Update app.py

app.py

@@ -143,7 +143,6 @@ knowledge_base = """
 
 
 
-# Update the predict_disease function
 def predict_disease(image):
     # Apply transformations to the image
     img_tensor = transform(image).unsqueeze(0)  # Add batch dimension
@@ -156,34 +155,19 @@ def predict_disease(image):
     # Get the predicted label
     predicted_label = class_names[predicted_class.item()]
 
-    # # Retrieve response from knowledge base
-    # if predicted_label in knowledge_base:
-    #     detailed_response = knowledge_base[predicted_label]
-    # else:
-    #     # Fallback to AI-generated response
-    predicted_label = f'The predicted label is {predicted_label}'  
-    detailed_response = get_response_llm(predicted_label,knowledge_base)
+    # Generate a detailed response using the LLM
+    detailed_response = get_response_llm(predicted_label, knowledge_base)
 
-    # Create a styled HTML output
+    # Create a styled HTML output with bullet points and emphasis
     output_message = f"""
     <div style='font-size: 18px; color: #4CAF50; font-weight: bold;'>
         Detected Disease: <span style='color: #FF5722;'>{predicted_label}</span>
     </div>
+    <div style='font-size: 16px; color: #757575; line-height: 1.6;'>
+        {detailed_response.replace("\n", "<br>").replace("-", "•")}
+    </div>
     """
     
-    if predicted_label != "Healthy":
-        output_message += f"""
-        <p style='font-size: 16px; color: #757575;'>
-            {detailed_response}
-        </p>
-        """
-    else:
-        output_message += f"""
-        <p style='font-size: 16px; color: #757575;'>
-            {detailed_response}
-        </p>
-        """
-    
     return output_message
 
 # Create Gradio interface