Update app.py

app.py

@@ -8,72 +8,27 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from transformers import pipeline
 
-# Must be the first Streamlit command!
+# Use environment variables for keys
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("HF_TOKEN environment variable not set. Please set it in your Hugging Face Space settings.")
+
+NASA_API_KEY = os.getenv("NASA_API_KEY")
+if NASA_API_KEY is None:
+    raise ValueError("NASA_API_KEY environment variable not set. Please set it in your Hugging Face Space settings.")
+
+# Set up Streamlit UI
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# Appearance settings (optional): you can modify these as needed
-user_bg_color = "#0078D7"
-assistant_bg_color = "#333333"
-text_color = "#FFFFFF"
-font_choice = "sans serif"
-
-# Inject custom CSS for appearance
-custom_css = f"""
-<style>
-    .user-msg {{
-        background-color: {user_bg_color};
-        color: {text_color};
-        padding: 10px;
-        border-radius: 10px;
-        margin-bottom: 5px;
-        width: fit-content;
-        max-width: 80%;
-        font-family: {font_choice};
-    }}
-    .assistant-msg {{
-        background-color: {assistant_bg_color};
-        color: {text_color};
-        padding: 10px;
-        border-radius: 10px;
-        margin-bottom: 5px;
-        width: fit-content;
-        max-width: 80%;
-        font-family: {font_choice};
-    }}
-    .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>
-"""
-st.markdown(custom_css, unsafe_allow_html=True)
-
-# Initialize session state variables
+# --- Initialize Session State Variables ---
 if "chat_history" not in st.session_state:
     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 = ""
-if "saved_conversations" not in st.session_state:
-    st.session_state.saved_conversations = {}  # dict mapping conv_id -> chat_history
-
-# Set up keys from environment variables
-HF_TOKEN = os.getenv("HF_TOKEN")
-if not HF_TOKEN:
-    raise ValueError("HF_TOKEN environment variable not set.")
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-if not NASA_API_KEY:
-    raise ValueError("NASA_API_KEY environment variable not set.")
 
-# --- Model & API functions ---
+# --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 sentiment_analyzer = pipeline(
     "sentiment-analysis",
@@ -97,20 +52,26 @@ def get_nasa_apod():
         data = response.json()
         return data.get("url", ""), data.get("title", ""), data.get("explanation", "")
     else:
-        return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now."
+        return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now. Please try again later."
 
 def analyze_sentiment(user_text):
     result = sentiment_analyzer(user_text)[0]
     return result['label']
 
 def predict_action(user_text):
-    return "nasa_info" if ("nasa" in user_text.lower() or "space" in user_text.lower()) else "general_query"
+    if "NASA" in user_text or "space" in user_text:
+        return "nasa_info"
+    return "general_query"
 
 def generate_follow_up(user_text):
+    """
+    Generates two variant follow-up questions and randomly selects one.
+    It also cleans up any unwanted quotation marks or extra meta commentary.
+    """
     prompt_text = (
-        f"Based on the user's question: '{user_text}', generate two concise, friendly follow-up questions that invite further discussion. "
-        "For example, one could be 'Would you like to know more about the six types of quarks?' and another 'Would you like to explore another aspect of quantum physics?'. "
-        "Return only the questions, separated by a newline."
+        f"Based on the user's question: '{user_text}', generate two concise, friendly follow-up questions "
+        "that invite further discussion. For example, one might be 'Would you like to know more about the six types of quarks?' "
+        "and another might be 'Would you like to explore another aspect of quantum physics?' Do not include extra commentary."
     )
     hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
     output = hf.invoke(input=prompt_text).strip()
@@ -121,8 +82,15 @@ def generate_follow_up(user_text):
     return random.choice(cleaned)
 
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
+    """
+    Generates HAL's answer with depth and a follow-up question.
+    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.
+    """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
+    
+    # Extract style instruction if present
     style_instruction = ""
     lower_text = user_text.lower()
     if "in the voice of" in lower_text or "speaking as" in lower_text:
@@ -131,7 +99,6 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
             style_instruction = match.group(2).strip().capitalize()
             style_instruction = f" Please respond in the voice of {style_instruction}."
     
-    # Handle NASA queries separately
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -140,7 +107,7 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         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:
@@ -149,43 +116,41 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         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, in-depth answer in a friendly, conversational tone. "
-            "Begin with a phrase like 'Certainly!', 'Of course!', or 'Great question!'." + style_clause +
+            "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!'." + style_clause +
             "\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))
+    # Remove any extra markers if present.
     response = response.split("HAL:")[-1].strip()
+    
+    # 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})
+    
     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 = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
-    return response, follow_up, chat_history, None
-
-# --- Sidebar: Save/Load Conversations ---
-st.sidebar.header("Saved Conversations")
-if st.sidebar.button("Save Current Conversation"):
-    conv_id = f"Conv {len(st.session_state.saved_conversations) + 1}"
-    st.session_state.saved_conversations[conv_id] = st.session_state.chat_history.copy()
-    st.sidebar.success(f"Conversation saved as {conv_id}.")
     
-if st.session_state.saved_conversations:
-    for conv_id in st.session_state.saved_conversations:
-        if st.sidebar.button(f"Load {conv_id}"):
-            st.session_state.chat_history = st.session_state.saved_conversations[conv_id].copy()
-            st.sidebar.info(f"Loaded {conv_id}.")
+    return response, follow_up, chat_history, None
 
-# --- Main Chat UI ---
+# --- Chat UI ---
 st.title("🚀 HAL - Your NASA AI Assistant")
 st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
 
@@ -195,13 +160,36 @@ if st.sidebar.button("Reset Chat"):
     st.session_state.follow_up = ""
     st.experimental_rerun()
 
-st.markdown("<div class='container'>", unsafe_allow_html=True)
-for message in st.session_state.chat_history:
-    if message["role"] == "user":
-        st.markdown(f"<div class='user-msg'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
-    else:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
-st.markdown("</div>", unsafe_allow_html=True)
+st.markdown("""
+    <style>
+    .user-msg {
+        background-color: #696969;
+        color: white;
+        padding: 10px;
+        border-radius: 10px;
+        margin-bottom: 5px;
+        width: fit-content;
+        max-width: 80%;
+    }
+    .assistant-msg {
+        background-color: #333333;
+        color: white;
+        padding: 10px;
+        border-radius: 10px;
+        margin-bottom: 5px;
+        width: fit-content;
+        max-width: 80%;
+    }
+    .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)
 
 user_input = st.chat_input("Type your message here...")
 
@@ -215,3 +203,11 @@ if user_input:
         st.image(image_url, caption="NASA Image of the Day")
     st.session_state.follow_up = follow_up
     st.session_state.response_ready = True
+
+st.markdown("<div class='container'>", unsafe_allow_html=True)
+for message in st.session_state.chat_history:
+    if message["role"] == "user":
+        st.markdown(f"<div class='user-msg'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
+    else:
+        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
+st.markdown("</div>", unsafe_allow_html=True)