process.py

import pandas as pd
import gradio as gr

def find_postcode_column(df):
    # UK Gov postcode regex
    postcode_pattern = r"([Gg][Ii][Rr] 0[Aa]{2})|((([A-Za-z][0-9]{1,2})|(([A-Za-z][A-Ha-hJ-Yj-y][0-9]{1,2})|(([A-Za-z][0-9][A-Za-z])|([A-Za-z][A-Ha-hJ-Yj-y][0-9][A-Za-z]?))))\s?[0-9][A-Za-z]{2})" 
    max_count = 0
    postcode_column = None

    for column in df.columns:
        # Count matches of the postcode pattern in each column
        matches = df[column].astype(str).str.match(postcode_pattern)
        valid_count = matches.sum()  # Sum of True values indicating valid postcodes

        # Select the column with the maximum count of valid postcodes
        if valid_count > max_count:
            max_count = valid_count
            postcode_column = column

    return postcode_column

def get_lat_lon(postcodes_df, postcode_mapping):
    try:
        # Attempt to identify the postcode column dynamically
        postcode_column = find_postcode_column(postcodes_df)
        if not postcode_column:
            raise ValueError("No valid postcode column found")

        # Rename columns for consistency
        postcode_mapping.rename(columns={'postcode': 'Postal code'}, inplace=True)

        # Normalize postcodes to ensure matching and count occurrences
        postcodes_df[postcode_column] = postcodes_df[postcode_column].str.lower().str.replace(' ', '')
        postcode_counts = postcodes_df[postcode_column].value_counts().reset_index()
        postcode_counts.columns = ['Postal code', 'count']
        
        # Normalize the postcodes in the mapping DataFrame
        postcode_mapping['Postal code'] = postcode_mapping['Postal code'].str.lower().str.replace(' ', '')
        
        # Merge the counts with the mapping data
        result_df = pd.merge(postcode_counts, postcode_mapping, on='Postal code', how='left')
        
        # Fill NaN values for latitude and longitude where postcode was not found in the mapping
        result_df['latitude'] = result_df['latitude'].fillna('')
        result_df['longitude'] = result_df['longitude'].fillna('')
        
        # Optionally, convert the DataFrame to a dictionary if needed, or work directly with the DataFrame
        results = result_df.to_dict(orient='records')

    except Exception as e:
        raise Exception("Error processing postal codes: " + str(e))
    
    return results