app.py

import streamlit as st
import pandas as pd
import asyncio
from llama_models import process_text
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

async def process_csv(file):
    df = pd.read_csv(file, header=None)  # Read the CSV file without a header
    descriptions = df[0].tolist()  # Access the first column directly
    SAMPLE_SIZE = min(5, len(descriptions))  # Adjust sample size as needed
    descriptions_subset = descriptions[:SAMPLE_SIZE]

    model_name = "instruction-pretrain/finance-Llama3-8B"  # or any other model you want to use

    results = []
    for desc in descriptions_subset:
        result = await process_text(model_name, desc)
        results.append(result)
    
    # Fill the rest of the results with empty strings to match the length of the DataFrame
    results.extend([''] * (len(descriptions) - SAMPLE_SIZE))
    
    df['predictions'] = results
    return df

st.title("Finance Model Deployment")

st.write("""
### Upload a CSV file with company descriptions to extract key products, geographies, and important keywords:
""")

uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file is not None:
    if st.button("Predict"):
        with st.spinner("Processing..."):
            df = asyncio.run(process_csv(uploaded_file))
            st.write(df)
            st.download_button(
                label="Download Predictions as CSV",
                data=df.to_csv(index=False).encode('utf-8'),
                file_name='predictions.csv',
                mime='text/csv'
            )