Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
 import re
+import random
 import requests
 import streamlit as st
 from langchain_huggingface import HuggingFaceEndpoint
@@ -21,19 +22,17 @@ st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
 # --- Initialize Session State Variables ---
 if "chat_history" not in st.session_state:
-    # The initial greeting is stored in 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  # Tracks whether HAL has responded
+    st.session_state.response_ready = False
 
 if "follow_up" not in st.session_state:
-    st.session_state.follow_up = ""  # Stores the follow-up question
+    st.session_state.follow_up = ""
 
 # --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
-# Initialize sentiment analysis pipeline with explicit model specification
 sentiment_analyzer = pipeline(
     "sentiment-analysis",
     model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
@@ -41,7 +40,6 @@ sentiment_analyzer = pipeline(
 )
 
 def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
-    # Specify task="text-generation" so that the endpoint uses the correct function.
     return HuggingFaceEndpoint(
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
@@ -70,47 +68,43 @@ def predict_action(user_text):
 
 def generate_follow_up(user_text):
     """
-    Generates a concise and conversational follow-up question related to the user's input.
-    The prompt instructs the model to avoid meta commentary.
+    Generates varied follow-up questions for the given user input.
+    The prompt instructs the LLM to produce two variants, and one is selected randomly.
     """
     prompt_text = (
-        f"Generate a concise, friendly follow-up question based on the user's question: '{user_text}'. "
-        "Do not include meta instructions or commentary such as 'Never return just a statement.' "
-        "For example, if the user asked about quarks, you might ask: "
-        "'Would you like to know more about the six types of quarks, or is there another aspect of quantum physics you're curious about?'"
+        f"Based on the user's question: '{user_text}', generate two concise, friendly follow-up questions "
+        "that are relevant to the topic. One should ask something like, "
+        "'Would you like to know more about the six types of quarks?' and the other should ask, "
+        "'Would you like to explore something else?' Do not include any extra commentary or meta instructions."
     )
-    hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.8)
-    follow_up = hf.invoke(input=prompt_text).strip()
-    # Remove extraneous quotes if present.
-    follow_up = follow_up.strip('\'"')
-    # Optionally, remove any unwanted phrases (you can add more replacements if needed).
-    follow_up = re.sub(r"Never return just a statement\.?", "", follow_up, flags=re.IGNORECASE).strip()
-    # Ensure that something non-empty is returned.
-    if not follow_up:
-        follow_up = "Would you like to explore this topic further?"
-    return follow_up
+    hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
+    output = hf.invoke(input=prompt_text).strip()
+    # Split the output into separate lines if the model returns multiple variants.
+    variants = re.split(r"\n|[;]+", output)
+    # Clean up any extraneous quotes or unwanted text.
+    cleaned = [v.strip(' "\'') for v in variants if v.strip()]
+    # If no valid variants are found, provide a default fallback.
+    if not cleaned:
+        cleaned = ["Would you like to explore this topic further?"]
+    return random.choice(cleaned)
 
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     """
-    Generates HAL's response in a friendly, conversational manner.
-    Uses sentiment analysis to adjust tone when appropriate and always generates a follow-up question.
-    If the user's input includes style instructions (e.g., 'in the voice of an astrophysicist'),
-    the prompt instructs HAL to adapt accordingly.
+    Generates HAL's response with a friendly, conversational tone.
+    Incorporates sentiment analysis and always generates a follow-up question with variation.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
     
-    # Check for style instructions in the user message.
+    # Check for style instructions in the user's text (e.g., "in the voice of an astrophysicist")
     style_instruction = ""
     lower_text = user_text.lower()
     if "in the voice of" in lower_text or "speaking as" in lower_text:
-        # Extract the style instruction (a simple heuristic: take the part after "in the voice of")
         match = re.search(r"(in the voice of|speaking as)(.*)", lower_text)
         if match:
             style_instruction = match.group(2).strip().capitalize()
             style_instruction = f" Please respond in the voice of {style_instruction}."
-
-    # Handle NASA-related queries separately.
+    
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -119,61 +113,54 @@ 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)
-
-    # Build a filtered conversation history excluding the initial greeting.
+    
     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"
-
-    # Add style instruction to the prompt if applicable.
-    style_clause = ""
-    if style_instruction:
-        style_clause = style_instruction
-
+    
+    style_clause = style_instruction if style_instruction else ""
+    
     prompt = PromptTemplate.from_template(
         (
             "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
             "User: {user_text}.\n [/INST]\n"
-            "AI: Please answer the user's question without repeating any previous greetings."
-            " Keep your response friendly and conversational, starting with a phrase like 'Certainly!', 'Of course!', or 'Great question!'." +
-            style_clause +
+            "AI: Please answer the user's question without repeating previous greetings. "
+            "Keep your response friendly and conversational, 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))
     response = response.split("HAL:")[-1].strip()
-
+    
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
-
-    # Only override with an empathetic response for negative sentiment if the input is not a direct question.
+    
     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 - Your NASA AI Assistant")
 st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
 
-# Sidebar: Reset Chat
 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 = ""
     st.experimental_rerun()
 
-# Custom Chat Styling
 st.markdown("""
     <style>
     .user-msg {
@@ -205,8 +192,7 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# --- Single Input Box for Both Initial and Follow-Up Messages ---
-user_input = st.chat_input("Type your message here...")  # Only ONE chat_input()
+user_input = st.chat_input("Type your message here...")
 
 if user_input:
     response, follow_up, st.session_state.chat_history, image_url = get_response(
@@ -214,14 +200,13 @@ if user_input:
         user_text=user_input,
         chat_history=st.session_state.chat_history
     )
-
+    
     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
 
-# Render the entire chat history.
 st.markdown("<div class='container'>", unsafe_allow_html=True)
 for message in st.session_state.chat_history:
     if message["role"] == "user":