import gradio as gr
import pandas as pd
import tempfile
import os
import requests
from openpyxl import load_workbook
from openpyxl.styles import Alignment

def auto_correct_names(series, threshold=90):
    try:
        from fuzzywuzzy import process, fuzz
    except ImportError:
        return series  # Fallback if fuzzywuzzy is not installed
    unique_names = series.dropna().unique()
    name_mapping = {}
    for name in unique_names:
        matches = process.extractBests(
            name, unique_names, 
            scorer=fuzz.token_sort_ratio, 
            score_cutoff=threshold
        )
        if matches:
            best_match = max(matches, key=lambda x: (x[1], list(series).count(x[0])))
            name_mapping[name] = best_match[0]
    return series.replace(name_mapping)

def adjust_excel_formatting(file_path):
    wb = load_workbook(file_path)
    ws = wb.active
    for col in ws.columns:
        max_length = 0
        col_letter = col[0].column_letter
        for cell in col:
            if cell.value:
                max_length = max(max_length, len(str(cell.value)))
            cell.alignment = Alignment(wrap_text=True)
        ws.column_dimensions[col_letter].width = max_length + 2
    wb.save(file_path)

def process_file_a_to_b(input_file, output_file):
    try:
        # Read the Excel file, skipping the first row (OVERNIGHT/MORNING header)
        input_df = pd.read_excel(input_file, header=1)
        
        # Get the date columns (all columns except the first one which contains model names)
        date_columns = input_df.columns[1:].tolist()
        
        # Melt the dataframe to long format
        df_long = input_df.melt(
            id_vars=[input_df.columns[0]],  # First column (Model names)
            var_name='DATE',
            value_name='CHATTER'
        )
        
        # Clean up the data
        df_long = df_long[df_long['CHATTER'].notna()]  # Remove empty cells
        df_long = df_long[df_long['CHATTER'] != '']    # Remove empty strings
        df_long = df_long[df_long['CHATTER'] != 'OFF'] # Remove 'OFF' entries
        
        # Group by chatter and date, collect all models
        grouped = df_long.groupby(['CHATTER', 'DATE'])[input_df.columns[0]].apply(
            lambda x: ', '.join(sorted(x))
        ).reset_index()
        
        # Pivot to get chatters as rows and dates as columns
        pivoted = grouped.pivot(
            index='CHATTER',
            columns='DATE',
            values=input_df.columns[0]
        )
        
        # Reorder columns to match original date order
        pivoted = pivoted[date_columns]
        
        # Define the expected order of chatters
        expected_chatters = [
            'VELJKO2', 'VELJKO3', 'MARKO', 'GODDARD', 'ALEKSANDER', 'FEELIP', 
            'DENIS', 'TOME', 'MILA', 'VELJKO', 'DAMJAN', 'DULE', 'CONRAD', 
            'ALEXANDER', 'VEJKO3'
        ]
        
        # Reindex with expected order, keeping any additional chatters at the end
        final_df = pivoted.reindex(expected_chatters + [x for x in pivoted.index if x not in expected_chatters])
        
        # Fill empty cells with 'OFF'
        final_df = final_df.fillna('OFF')
        
        # Reset index and rename the index column to 'CHATTER'
        final_df = final_df.reset_index()
        final_df = final_df.rename(columns={'index': 'CHATTER'})
        
        # Try to save to Excel, with error handling
        try:
            final_df.to_excel(output_file, index=False)
            print(f"Saved Format B to {output_file}")
        except PermissionError:
            # If file is in use, try a different name
            base, ext = os.path.splitext(output_file)
            new_output = f"{base}_new{ext}"
            final_df.to_excel(new_output, index=False)
            print(f"Original file was in use. Saved Format B to {new_output}")
    except Exception as e:
        print(f"Error processing file: {str(e)}")
        raise

def process_file_b_to_a(input_file, output_file):
    try:
        # Read the Excel file
        input_df = pd.read_excel(input_file, header=0)
        
        # Get the date columns (all columns except the first one which contains chatter names)
        date_columns = input_df.columns[1:].tolist()
        
        # Melt the dataframe to long format
        df_long = input_df.melt(
            id_vars=[input_df.columns[0]],  # First column (Chatter names)
            var_name='DATE',
            value_name='MODEL'
        )
        
        # Clean up the data
        df_long = df_long[df_long['MODEL'].notna()]  # Remove empty cells
        df_long = df_long[df_long['MODEL'] != '']    # Remove empty strings
        df_long = df_long[df_long['MODEL'] != 'OFF'] # Remove 'OFF' entries
        
        # Split comma-separated models into separate rows
        df_long['MODEL'] = df_long['MODEL'].str.split(', ')
        df_long = df_long.explode('MODEL')
        
        # Group by model and date, collect all chatters
        grouped = df_long.groupby(['MODEL', 'DATE'])[input_df.columns[0]].apply(
            lambda x: ', '.join(sorted(x))
        ).reset_index()
        
