Update app.py

app.py

@@ -1,150 +1,115 @@
 import pandas as pd
-import openpyxl
+import openpyxl  # ensure XLSX engine is available
 import gradio as gr
-import io
+import uuid
 import os
-import tempfile
-from datetime import datetime
+
+# 7-Day Schedule Converter with explicit iteration and header fill and download support
 
 def convert_schedule(file_path, direction):
-    try:
-        # 1. Load raw header rows to determine day labels
-        raw = pd.read_excel(file_path, header=None)
-        header1 = raw.iloc[0, 1:].astype(object)
-        header2 = raw.iloc[1, 1:].astype(object)
+    # 1. Load raw header rows to determine day labels
+    raw = pd.read_excel(file_path, header=None)
+    header1 = raw.iloc[0, 1:].astype(object)
+    header2 = raw.iloc[1, 1:].astype(object)
 
-        # Decide which header row to use: prefer second if fully populated
-        if header2.notna().all() and not header2.str.startswith('Unnamed').any():
-            days = header2.tolist()
-            data_start = 2
-        else:
-            # Forward-fill merged first-row headers
-            days = []
-            last = None
-            for val in header1:
-                if pd.isna(val) or str(val).startswith('Unnamed'):
-                    days.append(last)
-                else:
-                    last = str(val)
-                    days.append(last)
-            data_start = 1
+    # Decide which header row to use: prefer second if fully populated
+    if header2.notna().all() and not header2.str.startswith('Unnamed').any():
+        days = header2.tolist()
+        data_start = 2
+    else:
+        # Forward-fill merged first-row headers
+        days = []
+        last = None
+        for val in header1:
+            if pd.isna(val) or str(val).startswith('Unnamed'):
+                days.append(last)
+            else:
+                last = str(val)
+                days.append(last)
+        data_start = 1
 
-        # 2. Load actual data using resolved day columns
-        df = pd.read_excel(
-            file_path,
-            header=data_start,
-            index_col=0,
-            usecols=[0] + list(range(1, len(days) + 1))
-        )
-        df.columns = [str(day) for day in days]
+    # 2. Load actual data using resolved day columns
+    df = pd.read_excel(
+        file_path,
+        header=data_start,
+        index_col=0,
+        usecols=[0] + list(range(1, len(days) + 1))
+    )
+    df.columns = [str(day) for day in days]
 
-        # 3. Retain original day column order
-        day_cols = list(df.columns)
+    # 3. Retain original day column order
+    day_cols = list(df.columns)
 
-        # 4. Build assignment mapping via explicit iteration
-        assignments = {}
-        if direction == 'A to B':
-            # Models in rows → Texters as rows
-            for model in df.index.astype(str):
-                for day in day_cols:
-                    cell = df.at[model, day]
-                    if pd.isna(cell):
+    # 4. Build assignment mapping via explicit iteration
+    assignments = {}
+    if direction == 'A to B':
+        # Models in rows → Texters as rows
+        for model in df.index.astype(str):
+            for day in day_cols:
+                cell = df.at[model, day]
+                for texter in str(cell).split(','):
+                    texter = texter.strip()
+                    if not texter or texter.lower() in ['nan', 'none']:
                         continue
-                    for texter in str(cell).split(','):
-                        texter = texter.strip()
-                        if not texter or texter.lower() in ['nan', 'none', '']:
-                            continue
-                        assignments.setdefault(texter, {d: [] for d in day_cols})
-                        assignments[texter][day].append(model)
-            
-            if not assignments:
-                result = pd.DataFrame(columns=day_cols)
-                first_col_name = 'Texter'
-            else:
-                index = sorted(assignments.keys())
-                result = pd.DataFrame(index=index, columns=day_cols)
-                first_col_name = 'Texter'
-                for texter, days_map in assignments.items():
-                    for day in day_cols:
-                        models = days_map.get(day, [])
-                        result.at[texter, day] = ', '.join(models) if models else 'OFF'
-        else:
-            # Texters in rows → Models as rows
-            for texter in df.index.astype(str):
-                for day in day_cols:
-                    cell = df.at[texter, day]
-                    if pd.isna(cell):
+                    assignments.setdefault(texter, {d: [] for d in day_cols})
+                    assignments[texter][day].append(model)
+        index = sorted(assignments.keys())
+        result = pd.DataFrame(index=index, columns=day_cols)
+        first_col_name = 'Texter'
+        for texter, days_map in assignments.items():
+            for day in day_cols:
+                models = days_map.get(day, [])
+                result.at[texter, day] = ', '.join(models) if models else 'OFF'
+    else:
+        # Texters in rows → Models as rows
+        for texter in df.index.astype(str):
+            for day in day_cols:
+                cell = df.at[texter, day]
+                for model in str(cell).split(','):
+                    model = model.strip()
+                    if not model or model.lower() in ['nan', 'none']:
                         continue
-                    for model in str(cell).split(','):
-                        model = model.strip()
-                        if not model or model.lower() in ['nan', 'none', '']:
-                            continue
-                        assignments.setdefault(model, {d: [] for d in day_cols})
-                        assignments[model][day].append(texter)
-            
-            if not assignments:
-                result = pd.DataFrame(columns=day_cols)
-                first_col_name = 'Model'
-            else:
-                index = sorted(assignments.keys())
-                result = pd.DataFrame(index=index, columns=day_cols)
-                first_col_name = 'Model'
-                for model, days_map in assignments.items():
-                    for day in day_cols:
-                        texters = days_map.get(day, [])
-                        result.at[model, day] = ', '.join(texters) if texters else 'OFF'
+                    assignments.setdefault(model, {d: [] for d in day_cols})
+                    assignments[model][day].append(texter)
+        index = sorted(assignments.keys())
+        result = pd.DataFrame(index=index, columns=day_cols)
+        first_col_name = 'Model'
+        for model, days_map in assignments.items():
+            for day in day_cols:
+                texters = days_map.get(day, [])
+                result.at[model, day] = ', '.join(texters) if texters else 'OFF'
 
