# app.py
import streamlit as st
from transformers import AutoTokenizer, AutoModel

# Load the model and tokenizer
@st.cache_resource
import streamlit as st
from transformers import AutoTokenizer, AutoModel

@st.cache_resource
def load_model():
    model_name = "mradermacher/Indian_Legal_Assistant-GGUF"
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
    model = AutoModel.from_pretrained(model_name, use_auth_token=token)
    return tokenizer, model

tokenizer, model = load_model()


tokenizer, model = load_model()

# Streamlit App Layout
st.title("Indian Legal Assistant - Hugging Face Spaces Deployment")
st.write("This app provides answers to legal questions using the Indian Legal Assistant model.")

# User input for a legal query
user_input = st.text_area("Enter your legal question:")

if st.button("Generate Response"):
    if user_input:
        # Tokenize the input
        inputs = tokenizer(user_input, return_tensors="pt")

        # Generate response
        outputs = model.generate(**inputs, max_length=150)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Display the response
        st.write("### Response:")
        st.write(response)
    else:
        st.write("Please enter a question to get a response.")