2_data.get('MolecularWeight', 0)
         if mw1 and mw2:
             mw_diff = abs(mw1 - mw2)
             if mw_diff > 300:
-                descriptions.append("Significant molecular size difference may affect metabolic pathways")
-        
-        # Lipophilicity analysis
-        logp1 = drug1_data.get('XLogP', 0)
-        logp2 = drug2_data.get('XLogP', 0)
-        if logp1 is not None and logp2 is not None:
-            logp_diff = abs(logp1 - logp2)
-            if logp_diff > 2:
-                descriptions.append("Differing lipophilicity may affect membrane permeability")
-        
-        # Polar surface area analysis
-        tpsa1 = drug1_data.get('TPSA', 0)
-        tpsa2 = drug2_data.get('TPSA', 0)
-        if tpsa1 and tpsa2:
-            tpsa_diff = abs(tpsa1 - tpsa2)
-            if tpsa_diff > 80:
-                descriptions.append("Varying polar surface areas suggest different absorption characteristics")
+                descriptions.append("Significant molecular size difference")
         
         if not descriptions:
-            descriptions.append("Potential pharmacokinetic interaction based on molecular properties")
+            descriptions.append("Potential drug interaction")
         
         return ". ".join(descriptions) + ". Clinical evaluation recommended."
-        
-    except Exception:
-        return "Potential drug interaction requiring clinical assessment."
-
-def check_drugbank_interaction(drug1_name, drug2_name):
-    """Mock function for DrugBank interaction check"""
-    drug1_clean = drug1_name.lower().strip()
-    drug2_clean = drug2_name.lower().strip()
-    
-    known_interactions = {
-        ('warfarin', 'aspirin'): 'Severe: Increased risk of bleeding and hemorrhage',
-        ('warfarin', 'ibuprofen'): 'Moderate: Increased risk of gastrointestinal bleeding',
-        ('simvastatin', 'clarithromycin'): 'Severe: Increased risk of myopathy and rhabdomyolysis',
-        ('digoxin', 'quinine'): 'Moderate: Increased digoxin levels, risk of toxicity',
-    }
-    
-    interaction = (known_interactions.get((drug1_clean, drug2_clean)) or 
-                   known_interactions.get((drug2_clean, drug1_clean)))
-    
-    return interaction or "No known severe interaction in mock database"
+    except:
+        return "Potential drug interaction requiring assessment."
 
 def predict_ddi(drug1_name, drug2_name):
     """Main prediction function"""
     try:
-        if not MODEL_LOADED:
-            return "Model not loaded properly. Please check the deployment.", "", "", "", ""
+        if not MODEL_LOADED or predictor is None:
+            return "Model not loaded. Please check requirements.txt", "", "", ""
         
         if not drug1_name or not drug2_name:
-            return "Please enter both drug names", "", "", "", ""
+            return "Please enter both drug names", "", "", ""
         
-        # Fetch data for both drugs
+        # Fetch drug data
         drug1_data, error1 = fetch_pubchem_data(drug1_name)
         drug2_data, error2 = fetch_pubchem_data(drug2_name)
         
-        if error1:
-            return f"Error with {drug1_name}: {error1}", "", "", "", ""
-        if error2:
-            return f"Error with {drug2_name}: {error2}", "", "", "", ""
+        if error1 or error2:
+            return f"Error: {error1 or error2}", "", "", ""
         
-        # Generate interaction description
+        # Generate description
         interaction_description = generate_interaction_description(drug1_data, drug2_data)
         
-        # Make prediction using your hosted model
+        # Make prediction
         result = predictor.predict(interaction_description)
         
