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

# Load environment variables from .env file
load_dotenv()

# Ensure API key is loaded correctly
api_key = os.getenv("HUGGINGFACE_API_KEY")
if api_key is None:
    raise ValueError("Hugging Face API key is not set. Please add it as a secret in your Hugging Face Space settings.")
print(f"Hugging Face API Key: {api_key}")

async def process_csv(file):
    print("Reading CSV file...")
    df = pd.read_csv(file, header=None)  # Read the CSV file without a header
    print("CSV file read successfully.")
    
    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
    print(f"Model name: {model_name}")
    print(f"Processing {SAMPLE_SIZE} descriptions out of {len(descriptions)} total descriptions.")

    results = []
    for i, desc in enumerate(descriptions_subset):
        print(f"Processing description {i+1}/{SAMPLE_SIZE}...")
        result = await process_text_local(model_name, desc)
        print(f"Description {i+1} processed. Result: {result[:50]}...")  # Print first 50 characters of the result
        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))
    
    print("Assigning results to DataFrame...")
    df['predictions'] = results
    df.columns = df.columns.astype(str)  # Convert column names to strings to avoid warnings
    print("Results assigned to DataFrame successfully.")
    print(df.head())  # Print first few rows of the DataFrame to verify
    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..."):
            print("Starting CSV processing...")
            df = asyncio.run(process_csv(uploaded_file))
            print("CSV processing completed. Displaying results.")
            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'
            )
            print("Results displayed and download button created.")