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()

# Ensure API key is loaded correctly
api_key = os.getenv("HUGGINGFACE_API_KEY")
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(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.")