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| import os | |
| import requests | |
| import streamlit as st | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| from transformers import pipeline | |
| from config import NASA_API_KEY # Ensure this file exists with your NASA API Key | |
| # Set up Streamlit UI | |
| st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀") | |
| # --- Ensure Session State Variables are Initialized --- | |
| 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 # Tracks whether HAL has responded | |
| if "follow_up" not in st.session_state: | |
| st.session_state.follow_up = "" # Stores follow-up question | |
| if "last_topic" not in st.session_state: | |
| st.session_state.last_topic = "" # Tracks last discussed topic | |
| # --- Set Up Model & API Functions --- | |
| model_id = "mistralai/Mistral-7B-Instruct-v0.3" | |
| # Initialize sentiment analysis pipeline | |
| sentiment_analyzer = pipeline("sentiment-analysis") | |
| def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7): | |
| return HuggingFaceEndpoint( | |
| repo_id=model_id, | |
| max_new_tokens=max_new_tokens, | |
| temperature=temperature, | |
| token=os.getenv("HF_TOKEN") # Hugging Face API Token | |
| ) | |
| def get_nasa_apod(): | |
| url = f"https://api.nasa.gov/planetary/apod?api_key={NASA_API_KEY}" | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| 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. Please try again later." | |
| def analyze_sentiment(user_text): | |
| result = sentiment_analyzer(user_text)[0] | |
| return result['label'] | |
| def predict_action(user_text): | |
| """ | |
| Determines the topic of the user's message. | |
| """ | |
| if "NASA" in user_text or "space" in user_text: | |
| return "nasa_info" | |
| elif "quark" in user_text or "physics" in user_text or "quantum" in user_text: | |
| return "physics" | |
| elif "AI" in user_text or "machine learning" in user_text: | |
| return "AI" | |
| else: | |
| return "general_query" | |
| def generate_follow_up(user_text): | |
| """ | |
| Generates a concise and conversational follow-up question related to the user's input. | |
| """ | |
| prompt_text = ( | |
| f"Given the user's question: '{user_text}', generate a single friendly follow-up question. " | |
| "Make it short, conversational, and natural—like a human would ask. " | |
| "Example: If the user asks 'What is a quark?', respond with something like " | |
| "'Would you like to learn about the six types of quarks?' " | |
| "Do NOT include phrases like 'A natural follow-up question could be'." | |
| ) | |
| hf = get_llm_hf_inference(max_new_tokens=32, temperature=0.7) | |
| return hf.invoke(input=prompt_text).strip() | |
| def get_response(system_message, chat_history, user_text, max_new_tokens=256): | |
| """ | |
| Generates HAL's response and follow-up, ensuring no duplicate queries or misplaced follow-ups. | |
| """ | |
| sentiment = analyze_sentiment(user_text) | |
| action = predict_action(user_text) | |
| 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': 'assistant', 'content': response}) | |
| follow_up = generate_follow_up(user_text) | |
| return response, follow_up, chat_history, nasa_url | |
| hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9) | |
| prompt = PromptTemplate.from_template( | |
| "[INST] {system_message}\n\n" | |
| "Current Conversation:\n{chat_history}\n\n" | |
| "User: {user_text}.\n [/INST]\n" | |
| "AI: Keep responses conversational and engaging. Start with a friendly phrase like " | |
| "'Certainly!', 'Of course!', or 'Great question!' before answering." | |
| " Keep responses concise but engaging.\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=chat_history)) | |
| response = response.split("HAL:")[-1].strip() | |
| # ✅ Avoid duplicate user messages in history | |
| if not chat_history or chat_history[-1]["content"] != user_text: | |
| chat_history.append({'role': 'user', 'content': user_text}) | |
| chat_history.append({'role': 'assistant', 'content': response}) | |
| # ✅ Avoid repeating follow-ups when topic changes | |
| current_topic = action | |
| if current_topic != st.session_state.last_topic: | |
| st.session_state.follow_up = "" | |
| else: | |
| follow_up = generate_follow_up(user_text) | |
| chat_history.append({'role': 'assistant', 'content': follow_up}) | |
| st.session_state.follow_up = follow_up | |
| st.session_state.last_topic = current_topic | |
| return response, st.session_state.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.session_state.last_topic = "" | |
| st.rerun() | |
| # --- Chat History Display --- | |
| 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) | |
| # --- Single Input Box for Both Initial and Follow-Up Messages --- | |
| user_input = st.chat_input("Type your message here...") | |
| if user_input: | |
| # ✅ Prevent duplicate user messages | |
| if not st.session_state.chat_history or st.session_state.chat_history[-1]["content"] != user_input: | |
| st.session_state.chat_history.append({'role': 'user', 'content': user_input}) | |
| # Generate HAL's 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 | |
| ) | |
| st.session_state.chat_history.append({'role': 'assistant', 'content': 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 | |
| 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 | |