app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM

@st.cache_resource
def load_model():
    model_dir = "./"  # Ensure all files, including `vocab.txt`, are in this directory
    tokenizer = AutoTokenizer.from_pretrained(model_dir)
    model = AutoModelForCausalLM.from_pretrained(model_dir)
    return tokenizer, model

st.set_page_config(page_title="Legal AI Chatbot", layout="centered")
st.title("Legal AI Chatbot")

st.write("Interact with a legal AI chatbot powered by transformers.")

# Load tokenizer and model
tokenizer, model = load_model()

# User input for chatbot
user_input = st.text_input("Enter your legal query:")
if user_input:
    inputs = tokenizer(user_input, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"], max_length=150)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    st.text_area("Response:", response, height=200)