-        # Check DrugBank (mock)
-        drugbank_result = check_drugbank_interaction(drug1_name, drug2_name)
-        
         # Prepare output
-        drug1_info = f"""
-        **{drug1_name}** (PubChem CID: {drug1_data.get('CID', 'N/A')})
-        - **IUPAC Name:** {drug1_data.get('IUPACName', 'N/A')}
-        - **Molecular Weight:** {drug1_data.get('MolecularWeight', 'N/A')} g/mol
-        - **LogP:** {drug1_data.get('XLogP', 'N/A')}
-        - **TPSA:** {drug1_data.get('TPSA', 'N/A')} Ų
-        - **SMILES:** {drug1_data.get('CanonicalSMILES', 'N/A')}
-        """
-        
-        drug2_info = f"""
-        **{drug2_name}** (PubChem CID: {drug2_data.get('CID', 'N/A')})
-        - **IUPAC Name:** {drug2_data.get('IUPACName', 'N/A')}
-        - **Molecular Weight:** {drug2_data.get('MolecularWeight', 'N/A')} g/mol
-        - **LogP:** {drug2_data.get('XLogP', 'N/A')}
-        - **TPSA:** {drug2_data.get('TPSA', 'N/A')} Ų
-        - **SMILES:** {drug2_data.get('CanonicalSMILES', 'N/A')}
+        drug_info = f"""
+        **{drug1_name}**: MW={drug1_data.get('MolecularWeight', 'N/A')} g/mol
+        **{drug2_name}**: MW={drug2_data.get('MolecularWeight', 'N/A')} g/mol
         """
         
         prediction_output = f"""
-        ## 🔍 AI Prediction Results
-        
-        **Model Source:** Fredaaaaaa/drug_interaction_severity
-        **Generated Description:** {interaction_description}
-        
-        **Prediction:** **{result['prediction']}**
+        **Prediction:** {result['prediction']}
         **Confidence:** {result['confidence']:.1%}
-        
-        **Probabilities:**
-        {', '.join([f'{k}: {v:.1%}' for k, v in result['probabilities'].items()])}
+        **Description:** {interaction_description}
         """
         
-        return prediction_output, drug1_info, drug2_info, drugbank_result, interaction_description
+        return prediction_output, drug_info, interaction_description, "✅ Success"
         
     except Exception as e:
-        return f"Error: {str(e)}", "", "", "", ""
+        return f"Error: {str(e)}", "", "", ""
 
-# Create Gradio interface
-with gr.Blocks(title="Drug Interaction Severity Predictor", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 💊 Drug Interaction Severity Predictor")
-    gr.Markdown("Powered by AI model from: [Fredaaaaaa/drug_interaction_severity](https://huggingface.co/Fredaaaaaa/drug_interaction_severity)")
+# Create simple interface
+with gr.Blocks(title="Drug Interaction Predictor") as demo:
+    gr.Markdown("# 💊 Drug Interaction Predictor")
+    gr.Markdown("Model: Fredaaaaaa/drug_interaction_severity")
     
     with gr.Row():
-        drug1 = gr.Textbox(label="First Drug Name", placeholder="e.g., Warfarin, Aspirin...")
-        drug2 = gr.Textbox(label="Second Drug Name", placeholder="e.g., Ibuprofen, Clarithromycin...")
+        drug1 = gr.Textbox(label="Drug 1", placeholder="e.g., Aspirin")
+        drug2 = gr.Textbox(label="Drug 2", placeholder="e.g., Warfarin")
     
-    predict_btn = gr.Button("🔬 Predict Interaction", variant="primary")
+    predict_btn = gr.Button("Predict", variant="primary")
     
-    with gr.Row():
-        prediction_output = gr.Markdown("## 📊 Prediction Results will appear here")
-    
-    with gr.Row():
-        with gr.Column():
-            drug1_info = gr.Markdown("### 💊 Drug 1 Properties")
-        with gr.Column():
-            drug2_info = gr.Markdown("### 💊 Drug 2 Properties")
-    
-    with gr.Row():
-        drugbank_info = gr.Textbox(label="🏥 DrugBank Comparison (Mock Data)")
-        interaction_desc = gr.Textbox(label="📝 AI-Generated Description")
-    
-    # Examples
-    gr.Examples(
-        examples=[["Warfarin", "Aspirin"], ["Simvastatin", "Clarithromycin"]],
-        inputs=[drug1, drug2],
-        label="💡 Try these examples:"
-    )
+    output = gr.Markdown("## Results will appear here")
+    drug_info = gr.Markdown()
+    interaction_desc = gr.Textbox(label="Generated Description")
+    status = gr.Textbox(label="Status")
     
     predict_btn.click(
         predict_ddi,
         [drug1, drug2],
-        [prediction_output, drug1_info, drug2_info, drugbank_info, interaction_desc]
+        [output, drug_info, interaction_desc, status]
     )
 
 if __name__ == "__main__":