-        # 5. Cleanup axis names
-        result.index.name = None
-        result.columns.name = None
+    # 5. Cleanup axis names
+    result.index.name = None
+    result.columns.name = None
 
         # For display, include index as a column
-        display_df = result.reset_index().rename(columns={'index': first_col_name})
+    display_df = result.reset_index().rename(columns={'index': first_col_name})
 
-        # 6. Create downloadable file
-        result_clean = result.copy().fillna('OFF')
-        
-        # Ensure all values are strings
-        for col in result_clean.columns:
-            result_clean[col] = result_clean[col].astype(str)
+        # 6. Save to Excel for download in the working directory
+    output_filename = f"converted_{uuid.uuid4().hex}.xlsx"
+    output_path = os.path.join(os.getcwd(), output_filename)
+    display_df.to_excel(output_path, engine='openpyxl', index=False)
 
-        # Create a temporary file for download
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        temp_dir = tempfile.gettempdir()
-        output_filename = f"converted_schedule_{timestamp}.xlsx"
-        output_path = os.path.join(temp_dir, output_filename)
-        
-        # Save to Excel file
-        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
-            # Reset index to include the first column name
-            download_df = result_clean.reset_index().rename(columns={'index': first_col_name})
-            download_df.to_excel(writer, sheet_name='Converted Schedule', index=False)
+    # Return both DataFrame and absolute download path
+    return display_df, output_path
 
-        return display_df, output_path
-
-    except Exception as e:
-        error_df = pd.DataFrame({'Error': [f"Error processing file: {str(e)}"]})
-        return error_df, None
-
-# Gradio interface with proper file download
+# Build Gradio interface and launch immediately for Hugging Face Spaces
 iface = gr.Interface(
     fn=convert_schedule,
     inputs=[
         gr.File(label='Upload Weekly Schedule (.xlsx)', file_count='single', type='filepath'),
-        gr.Radio(['A to B', 'B to A'], label='Convert Direction', value='A to B')
+        gr.Radio(['A to B', 'B to A'], label='Convert Direction')
     ],
     outputs=[
-        gr.Dataframe(label='Converted Schedule (Preview)'),
-        gr.File(label='Download Converted Schedule (.xlsx)')
+        gr.Dataframe(label='Converted Schedule'),
+        gr.File(label='Download Converted Excel')
     ],
     title='7-Day Schedule Converter',
     description=(
         'Upload a 7-column weekly schedule (Models vs Days) with merged or single headers, '
-        'then flip between Models→Texters or Texters→Models. '
-        'The converted file will be available for download as an Excel file.'
+        'then flip between Models→Texters or Texters→Models. Download the result as .xlsx.'
     ),
     flagging_mode='never'
 )
-
-if __name__ == "__main__":
-    iface.launch(server_name='0.0.0.0', server_port=7860)
+iface.launch(server_name='0.0.0.0', server_port=7860)