app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model from local files
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("./", config="config.json")
    model = AutoModelForCausalLM.from_pretrained("./")
    return tokenizer, model

# Initialize the model and tokenizer
tokenizer, model = load_model()

# Set up Streamlit page configuration
st.set_page_config(page_title="Legal AI Chatbot", layout="centered")

st.title("Legal AI Chatbot")
st.write("This chatbot provides responses based on a legal language model.")

# User input
user_input = st.text_input("Enter your query:")

if user_input:
    # Tokenize and generate response
    inputs = tokenizer.encode(user_input, return_tensors="pt")
    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
    
    # Decode and display the output
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    st.text_area("Response:", response, height=200)