import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "InvestmentResearchAI/LLM-ADE_tiny-v0.001"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_response(input_text):
    """Generate response from the model based on the input text."""
    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    output = model.generate(**inputs, max_length=512, num_return_sequences=1)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Streamlit interface
st.title("IRAI LLM-ADE Model")
user_input = st.text_area("Enter your text here:", "")
if st.button("Generate"):
    if user_input:
        response = generate_response(user_input)
        st.text_area("Model Response:", response, height=300)
    else:
        st.warning("Please enter some text to generate a response.")