Update app.py

app.py

@@ -66,68 +66,6 @@ def generate_response(user_query, relevant_segment):
     Generate a response emphasizing the bot's capability to provide information related to composting food.
     """
     
-    classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-    
-    try:
-        # Define the messages
-        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} {user_query}"
-        messages = [
-            {"role": "system", "content": system_message},
-            {"role": "user", "content": user_message}
-        ]
-        
-        # Generate the response using gpt-3.5-turbo-instruct
-        response = openai.ChatCompletion.create(
-            model="gpt-4o", 
-            messages=messages,
-            max_tokens=300,
-            temperature=0.5,
-            top_p=1
-            frequency_penalty=0.5,
-            presence_penalty=0.5
-        )
-        
-        # Extract the generated text
-        generated_text = response['choices'][0]['message']['content'].strip()
-        
-        return generated_text
-    
-    except Exception as e:
-        print(f"Error in generating response: {e}")
-        return f"Error in generating response: {e}"
-
-
-    # Function to classify the generated response and get confidence scores
-def classify_response(response_text, candidate_labels=["food composting", "other"]):
-    try:
-        # Perform classification
-        response = classifier(
-            sequences=response_text,
-            candidate_labels=candidate_labels
-        )
-
-    # Extract the confidence score for the most likely label
-        confidence_score = response['scores'][0]  # Confidence score for the most likely label
-
-        return confidence_score
-   
-    except Exception as e:
-        print(f"Error in classifying response: {e}")
-        return f"Error in classifying response: {e}"
-
-# Example usage
-openai.api_key = 'sk-proj-X437DVJaksFxSGcGBcuzT3BlbkFJBcQkP2utlrZg09UMRIIZ'  
-user_query = "How to compost food scraps?"
-relevant_segment = "Food composting involves..."
-generated_response = generate_response(user_query, relevant_segment)
-
-if generated_response:
-    confidence_score = classify_response(generated_response)
-    print(f"Generated Response: {generated_response}")
-    print(f"Confidence Score: {confidence_score}")
-
-    '''
     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}"
@@ -148,7 +86,6 @@ if generated_response:
     except Exception as e:
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
-    '''
 
 
 def query_model(question):