Update app.py

app.py

@@ -40,15 +40,9 @@ transform = transforms.Compose([
 # Load the class names (disease types)
 class_names = ['BacterialBlights', 'Healthy', 'Mosaic', 'RedRot', 'Rust', 'Yellow']
 
-# Updated function to avoid backslash issues
-def get_response_llm(predicted_label, knowledge_base):
-    # Break down the prompt without unintended escape characters
-    prompt = (
-        f"You're a helpful assistant who helps farmers know about sugarcane diseases. "
-        f"Predicted disease label: '{predicted_label}'. "
-        f"Here is some additional knowledge: {knowledge_base}. "
-        f"Please provide a detailed response including advice, symptoms, and precautions."
-    )
+#Gemini Response 
+def get_response_llm(predicted_label,knowledge_base):
+    prompt = f"Your an helpful assistant who helps farmers know about the sugarcane leaf diseases , precaution, advise etc....Predicted disease label will is given to you  '{predicted_label}' and also {knowledge_base} Provide breif answer of advise for managing this condition.Give the response in a beautiful way like bold or bullet point etc.. wherever required"
     genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
     model = genai.GenerativeModel("gemini-1.5-flash")
     response = model.generate_content([prompt])
@@ -149,6 +143,7 @@ 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
@@ -161,19 +156,34 @@ def predict_disease(image):
     # Get the predicted label
     predicted_label = class_names[predicted_class.item()]
 
-    # Generate a detailed response using the LLM
-    detailed_response = get_response_llm(predicted_label, knowledge_base)
+    # # 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)
 
-    # Create a styled HTML output with bullet points and emphasis
+    # Create a styled HTML output
     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