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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
# --- 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",
revision="714eb0f"
)
def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
# Explicitly specify task="text-generation" so that the endpoint knows which task to run
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={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 or "space" in user_text:
return "nasa_info"
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 SHORT and SIMPLE follow-up question. "
"Make it conversational and friendly. Example: 'Would you like to learn more about the six types of quarks?' "
"Do NOT provide long explanations—just ask a friendly follow-up question."
)
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, making it more conversational and engaging.
"""
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': '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)
prompt = PromptTemplate.from_template(
(
"[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\nUser: {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()
chat_history.append({'role': 'user', 'content': user_text})
chat_history.append({'role': 'assistant', 'content': response})
if sentiment == "NEGATIVE":
response = "I'm here to help. Let me know what I can do for you. 😊"
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 {
background-color: #0078D7;
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)
# 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...") # Only ONE chat_input()
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
)
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
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