Update app.py

app.py

@@ -373,23 +373,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 # Use GPT-4o with its vector store and template
                 retriever = gpt_retriever
                 prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-            elif selected_model == phi_pipe:
-                # Use Phi-3.5 with its vector store and always use template2
-                retriever = phi_retriever
-                context = retriever.get_relevant_documents(message)
-                # Construct a simple, direct prompt for Phi-3.5
-                prompt = f"""
-                Based on the following information, provide a concise and well-formatted response without including questions or 'Helpful Answer' sections:
-
-                {context}
-
-                Information:
-                {message}
-                """
-
-            if selected_model == chat_model:
-                # Use GPT-4o with Langchain
-                prompt_template = QA_CHAIN_PROMPT_2  # Always using template2 for simplicity
                 context = retriever.get_relevant_documents(message)
                 prompt = prompt_template.format(context=context, question=message)
 
@@ -403,14 +386,32 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 return response['result'], extract_addresses(response['result'])
 
             elif selected_model == phi_pipe:
-                # Use Phi-3.5 directly with the simplified prompt
+                # Use Phi-3.5 with its vector store and a simplified prompt
+                retriever = phi_retriever
+                context = retriever.get_relevant_documents(message)
+                prompt = f"""
+                Here is the information based on the documents provided:
+                {context}
+
+                {message}
+                """
+
+                logging.debug(f"Phi-3.5 Prompt: {prompt}")
+
                 response = selected_model(prompt, **{
                     "max_new_tokens": 300,
                     "return_full_text": False,
                     "temperature": 0.5,
                     "do_sample": False,
-                })[0]['generated_text']
-                return response, extract_addresses(response)
+                })
+
+                if response:
+                    generated_text = response[0]['generated_text']
+                    logging.debug(f"Phi-3.5 Response: {generated_text}")
+                    return generated_text, extract_addresses(generated_text)
+                else:
+                    logging.error("Phi-3.5 did not return any response.")
+                    return "No response generated.", []
 
         elif retrieval_mode == "KGF":
             response = chain_neo4j.invoke({"question": message})