Update app.py

app.py

@@ -44,11 +44,12 @@ def load_spacy_model():
 nlp = load_spacy_model()
 
 class EnhancedContextDrivenChatbot:
-    def __init__(self, history_size=10):
+    def __init__(self, history_size=10, model=None):
         self.history = []
         self.history_size = history_size
         self.entity_tracker = {}
         self.conversation_context = ""
+        self.model = model
 
     def add_to_history(self, text):
         self.history.append(text)
@@ -100,6 +101,9 @@ class EnhancedContextDrivenChatbot:
         return question
 
     def rephrase_query(self, question):
+        if not self.model:
+            return question  # Return original question if no model is available
+        
         prompt = f"""
         Given the conversation context and the current question, rephrase the question to include relevant context:
         
@@ -109,22 +113,18 @@ class EnhancedContextDrivenChatbot:
         Rephrased question:
         """
         
-        # Use your language model to generate the rephrased question
-        rephrased_question = generate_chunked_response(model, prompt)
+        rephrased_question = generate_chunked_response(self.model, prompt)
         
         return rephrased_question.strip()
 
     def process_question(self, question):
         contextualized_question = self.get_most_relevant_context(question)
         
-        # Extract topics from the question
         topics = self.extract_topics(question)
         
-        # Check if it's a follow-up question and rephrase if necessary
         if self.is_follow_up_question(question):
             contextualized_question = self.rephrase_query(contextualized_question)
         
-        # Add the new question to history
         self.add_to_history(question)
         
         return contextualized_question, topics, self.entity_tracker
@@ -309,6 +309,10 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search, c
         return "Please enter a question."
 
     model = get_model(temperature, top_p, repetition_penalty)
+    
+    # Update the chatbot's model
+    chatbot.model = model
+
     embed = get_embeddings()
 
     if os.path.exists("faiss_database"):