Update app.py

app.py

@@ -11,29 +11,32 @@ from langdetect import detect  # Ensure this package is installed
 
 # ✅ Environment Variables
 HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
+NASA_API_KEY = os.getenv("NASA_API_KEY")
+
+if not HF_TOKEN:
     raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")
 
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-if NASA_API_KEY is None:
+if not NASA_API_KEY:
     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
+# ✅ Ensure Session State Variables (Preserve History)
 if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
+    st.session_state.chat_history = []
+
 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
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=1024, temperature=0.7):
+# ✅ Initialize Hugging Face Model with Limited Tokens
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=300, temperature=0.7):
     return HuggingFaceEndpoint(
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
@@ -52,10 +55,8 @@ 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]
@@ -67,154 +68,5 @@ def predict_action(user_text):
         return "nasa_info"
     return "general_query"
 
-# ✅ 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=1024):
-    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:
-    # ✅ Ensure get_response() returns a response
-    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
-    )
-
-    # ✅ 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
+# ✅ Generate Follow-Up Question
+def generate_foll