Update app.py

app.py

@@ -63,6 +63,7 @@ def find_relevant_segment(user_query, segments):
 def generate_response(user_query, relevant_segment):
     """
     Generate a response emphasizing the bot's capability to provide information related to composting food.
+    """
     try:
         system_message = "You are a chatbot specialized in providing information about food composting tips, tricks, and basics."
         user_message = f"Here's the information on composting: {relevant_segment}"
@@ -83,17 +84,6 @@ def generate_response(user_query, relevant_segment):
     except Exception as e:
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
-    """
-    # Create pipeline for text generation with confidence scores
-    generator = pipeline("text-davinci-003", device=0)  # Adjust device if needed
-
-    # Generate response and get confidence score
-    response = generator(query=f"Here's the information on chess: {relevant_segment} {user_query}", max_length=150, temperature=0.2, top_p=1)
-    generated_text = response[0]['generated_text'].strip()
-    confidence_score = response[0]['score']
-
-    return generated_text, confidence_score
-
 
 def query_model(question):
     """
@@ -140,15 +130,6 @@ with gr.Blocks(theme='gradio/seafoam') as demo:
             submit_button = gr.Button("Submit")
             submit_button.click(fn=query_model, inputs=question, outputs=answer)
     
-    answer = gr.Textbox(label="CompBot Response", placeholder="CompBot's response is...", interactive=False, lines=5)
-    submit_button = gr.Button("Submit")
-
-    def display_response(question):
-    response, confidence_score = query_model(question)
-    answer.value = f"Response: {response}\nConfidence Score: {confidence_score:.2f}"  
-
-    submit_button.click(fn=display_response, inputs=question, outputs=None)
-    
 
 # Launch the Gradio app to allow user interaction
 demo.launch(share=True)