app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login
import torch
import os


# Set model map
model_map = {
    "FinGPT": "AI4Finance/FinGPT",
    "FinanceConnect": "ceadar-ie/FinanceConnect-13B",
    "Sujet-Finance": "sujet-ai/Sujet-Finance-8B-v0.1"
}

# Cache model loading for performance
@st.cache_resource
def load_model_and_tokenizer(model_id):
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
    )
    return model, tokenizer

# Query model
def query_model(model_id, question):
    model, tokenizer = load_model_and_tokenizer(model_id)
    inputs = tokenizer(question, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=150)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Streamlit app layout
st.title("💼 Financial LLM Evaluation Interface")

model_choice = st.selectbox("Select a Financial Model", list(model_map.keys()))
user_question = st.text_area("Enter your financial question:", "What is EBITDA?")

if st.button("Get Response"):
    with st.spinner("Generating response..."):
        try:
            answer = query_model(model_map[model_choice], user_question)
            st.subheader(f"Response from {model_choice}:")
            st.write(answer)
        except Exception as e:
            st.error(f"Something went wrong: {e}")