Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import tempfile
+import os
+from openpyxl import load_workbook
+from openpyxl.styles import Alignment
+
+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):
+    try:
+        # Read the Excel file, skipping the first row (OVERNIGHT/MORNING header)
+        input_df = pd.read_excel(input_file.name, 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'})
+        
+        return final_df
+    except Exception as e:
+        return pd.DataFrame({"Error": [str(e)]})
+
+def process_file_b_to_a(input_file):
+    try:
+        # Read the Excel file
+        input_df = pd.read_excel(input_file.name, 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 alphabetically
+        final_df = pivoted.sort_index()
+        
+        # Fill empty cells with 'OFF'
+        final_df = final_df.fillna('OFF')
+        
+        # Reset index to make MODEL a column
+        final_df = final_df.reset_index()
+        
+        return final_df
+    except Exception as e:
+        return pd.DataFrame({"Error": [str(e)]})
+
+def convert_schedule(file, direction):
+    if direction == "Format A → Format B":
+        df = process_file_a_to_b(file)
+    else:
+        df = process_file_b_to_a(file)
+    # Save to temp file for download
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
+        df.to_excel(tmp.name, index=False)
+        adjust_excel_formatting(tmp.name)
+        tmp.seek(0)
+        data = tmp.read()
+    return df, (tmp.name,)
+
+def download_file(file_tuple):
+    return file_tuple[0]
+
+demo = gr.Blocks()
+with demo:
+    gr.Markdown("# 📅 Schedule Converter")
+    gr.Markdown("Upload your schedule Excel file, select conversion direction, and download the result.")
+    with gr.Row():
+        file = gr.File(label="Upload Schedule File", type="file")
+        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 = convert_schedule(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=[file, direction],
+        outputs=[output_table, temp_file_path, download_button]
+    )
+    reset_btn.click(
+        reset_components,
+        outputs=[file, output_table, temp_file_path, download_button]
+    )
+    download_button.click(
+        download_file,
+        inputs=temp_file_path,
+        outputs=gr.File(label="Processed Schedule")
+    )
+
+demo.launch()