app.py

import gradio as gr
import pandas as pd
import json
import functions  # Assuming functions.py has necessary imports and functions
import tempfile


def process_tables(table1_path, table2_path, params_file_path):
    # Load tables
    table1 = pd.read_excel(table1_path.name)
    table2 = pd.read_excel(table2_path.name)
    
    # Load parameters from the JSON file
    with open(params_file_path.name, 'r') as f:
        params = json.load(f)
    
    # Extract parameters
    harmonization = params.get("harmonization", True)
    columns_embeddings_col1 = params.get("columns_embeddings_col1", "Indicator Name")
    columns_embeddings_col2 = params.get("columns_embeddings_col2", "Indicator name (leonardo)")
    
    # Process column names into lists
    columns_embeddings_col1 = [col.strip() for col in columns_embeddings_col1.split(',')]
    columns_embeddings_col2 = [col.strip() for col in columns_embeddings_col2.split(',')]

    # Process the tables using a function from functions.py
    result_final = functions.process_similarity_results(
        table1=table1,
        table2=table2,
        columns_embeddings_col1=columns_embeddings_col1,
        columns_embeddings_col2=columns_embeddings_col2,
        harmonization=harmonization
    )

    # Save the result to a temporary CSV file
    result_path = tempfile.NamedTemporaryFile(suffix=".csv", delete=False).name
    result_final.to_csv(result_path, index=False)

    return result_path

# Define the Gradio interface
iface = gr.Interface(
    fn=process_tables,
    inputs=[
        gr.File(label="Upload Table 1 (Client Indicators or Framework Table)"),
        gr.File(label="Upload Table 2 (Internal Indicator or Indicator Table)"),
        gr.File(label="Upload Parameters File (JSON format)")
    ],
    outputs="file",
    description=(
        "Upload two tables and a JSON parameters file to process them. The parameters file should include: "
        "'harmonization' (True or False), 'columns_embeddings_col1' (comma-separated column names), "
        "and 'columns_embeddings_col2' (comma-separated column names)."
    )
)

iface.launch()