Update app.py

app.py

@@ -70,13 +70,13 @@ 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 is designed to prompt a follow-up question that is relevant to the topic.
+    This version is designed to prompt a friendly follow-up question that is relevant to the topic.
     """
     prompt_text = (
-        f"Given the user's question: '{user_text}', generate a friendly, varied follow-up question. "
+        f"Given the user's question: '{user_text}', generate a friendly and varied follow-up question. "
         "For example, if the question is about quarks, you might ask, "
         "'Would you like to know more about the six types of quarks, or is there another topic you'd like to explore?' "
-        "Make sure the follow-up is concise and conversational."
+        "Keep it concise and conversational."
     )
     hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
     return hf.invoke(input=prompt_text).strip()
@@ -84,13 +84,13 @@ 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.
-    It uses sentiment analysis to adjust the tone if the user's input is negative.
-    The function also generates a follow-up question.
+    Uses sentiment analysis to adjust tone when appropriate, but only overrides for negative sentiment if
+    the user's input is not a direct question.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
 
-    # Handle NASA-related queries separately
+    # Handle NASA-related queries separately.
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -102,7 +102,7 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
 
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
 
-    # Build a filtered conversation history excluding the initial greeting
+    # Build a filtered conversation history excluding the initial greeting.
     filtered_history = ""
     for message in chat_history:
         if message["role"] == "assistant" and message["content"].strip() == "Hello! How can I assist you today?":
@@ -113,8 +113,8 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         (
             "[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, beginning with a phrase like "
+            "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!'.\nHAL:"
         )
     )
@@ -123,14 +123,13 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
     response = response.split("HAL:")[-1].strip()
 
-    # Append user and assistant messages to the chat history
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
 
-    # Adjust the response if the sentiment is negative
-    if sentiment == "NEGATIVE":
+    # Only override for negative sentiment if the user 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?"
-        # Update the last assistant message in chat_history with the empathetic response
+        # Update the last assistant message in chat_history with the empathetic response.
         chat_history[-1]['content'] = response
 
     follow_up = generate_follow_up(user_text)
@@ -197,7 +196,7 @@ if user_input:
     st.session_state.follow_up = follow_up
     st.session_state.response_ready = True
 
-# Render the entire chat history after processing new input.
+# Render the entire chat history.
 st.markdown("<div class='container'>", unsafe_allow_html=True)
 for message in st.session_state.chat_history:
     if message["role"] == "user":