Update app.py

app.py

@@ -1,3 +1,4 @@
+pip install openai transformers
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import transformers
@@ -65,6 +66,9 @@ 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."
@@ -74,26 +78,56 @@ def generate_response(user_query, relevant_segment):
             {"role": "user", "content": user_message}
         ]
         
-        # Generate the response
+        # Generate the response using gpt-3.5-turbo-instruct
         response = openai.ChatCompletion.create(
-            model="text-davinci-003",
+            model="gpt-4o", 
             messages=messages,
-            max_tokens=150,
-            temperature=0.2,
+            max_tokens=300,
+            temperature=0.5,
             top_p=1
+            frequency_penalty=0.5,
+            presence_penalty=0.5
         )
         
-        # Extract the generated text and confidence score
+        # Extract the generated text
         generated_text = response['choices'][0]['message']['content'].strip()
-        # Confidence score extraction may vary depending on the model and API response structure
-        confidence_score = response['choices'][0].get('logprobs', {}).get('token_logprobs', [None])[0]
-
-        return generated_text, confidence_score
+        
+        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 = 'YOUR_API_KEY'  
+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."