Update app.py

app.py

@@ -8,6 +8,50 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from transformers import pipeline
 
+# Appearance Settings: Allow user to adjust UI appearance via sidebar.
+user_bg_color = st.sidebar.color_picker("User Message Background", "#0078D7")
+assistant_bg_color = st.sidebar.color_picker("Assistant Message Background", "#333333")
+text_color = st.sidebar.color_picker("Message Text Color", "#FFFFFF")
+font_choice = st.sidebar.selectbox("Font Family", ["sans serif", "serif", "monospace"])
+
+# 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)
+
 # Use environment variables for keys
 HF_TOKEN = os.getenv("HF_TOKEN")
 if HF_TOKEN is None:
@@ -64,14 +108,10 @@ 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 (e.g., one might ask, 'Would you like to know more about the six types of quarks?' "
+        "and another might ask, 'Would you like to explore something else?'). Do not include extra meta commentary."
     )
     hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
     output = hf.invoke(input=prompt_text).strip()
@@ -82,15 +122,8 @@ 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:
@@ -98,7 +131,6 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         if match:
             style_instruction = match.group(2).strip().capitalize()
             style_instruction = f" Please respond in the voice of {style_instruction}."
-    
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -107,51 +139,37 @@ 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:
         if message["role"] == "assistant" and message["content"].strip() == "Hello! How can I assist you today?":
             continue
         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 explanation in depth. "
-            "Ensure your response covers the topic thoroughly and is written in a friendly, conversational style, "
+            "AI: Please answer the user's question in depth and 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]."
-    
     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
 
-# --- Chat UI ---
-st.title("🚀 HAL - A NASA AI Assistant")
+st.title("🚀 HAL - Your NASA AI Assistant")
 st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
 
 if st.sidebar.button("Reset Chat"):
@@ -160,36 +178,13 @@ 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 +198,3 @@ 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)