Update app.py

app.py

@@ -11,32 +11,29 @@ from langdetect import detect  # Ensure this package is installed
 
 # ✅ Environment Variables
 HF_TOKEN = os.getenv("HF_TOKEN")
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-
-if not HF_TOKEN:
+if HF_TOKEN is None:
     raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")
 
-if not NASA_API_KEY:
+NASA_API_KEY = os.getenv("NASA_API_KEY")
+if NASA_API_KEY is None:
     raise ValueError("NASA_API_KEY is not set. Please add it to your environment variables.")
 
 # ✅ Set Up Streamlit
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# ✅ Ensure Session State Variables (Preserve History)
+# ✅ Ensure Session State Variables
 if "chat_history" not in st.session_state:
-    st.session_state.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
-
 if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""
 
 # ✅ Model Configuration
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
-# ✅ Initialize Hugging Face Model with Limited Tokens
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=300, temperature=0.7):
+# ✅ Initialize Hugging Face Model
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=1024, temperature=0.7):
     return HuggingFaceEndpoint(
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
@@ -55,8 +52,10 @@ def get_nasa_apod():
     return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now."
 
 # ✅ Sentiment Analysis
-sentiment_analyzer = pipeline("sentiment-analysis",
-    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+)
 
 def analyze_sentiment(user_text):
     result = sentiment_analyzer(user_text)[0]
@@ -68,5 +67,161 @@ def predict_action(user_text):
         return "nasa_info"
     return "general_query"
 
-# ✅ Generate Follow-Up Question
-def generate_foll
+# ✅ Follow-Up Question Generation
+def generate_follow_up(user_text):
+    prompt_text = f"Based on: '{user_text}', generate a concise, friendly follow-up."
+    hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
+    output = hf.invoke(input=prompt_text).strip()
+    return output if output else "Would you like to explore this topic further?"
+
+# ✅ Ensure English Responses
+def ensure_english(text):
+    try:
+        detected_lang = detect(text)
+        if detected_lang != "en":
+            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
+    except:
+        return "⚠️ Language detection failed. Please ask your question again."
+    return text
+
+# ✅ Ensure Every Response Has a Follow-Up Question
+def generate_follow_up(user_text):
+    """Generates a follow-up question to guide the user toward related topics or next steps."""
+    prompt_text = (
+        f"Given the user's question: '{user_text}', generate a SHORT follow-up question "
+        "suggesting either a related topic or asking if they need further help. "
+        "Example: 'Would you like to explore quantum superposition or ask about another physics concept?' "
+        "Keep it concise and engaging."
+    )
+    hf = get_llm_hf_inference(max_new_tokens=40, temperature=0.8)
+    output = hf.invoke(input=prompt_text).strip()
+    
+    # Fallback in case of an empty response
+    return output if output else "Would you like to explore another related topic or ask about something else?"
+
+# ✅ Main Response Function
+def get_response(system_message, chat_history, user_text, max_new_tokens=512):
+    action = predict_action(user_text)  # 🔥 Fix: Define 'action'
+
+    # ✅ 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}"
+        chat_history.append({'role': 'user', 'content': user_text})
+        chat_history.append({'role': 'assistant', 'content': response})
+        follow_up = generate_follow_up(user_text)
+        chat_history.append({'role': 'assistant', 'content': follow_up})
+        return response, follow_up, chat_history, nasa_url
+
+    # ✅ Set Up LLM Request
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
+
+    # ✅ Format Chat History
+    filtered_history = "\n".join(f"{msg['role']}: {msg['content']}" for msg in chat_history)
+
+    # ✅ Prompt Engineering
+    prompt = PromptTemplate.from_template(
+        "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
+        "User: {user_text}.\n [/INST]\n"
+        "AI: Provide a detailed explanation with depth. "
+        "Use a conversational style, starting with 'Certainly!', 'Of course!', or 'Great question!'."
+        "🚨 Answer **only in English**."
+        "\nHAL:"
+    )
+
+    # ✅ Invoke LLM Model
+    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() if "HAL:" in response else response.strip()
+
+    # ✅ Ensure English
+    response = ensure_english(response)
+
+    # ✅ Fallback Response
+    if not response:
+        response = "I'm sorry, but I couldn't generate a response. Can you rephrase your question?"
+
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+
+    follow_up = generate_follow_up(user_text)
+    chat_history.append({'role': 'assistant', 'content': follow_up})
+
+    return response, follow_up, chat_history, None
+
+# ✅ Streamlit UI
+st.title("🚀 HAL - NASA AI Assistant")
+
+# ✅ Justify all chatbot responses
+st.markdown("""
+    <style>
+    .user-msg {
+        background-color: #696969;
+        color: white;
+        padding: 10px;
+        border-radius: 10px;
+        margin-bottom: 5px;
+        width: fit-content;
+        max-width: 80%;
+        text-align: justify;  /* ✅ Justify text */
+    }
+    .assistant-msg {
+        background-color: #333333;
+        color: white;
+        padding: 10px;
+        border-radius: 10px;
+        margin-bottom: 5px;
+        width: fit-content;
+        max-width: 80%;
+        text-align: justify;  /* ✅ Justify text */
+    }
+    .container {
+        display: flex;
+        flex-direction: column;
+        align-items: flex-start;
+    }
+    @media (max-width: 600px) {
+        .user-msg, .assistant-msg { font-size: 16px; max-width: 100%; }
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+
+# ✅ Reset Chat Button
+if st.sidebar.button("Reset Chat"):
+    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
+    st.session_state.response_ready = False
+    st.session_state.follow_up = ""
+
+# ✅ Chat UI
+user_input = st.chat_input("Type your message here...")
+
+if user_input:
+    # Save user message
+    st.session_state.chat_history.append({'role': 'user', 'content': user_input})
+
+    # Get AI response (replace with actual function)
+    response = "This is HAL's response."  # Example response
+    follow_up = "Would you like to explore a related topic?"  # Follow-up suggestion
+
+    # Save AI response & follow-up
+    st.session_state.chat_history.append({'role': 'assistant', 'content': response})
+    st.session_state.chat_history.append({'role': 'assistant', 'content': follow_up})
+
+    # Store follow-up question separately if needed
+    st.session_state.follow_up = follow_up
+
+    # ✅ Ensure response is not empty before calling st.markdown()
+    if response:
+        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")
+
+    st.session_state.follow_up = follow_up
+    st.session_state.response_ready = True
+
+# ✅ Check before displaying follow-up message
+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)
+    st.session_state.response_ready = False