Update app.py

app.py

@@ -8,25 +8,20 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from transformers import pipeline
 
-# 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
+# Must be the very first command!
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# --- Initialize Session State Variables ---
+# Appearance adjustments (if any) could be added here as well
+
+# 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 = {}  # Dictionary: conv_id -> chat_history
 
 # --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
@@ -41,12 +36,12 @@ def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7)
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
         temperature=temperature,
-        token=HF_TOKEN,
+        token=os.getenv("HF_TOKEN"),
         task="text-generation"
     )
 
 def get_nasa_apod():
-    url = f"https://api.nasa.gov/planetary/apod?api_key={NASA_API_KEY}"
+    url = f"https://api.nasa.gov/planetary/apod?api_key={os.getenv('NASA_API_KEY')}"
     response = requests.get(url)
     if response.status_code == 200:
         data = response.json()
@@ -64,14 +59,11 @@ def predict_action(user_text):
     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 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."
+        "that invite further discussion. For example, one variant might ask, "
+        "'Would you like to know more about the six types of quarks?' and another might ask, "
+        "'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()
@@ -82,15 +74,8 @@ 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:
@@ -117,24 +102,20 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     
     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!'." + style_clause +
+            "AI: Please answer the user's question in depth in a friendly, conversational tone, 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]."
     
@@ -150,7 +131,22 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     
     return response, follow_up, chat_history, None
 
-# --- Chat UI ---
+# --- Sidebar: Saved Conversations ---
+st.sidebar.header("Saved Conversations")
+if st.sidebar.button("Save Current Conversation"):
+    conv_id = f"Conv {len(st.session_state.saved_conversations) + 1}"
+    # Save a copy of the current conversation history
+    st.session_state.saved_conversations[conv_id] = st.session_state.chat_history.copy()
+    st.sidebar.success(f"Conversation saved as {conv_id}.")
+
+# Display saved conversation links
+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}.")
+
+# --- Chat UI Rendering ---
 st.title("🚀 HAL - Your NASA AI Assistant")
 st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
 
@@ -160,36 +156,13 @@ if st.sidebar.button("Reset Chat"):
     st.session_state.follow_up = ""
     st.experimental_rerun()
 
-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)
+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)
 
 user_input = st.chat_input("Type your message here...")
 
@@ -203,11 +176,3 @@ 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)