app.py

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
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):
    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 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, 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}"
            "\nCurrent Conversation:\n{chat_history}\n\n"
            "\nUser: {user_text}.\n [/INST]"
            "\nAI: 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})

    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