Update app.py

app.py

@@ -23,16 +23,21 @@ if "follow_up" not in st.session_state:
 # --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
-# Initialize sentiment analysis pipeline
-sentiment_analyzer = pipeline("sentiment-analysis")
-
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
+# Initialize sentiment analysis pipeline with explicit model specification
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+    revision="714eb0f"
+)
+
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
+    # Explicitly specify task="text-generation" so that the endpoint knows which task to run
     return HuggingFaceEndpoint(
         repo_id=model_id,
-        task="text-generation",  # Specify the task explicitly
         max_new_tokens=max_new_tokens,
         temperature=temperature,
-        token=os.getenv("HF_TOKEN")  # Hugging Face API Token
+        token=os.getenv("HF_TOKEN"),
+        task="text-generation"
     )
 
 def get_nasa_apod():
@@ -54,14 +59,21 @@ def predict_action(user_text):
     return "general_query"
 
 def generate_follow_up(user_text):
+    """
+    Generates a concise and conversational follow-up question related to the user's input.
+    """
     prompt_text = (
-        f"Based on the user's message: '{user_text}', suggest a natural follow-up question "
-        "to keep the conversation engaging."
+        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."
     )
-    hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
+    hf = get_llm_hf_inference(max_new_tokens=32, 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, making it more conversational and engaging.
+    """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
 
@@ -75,20 +87,25 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         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=0.1)
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
 
     prompt = PromptTemplate.from_template(
-        "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\nAI:"
+        (
+            "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\n"
+            "AI: Keep responses conversational and engaging. Start with a friendly phrase like "
+            "'Certainly!', 'Of course!', or 'Great question!' before answering. "
+            "Keep responses concise but engaging.\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=chat_history))
-    response = response.split("AI:")[-1]
+    response = response.split("HAL:")[-1].strip()
 
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
 
     if sentiment == "NEGATIVE":
-        response += "\n😞 I'm sorry to hear that. How can I assist you further?"
+        response = "I'm here to help. Let me know what I can do for you. 😊"
 
     follow_up = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
@@ -147,42 +164,24 @@ for message in st.session_state.chat_history:
         st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
 st.markdown("</div>", unsafe_allow_html=True)
 
-# --- Input & Button Handling ---
-user_input = st.text_area("Type your message:", height=100)
-
-send_button_placeholder = st.empty()
+# --- Single Input Box for Both Initial and Follow-Up Messages ---
+user_input = st.chat_input("Type your message here...")  # Only ONE chat_input()
 
-if not st.session_state.response_ready:
-    if send_button_placeholder.button("Send"):
-        if user_input:
-            response, follow_up, st.session_state.chat_history, image_url = get_response(
-                system_message="You are a helpful AI assistant.",
-                user_text=user_input,
-                chat_history=st.session_state.chat_history
-            )
+if user_input:
+    response, follow_up, st.session_state.chat_history, image_url = get_response(
+        system_message="You are a helpful AI assistant.",
+        user_text=user_input,
+        chat_history=st.session_state.chat_history
+    )
 
-            st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
+    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
 
-            if image_url:
-                st.image(image_url, caption="NASA Image of the Day")
+    if image_url:
+        st.image(image_url, caption="NASA Image of the Day")
 
-            # Store follow-up question
-            st.session_state.follow_up = follow_up
-            st.session_state.response_ready = True  # Hide Send button after response
+    st.session_state.follow_up = follow_up
+    st.session_state.response_ready = True
 
-# Conversational Follow-up
 if st.session_state.response_ready and st.session_state.follow_up:
     st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {st.session_state.follow_up}</div>", unsafe_allow_html=True)
-
-    next_input = st.text_input("HAL is waiting for your response...")
-
-    if next_input:
-        response, _, st.session_state.chat_history, _ = get_response(
-            system_message="You are a helpful AI assistant.",
-            user_text=next_input,
-            chat_history=st.session_state.chat_history
-        )
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
-
-        st.session_state.response_ready = False
-        st.session_state.follow_up = ""
+    st.session_state.response_ready = False