Update app.py

app.py

@@ -116,7 +116,10 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=512):
     The prompt instructs the model to provide a detailed explanation and then generate a follow-up.
     If the answer comes back empty, a fallback answer is used.
     """
-
+    
+    # 🔍 Determine the user's intent (NASA Info or General Query)
+    action = predict_action(user_text)  # 🔥 Define 'action' here
+    
     # Extract style instruction if present
     style_instruction = ""
     lower_text = user_text.lower()
@@ -125,7 +128,8 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=512):
         if match:
             style_instruction = match.group(2).strip().capitalize()
             style_instruction = f" Please respond in the voice of {style_instruction}."
-    
+
+    # 🚀 Handle NASA-specific queries
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -134,29 +138,29 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=512):
         follow_up = generate_follow_up(user_text)
         chat_history.append({'role': 'assistant', 'content': follow_up})
         return response, follow_up, chat_history, nasa_url
-    
+
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
     filtered_history = ""
     for message in chat_history:
         if message["role"] == "assistant" and message["content"].strip() == "Hello! How can I assist you today?":
             continue
         filtered_history += f"{message['role']}: {message['content']}\n"
-    
+
     style_clause = style_instruction if style_instruction else ""
-    
+
     # Instruct the model to generate a detailed, in-depth answer.
     prompt = PromptTemplate.from_template(
-    (
-        "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
-        "User: {user_text}.\n [/INST]\n"
-        "AI: Please provide a detailed explanation in depth. "
-        "Ensure your response covers the topic thoroughly and is written in a friendly, conversational style, "
-        "starting with a phrase like 'Certainly!', 'Of course!', or 'Great question!'."
-        "🚨 IMPORTANT: Answer exclusively in **English only**. Do not generate responses in any other language."
-        "\nHAL:"
+        (
+            "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
+            "User: {user_text}.\n [/INST]\n"
+            "AI: Please provide a detailed explanation in depth. "
+            "Ensure your response covers the topic thoroughly and is written in a friendly, conversational style, "
+            "starting with a phrase like 'Certainly!', 'Of course!', or 'Great question!'."
+            "🚨 IMPORTANT: Answer exclusively in **English only**. Do not generate responses in any other language."
+            "\nHAL:"
+        )
     )
-)
-    
+
     chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
     response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
     response = response.split("HAL:")[-1].strip()
@@ -164,24 +168,10 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=512):
     # 🚨 Ensure the response is in English
     response = ensure_english(response)
 
-
     # Fallback in case the generated answer is empty
     if not response:
-        response = "Certainly, here is an in-depth explanation: [Fallback explanation]."
-
-    chat_history.append({'role': 'user', 'content': user_text})
-    chat_history.append({'role': 'assistant', 'content': response})
-
-    # 🔍 FIX: Only override if strongly negative and NOT a 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
-        follow_up = None  # 🚨 Don't generate follow-up if negative sentiment triggers a different message
-    else:
-        follow_up = generate_follow_up(user_text)
-        chat_history.append({'role': 'assistant', 'content': follow_up})
+    
 
-    return response, follow_up, chat_history, None
 
 
 # --- Chat UI ---