app.py

import streamlit as st
import pandas as pd
from transformers import pipeline
import io

# Initialize the Hugging Face model pipeline (e.g., sentiment analysis, question answering, etc.)
# Replace this with the model that fits your query processing needs
model = pipeline("text-classification")  # Example for a text classification task

def process_query(query, dataframe):
    """
    This function processes the user query with the Hugging Face model and returns the result.
    You can adapt this based on your specific query processing.
    """
    # For simplicity, let's assume we're running text classification on a text column in the dataframe.
    # This part will change depending on your use case.
    results = []
    for index, row in dataframe.iterrows():
        result = model(row['text_column'])  # Example, modify 'text_column' to your actual column name
        results.append(result[0]['label'])
    
    dataframe['query_result'] = results
    return dataframe

def handle_file_upload():
    """
    Function to handle multiple file uploads.
    """
    uploaded_files = st.file_uploader("Upload multiple Excel files", type=["xlsx"], accept_multiple_files=True)

    if uploaded_files:
        return uploaded_files
    return None

def main():
    st.title("Excel Query Processing Application")
    
    # Step 1: File upload section
    uploaded_files = handle_file_upload()
    
    if uploaded_files:
        # Step 2: Process each uploaded Excel file
        for file in uploaded_files:
            # Read the Excel file into a DataFrame
            df = pd.read_excel(file)
            st.write(f"Data from {file.name}:")
            st.write(df.head())  # Show a preview of the data
            
            # Step 3: Get user query input
            query = st.text_input("Enter your query to process the file:", "")
            
            if query:
                # Step 4: Process the query on the data
                result_df = process_query(query, df)
                
                # Step 5: Display the processed result
                st.write("Processed Result:")
                st.write(result_df.head())  # Show a preview of the result
                
                # Step 6: Provide an option to download the processed file
                output = io.BytesIO()
                result_df.to_excel(output, index=False)
                output.seek(0)
                
                st.download_button(
                    label="Download Processed Excel",
                    data=output,
                    file_name=f"processed_{file.name}",
                    mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                )

if __name__ == "__main__":
    main()