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

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