Update app.py

app.py

@@ -20,14 +20,14 @@ st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
 # --- Initialize Session State Variables ---
 if "chat_history" not in st.session_state:
-    # The initial greeting is stored in chat_history
+    # The initial greeting is stored in chat_history.
     st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
 
 if "response_ready" not in st.session_state:
     st.session_state.response_ready = False  # Tracks whether HAL has responded
 
 if "follow_up" not in st.session_state:
-    st.session_state.follow_up = ""  # Stores follow-up question
+    st.session_state.follow_up = ""  # Stores the follow-up question
 
 # --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
@@ -70,23 +70,27 @@ 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.
     """
     prompt_text = (
-        f"Given the user's question: '{user_text}', generate a SHORT and SIMPLE follow-up question. "
-        "Make it conversational and friendly. Example: 'Would you like to learn more about the six types of quarks?' "
-        "Do NOT provide long explanations—just ask a friendly follow-up question."
+        f"Given the user's question: '{user_text}', generate a friendly, 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."
     )
-    hf = get_llm_hf_inference(max_new_tokens=32, temperature=0.7)
+    hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
     return hf.invoke(input=prompt_text).strip()
 
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     """
     Generates HAL's response in a friendly, conversational manner.
-    This version filters out the initial greeting from chat_history to prevent repetition.
+    It uses sentiment analysis to adjust the tone if the user's input is negative.
+    The function also generates a follow-up question.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
 
+    # 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}"
@@ -109,8 +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 any previous greetings. "
-            "Keep your response friendly and conversational, starting with a phrase like "
+            "AI: Please answer the user's question without repeating previous greetings. "
+            "Keep your response friendly and conversational, beginning with a phrase like "
             "'Certainly!', 'Of course!', or 'Great question!'.\nHAL:"
         )
     )
@@ -119,11 +123,15 @@ 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":
-        response = "I'm here to help. Let me know what I can do for you. 😊"
+        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
+        chat_history[-1]['content'] = response
 
     follow_up = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
@@ -189,7 +197,7 @@ if user_input:
     st.session_state.follow_up = follow_up
     st.session_state.response_ready = True
 
-# Now render the entire chat history after processing new input.
+# Render the entire chat history after processing new input.
 st.markdown("<div class='container'>", unsafe_allow_html=True)
 for message in st.session_state.chat_history:
     if message["role"] == "user":
@@ -197,4 +205,3 @@ for message in st.session_state.chat_history:
     else:
         st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
 st.markdown("</div>", unsafe_allow_html=True)
-