Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import re
 import requests
 import streamlit as st
 from langchain_huggingface import HuggingFaceEndpoint
@@ -70,16 +71,21 @@ def predict_action(user_text):
 def generate_follow_up(user_text):
     """
     Generates a concise and conversational follow-up question related to the user's input.
-    This version uses a clear prompt and provides a fallback if the generated output is empty.
+    The prompt instructs the model to avoid meta commentary.
     """
     prompt_text = (
-        f"Generate a friendly, concise follow-up question based on the user's question: '{user_text}'. "
-        "The follow-up should invite further discussion. For example, if the user asked about quarks, you might ask, "
-        "'Would you like to learn more about the six types of quarks, or is there another topic you're curious about?' "
-        "Always return a follow-up question."
+        f"Generate a concise, friendly follow-up question based on the user's question: '{user_text}'. "
+        "Do not include meta instructions or commentary such as 'Never return just a statement.' "
+        "For example, if the user asked about quarks, you might ask: "
+        "'Would you like to know more about the six types of quarks, or is there another aspect of quantum physics you're curious about?'"
     )
     hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.8)
     follow_up = hf.invoke(input=prompt_text).strip()
+    # Remove extraneous quotes if present.
+    follow_up = follow_up.strip('\'"')
+    # Optionally, remove any unwanted phrases (you can add more replacements if needed).
+    follow_up = re.sub(r"Never return just a statement\.?", "", follow_up, flags=re.IGNORECASE).strip()
+    # Ensure that something non-empty is returned.
     if not follow_up:
         follow_up = "Would you like to explore this topic further?"
     return follow_up
@@ -87,11 +93,22 @@ def generate_follow_up(user_text):
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     """
     Generates HAL's response in a friendly, conversational manner.
-    Uses sentiment analysis to adjust tone when appropriate.
-    Always generates a follow-up question that is appended to the chat history.
+    Uses sentiment analysis to adjust tone when appropriate and always generates a follow-up question.
+    If the user's input includes style instructions (e.g., 'in the voice of an astrophysicist'),
+    the prompt instructs HAL to adapt accordingly.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
+    
+    # Check for style instructions in the user message.
+    style_instruction = ""
+    lower_text = user_text.lower()
+    if "in the voice of" in lower_text or "speaking as" in lower_text:
+        # Extract the style instruction (a simple heuristic: take the part after "in the voice of")
+        match = re.search(r"(in the voice of|speaking as)(.*)", lower_text)
+        if match:
+            style_instruction = match.group(2).strip().capitalize()
+            style_instruction = f" Please respond in the voice of {style_instruction}."
 
     # Handle NASA-related queries separately.
     if action == "nasa_info":
@@ -112,13 +129,19 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
             continue
         filtered_history += f"{message['role']}: {message['content']}\n"
 
+    # Add style instruction to the prompt if applicable.
+    style_clause = ""
+    if style_instruction:
+        style_clause = style_instruction
+
     prompt = PromptTemplate.from_template(
         (
             "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
             "User: {user_text}.\n [/INST]\n"
-            "AI: Please answer the user's question without repeating previous greetings. "
-            "Keep your response friendly and conversational, starting with a phrase like "
-            "'Certainly!', 'Of course!', or 'Great question!'.\nHAL:"
+            "AI: Please answer the user's question without repeating any previous greetings."
+            " Keep your response friendly and conversational, starting with a phrase like 'Certainly!', 'Of course!', or 'Great question!'." +
+            style_clause +
+            "\nHAL:"
         )
     )
 
@@ -129,7 +152,7 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
 
-    # Only override with an empathetic response for negative sentiment if appropriate.
+    # Only override with an empathetic response for negative sentiment if the input is not a direct question.
     if sentiment == "NEGATIVE" and not user_text.strip().endswith("?"):
         response = "I'm sorry you're feeling this way. I'm here to help. What can I do to assist you further?"
         chat_history[-1]['content'] = response