app.py

# hal_bot.py

import os
import re
import requests
import torch
import streamlit as st
from langchain_community.llms import HuggingFaceEndpoint
from langchain.llms import HuggingFacePipeline
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langdetect import detect

# ✅ Switched to Flan-T5 Model
MODEL_ID = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)


# ✅ Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"✅ Using device: {device}")

# ✅ Environment Variables
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")

NASA_API_KEY = os.getenv("NASA_API_KEY")
if NASA_API_KEY is None:
    raise ValueError("NASA_API_KEY is not set. Please add it to your environment variables.")

# ✅ Streamlit Setup
st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")

if "chat_history" not in st.session_state:
    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]

def load_local_llm(model_id):
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
    return pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)




llm = HuggingFacePipeline(pipeline=pipe)

def get_llm_hf_inference(model_id=MODEL_ID, max_new_tokens=500, temperature=0.3):
    return HuggingFaceEndpoint(
        repo_id=model_id,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        token=HF_TOKEN,
        task="text2text-generation",
        device=-1 if device == "cpu" else 0
    )

def ensure_english(text):
    try:
        detected_lang = detect(text)
        if detected_lang != "en":
            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
    except:
        return "⚠️ Language detection failed. Please ask your question again."
    return text

def get_response(system_message, chat_history, user_text, max_new_tokens=500):
    filtered_history = "\n".join(
        f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_history[-5:]
    )

    prompt = PromptTemplate.from_template(
        """
        You are a helpful NASA AI assistant.
        Answer concisely and clearly based on the conversation history and the user's latest message.

        Conversation History:
        {chat_history}

        User: {user_text}
        Assistant:
        """
    )

    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.3)
    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.strip()
    response = ensure_english(response)

    if not response:
        response = "I'm sorry, but I couldn't generate a response. Can you rephrase your question?"

    chat_history.append({'role': 'user', 'content': user_text})
    chat_history.append({'role': 'assistant', 'content': response})

    return response, chat_history[-10:]

st.title("🚀 HAL - NASA AI Assistant")

st.markdown("""
    <style>
    .user-msg, .assistant-msg {
        padding: 11px;
        border-radius: 10px;
        margin-bottom: 5px;
        width: fit-content;
        max-width: 80%;
        text-align: justify;
    }
    .user-msg { background-color: #696969; color: white; }
    .assistant-msg { background-color: #333333; color: white; }
    .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)

user_input = st.chat_input("Type your message here...")

if user_input:
    response, st.session_state.chat_history = get_response(
        system_message="You are a helpful AI assistant.",
        user_text=user_input,
        chat_history=st.session_state.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)