Update app.py

app.py

@@ -51,6 +51,20 @@ def predict_action(user_text):
         return "weather_info"
     return "general_query"
 
+def generate_follow_up(user_text):
+    """
+    Generates a relevant follow-up question based on the user's input.
+    """
+    prompt_text = (
+        f"Given the user's message: '{user_text}', suggest a natural follow-up question "
+        "that continues the conversation and encourages engagement."
+    )
+
+    hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
+    chat = hf.invoke(input=prompt_text)
+    
+    return chat.strip()
+
 def get_response(system_message, chat_history, user_text, 
                  eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
     sentiment = analyze_sentiment(user_text)
@@ -60,7 +74,10 @@ def get_response(system_message, chat_history, user_text,
         nasa_response = get_nasa_apod()
         chat_history.append({'role': 'user', 'content': user_text})
         chat_history.append({'role': 'assistant', 'content': nasa_response})
-        return nasa_response, chat_history
+        
+        follow_up = generate_follow_up(user_text)
+        chat_history.append({'role': 'assistant', 'content': follow_up})
+        return f"{nasa_response}\n\n{follow_up}", chat_history
 
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
 
@@ -81,9 +98,12 @@ def get_response(system_message, chat_history, user_text,
 
     # Modify response based on sentiment analysis (e.g., offer help for negative sentiments)
     if sentiment == "NEGATIVE":
-        response = "I'm sorry to hear that. How can I assist you further?"
+        response += "\nI'm sorry to hear that. How can I assist you further?"
+
+    follow_up = generate_follow_up(user_text)
+    chat_history.append({'role': 'assistant', 'content': follow_up})
 
-    return response, chat_history
+    return f"{response}\n\n{follow_up}", chat_history
 
 # Streamlit setup
 st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")