Update app.py

app.py

@@ -1,114 +1,150 @@
 import pandas as pd
-import openpyxl  # ensure XLSX engine is available
+import openpyxl
 import gradio as gr
-import uuid
+import io
 import os
-
-# 7-Day Schedule Converter with explicit iteration and header fill and download support
+import tempfile
+from datetime import datetime
 
 def convert_schedule(file_path, direction):
-    # 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)
+    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)
 
-    # 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]
-                for texter in str(cell).split(','):
-                    texter = texter.strip()
-                    if not texter or texter.lower() in ['nan', 'none']:
+        # 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):
                         continue
-                    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']:
+                    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):
                         continue
-                    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'
+                    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'
 
-    # 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. Save to Excel for download (relative path so Gradio can serve it)
-    output_filename = f"converted_{uuid.uuid4().hex}.xlsx"
-    display_df.to_excel(output_filename, engine='openpyxl', index=False)
+        # 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)
 
-    # Return both DataFrame and download path
-    return display_df, output_filename
+        # 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)
 
-# Build Gradio interface and launch immediately for Hugging Face Spaces
+        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
 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')
+        gr.Radio(['A to B', 'B to A'], label='Convert Direction', value='A to B')
     ],
     outputs=[
-        gr.Dataframe(label='Converted Schedule'),
-        gr.File(label='Download Converted Excel')
+        gr.Dataframe(label='Converted Schedule (Preview)'),
+        gr.File(label='Download Converted Schedule (.xlsx)')
     ],
     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. Download the result as .xlsx.'
+        'then flip between Models→Texters or Texters→Models. '
+        'The converted file will be available for download as an Excel file.'
     ),
     flagging_mode='never'
 )
-iface.launch(server_name='0.0.0.0', server_port=7860)
+
+if __name__ == "__main__":
+    iface.launch(server_name='0.0.0.0', server_port=7860)