app.py

import os
import re
import random
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

# Must be the first Streamlit command!
st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")

# --- 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 = ""

# --- 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 one concise, friendly follow-up question related to the user's input.
    The prompt instructs the model to output a single question without extra commentary.
    """
    prompt_text = (
        f"Generate one concise, friendly follow-up question related to the topic of the user's question: '{user_text}'. "
        "The output should be only the question, with no extra text."
    )
    hf = get_llm_hf_inference(max_new_tokens=60, temperature=0.9)
    output = hf.invoke(input=prompt_text).strip()
    # If the output is too short or empty, return a default fallback question.
    if len(output) < 10:
        return "Would you like to explore this topic further?"
    # Clean the output from any extraneous quotes.
    follow_up = output.strip(' "\'')
    return follow_up

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.
    Style instructions (e.g. "in the voice of a physicist") are appended if present.
    """
    sentiment = analyze_sentiment(user_text)
    action = predict_action(user_text)
    
    # 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. "
            "Begin with a phrase like 'Certainly!', 'Of course!', or 'Great question!'." + style_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