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| import os | |
| import re | |
| import random | |
| import requests | |
| import streamlit as st | |
| import spacy # for additional NLP processing | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| from transformers import pipeline | |
| # Must be the first Streamlit command! | |
| st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀") | |
| # --- Appearance Section (optional) --- | |
| # (You can adjust CSS or appearance settings here if needed) | |
| # --- 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 = "" | |
| # --- Load spaCy Model for Additional NLP --- | |
| nlp_spacy = spacy.load("en_core_web_sm") | |
| def extract_context(text): | |
| """ | |
| Extract key entities from the text using spaCy to provide extra context. | |
| Returns a comma-separated string of entities (if any). | |
| """ | |
| doc = nlp_spacy(text) | |
| entities = [ent.text for ent in doc.ents] | |
| return ", ".join(entities) if entities else "" | |
| # --- Set Up Model & API Functions --- | |
| model_id = "mistralai/Mistral-7B-Instruct-v0.3" | |
| sentiment_analyzer = pipeline( | |
| "sentiment-analysis", | |
| model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", | |
| revision="714eb0f" | |
| ) | |
| 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"), | |
| task="text-generation" | |
| ) | |
| def get_nasa_apod(): | |
| 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() | |
| 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): | |
| if "nasa" in user_text.lower() or "space" in user_text.lower(): | |
| return "nasa_info" | |
| return "general_query" | |
| def generate_follow_up(user_text): | |
| """ | |
| Generates two variant follow-up questions and randomly selects one. | |
| Cleans up extraneous quotation marks. | |
| """ | |
| 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 '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() | |
| variants = re.split(r"\n|[;]+", output) | |
| cleaned = [v.strip(' "\'') for v in variants if v.strip()] | |
| 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 with a detailed explanation and a follow-up question. | |
| Uses sentiment analysis and extracts additional context from the user's text via spaCy. | |
| """ | |
| sentiment = analyze_sentiment(user_text) | |
| action = predict_action(user_text) | |
| # Extract additional context using spaCy | |
| context_info = extract_context(user_text) | |
| if context_info: | |
| context_clause = f" The key topics here are: {context_info}." | |
| else: | |
| context_clause = "" | |
| # 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: | |
| 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}." | |
| 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': 'user', 'content': user_text}) | |
| chat_history.append({'role': 'assistant', 'content': response}) | |
| 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 "" | |
| 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 that covers the topic thoroughly." | |
| + style_clause + context_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() | |
| 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 - Your NASA AI Assistant") | |
| st.markdown("🌌 *Ask me about space, NASA, and beyond!*") | |
| 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() | |
| # Render the chat history. | |
| 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...") | |
| if user_input: | |
| 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 | |
| ) | |
| 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 | |