        # Pivot to get models as rows and dates as columns
        pivoted = grouped.pivot(
            index='MODEL',
            columns='DATE',
            values=input_df.columns[0]
        )
        
        # Reorder columns to match original date order
        pivoted = pivoted[date_columns]
        
        # Sort models by frequency
        model_order = grouped['MODEL'].value_counts().index.tolist()
        final_df = pivoted.reindex(model_order)
        
        # Fill empty cells with 'OFF'
        final_df = final_df.fillna('OFF')
        
        # Reset index to make MODEL a column
        final_df = final_df.reset_index()
        
        # Try to save to Excel, with error handling
        try:
            final_df.to_excel(output_file, index=False)
            print(f"Saved Format A to {output_file}")
        except PermissionError:
            # If file is in use, try a different name
            base, ext = os.path.splitext(output_file)
            new_output = f"{base}_new{ext}"
            final_df.to_excel(new_output, index=False)
            print(f"Original file was in use. Saved Format A to {new_output}")
    except Exception as e:
        print(f"Error processing file: {str(e)}")
        raise

def save_and_format_excel(df, original_filename):
    name_part, ext_part = os.path.splitext(os.path.basename(original_filename))
    processed_filename = f"{name_part}_processed{ext_part}"
    temp_file_path = os.path.join(tempfile.gettempdir(), processed_filename)
    df.to_excel(temp_file_path, index=False, sheet_name='Schedule')
    adjust_excel_formatting(temp_file_path)
    return temp_file_path

def get_local_file_from_path_or_url(path_or_url):
    if path_or_url.startswith('http://') or path_or_url.startswith('https://'):
        # Download the file
        response = requests.get(path_or_url, stream=True)
        response.raise_for_status()
        suffix = os.path.splitext(path_or_url)[-1] or '.xlsx'
        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_file:
            for chunk in response.iter_content(chunk_size=8192):
                tmp_file.write(chunk)
            return tmp_file.name
    else:
        return path_or_url

def process_file(input_file, direction):
    try:
        if direction == "Format A → Format B":
            df = process_file_a_to_b(input_file, direction)
        else:
            df = process_file_b_to_a(input_file, direction)
        temp_file_path = save_and_format_excel(df, input_file)
        return df, temp_file_path
    except Exception as e:
        error_df = pd.DataFrame({"Error": [f"⚠️ {str(e)}"]})
        return error_df, None

def download_file(out_path):
    return out_path

with gr.Blocks(title="Schedule Converter") as demo:
    gr.Markdown("# 📅 Schedule Converter")
    gr.Markdown("Upload your schedule Excel file, select conversion direction, and download the result.")
    with gr.Row():
        input_file = gr.File(label="Upload Schedule File", type="filepath")
        direction = gr.Dropdown([
            "Format A → Format B",
            "Format B → Format A"
        ], value="Format A → Format B", label="Conversion Direction")
    with gr.Row():
        process_btn = gr.Button("Process File", variant="primary")
        reset_btn = gr.Button("Upload New File")
    output_table = gr.Dataframe(label="Preview", wrap=True)
    download_button = gr.Button("Download Processed File", visible=False)
    temp_file_path = gr.State(value=None)
    def reset_components():
        return [None, pd.DataFrame(), None, gr.update(visible=False)]
    def process_and_show(file, direction):
        df, out_path = process_file(file, direction)
        if out_path:
            return df, out_path, gr.update(visible=True)
        return df, None, gr.update(visible=False)
    process_btn.click(
        process_and_show,
        inputs=[input_file, direction],
        outputs=[output_table, temp_file_path, download_button]
    )
    reset_btn.click(
        reset_components,
        outputs=[input_file, output_table, temp_file_path, download_button]
    )
    download_button.click(
        download_file,
        inputs=temp_file_path,
        outputs=gr.File(label="Processed Schedule")
    )
if __name__ == "__main__":
    print("Enter the path or URL to your Excel file:")
    file_path_or_url = input().strip()
    print("Enter output file path (e.g., D:/work/formatter/schedule_a.xlsx):")
    output_path = input().strip()
    print("Select conversion direction:")
    print("1: Format A → Format B")
    print("2: Format B → Format A")
    direction_input = input().strip()
    direction = "Format A → Format B" if direction_input == "1" else "Format B → Format A"
    
    try:
        local_file = get_local_file_from_path_or_url(file_path_or_url)
        if direction == "Format A → Format B":
            process_file_a_to_b(local_file, output_path)
        else:
            process_file_b_to_a(local_file, output_path)
    except Exception as e:
        print(f"Error: {str(e)}")
        print("Please make sure:")
        print("1. The input file exists and is not open in Excel")
        print("2. The output file is not open in Excel")
        print("3. You have write permissions in the output directory")
    demo.launch()