Update app.py

app.py

@@ -1,68 +1,92 @@
 import pandas as pd
 import gradio as gr
 
-# Pivot between Model↔Texter schedules (7-day weekly format), handling merged headers
+# Conversion by explicit iteration: map texters ↔ models per day
+# Handles merged headers, multi-row headers, and 'OFF' days
+
 def convert_schedule(file_path, direction):
-    # 1. Try reading with two header rows for merged timeslot/day layouts
+    # -- 1. Load Excel with potential two-row header for merged cells --
     try:
         df = pd.read_excel(file_path, header=[0,1], index_col=0)
-        # Extract the second-level headers (actual days)
-        second_level = df.columns.get_level_values(1).astype(str)
-        # If these are all unnamed, fallback
-        if all(lbl.startswith('Unnamed') for lbl in second_level):
+        # Use second header row as actual day labels
+        days = df.columns.get_level_values(1).astype(str)
+        # If all are unnamed, treat as single-header
+        if all(lbl.startswith('Unnamed') for lbl in days):
             raise ValueError
-        df.columns = second_level
+        df.columns = days
     except Exception:
-        # Fallback: single header row, simple day labels
         df = pd.read_excel(file_path, header=0, index_col=0)
         df.columns = df.columns.astype(str)
 
-    # 2. Drop any 'Unnamed' or blank header columns
-    df = df.loc[:, ~df.columns.str.match(r'^Unnamed')]
-
-    # 3. Determine id/val columns
-    if direction == 'A to B':
-        id_col, val_col = 'Model', 'Texter'
-    else:
-        id_col, val_col = 'Texter', 'Model'
+    # -- 2. Clean up: drop any phantom 'Unnamed' columns --
+    df = df.loc[:, ~df.columns.str.match(r'^Unnamed')]  # keep only real day columns
 
-    # 4. Melt into long format
-    df_reset = df.reset_index().rename(columns={df.index.name or df.columns.name or '': id_col})
-    df_melt = df_reset.melt(id_vars=[id_col], var_name='Day', value_name=val_col)
-    df_melt = df_melt.dropna(subset=[val_col])
+    # Order of days as in the original sheet
+    day_cols = list(df.columns)
 
-    # 5. Pivot table: join duplicates with comma
-    pivot = df_melt.pivot_table(
-        index=val_col,
-        columns='Day',
-        values=id_col,
-        aggfunc=lambda x: ', '.join(map(str, x))
-    )
-    pivot = pivot.fillna('')
+    # -- 3. Build assignment mapping --
+    # For A→B: iterate models (rows), assign each texter
+    # For B→A: iterate texters (rows), assign each model
+    assignments = {}
+    if direction == 'A to B':
+        for model in df.index:
+            for day in day_cols:
+                texters = df.at[model, day]
+                # split if multiple names in one cell
+                for texter in str(texters).split(','):
+                    texter = texter.strip()
+                    if not texter or texter.lower() in ['nan', 'none']:
+                        continue
+                    if texter not in assignments:
+                        # initialize all days as empty list
+                        assignments[texter] = {d: [] for d in day_cols}
+                    assignments[texter][day].append(str(model))
+        # After populating, convert lists to comma-joined or 'OFF'
+        index = sorted(assignments.keys())
+        result = pd.DataFrame(index=index, columns=day_cols)
+        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'
 
-    # 6. Reorder columns to match original
-    result = pivot.reindex(columns=df.columns.tolist(), fill_value='')
+    else:  # B→A
+        for texter in df.index:
+            for day in day_cols:
+                models = df.at[texter, day]
+                for model in str(models).split(','):
+                    model = model.strip()
+                    if not model or model.lower() in ['nan', 'none']:
+                        continue
+                    if model not in assignments:
+                        assignments[model] = {d: [] for d in day_cols}
+                    assignments[model][day].append(str(texter))
+        index = sorted(assignments.keys())
+        result = pd.DataFrame(index=index, columns=day_cols)
+        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'
 
-    # Cleanup axis names
+    # -- 4. Cleanup axis names --
     result.index.name = None
     result.columns.name = None
     return result
 
-# Build Gradio app
-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')
-    ],
-    outputs=gr.Dataframe(label='Converted Schedule'),
-    title='7-Day Schedule Pivot',
-    description=(
-        'Upload a 7-column weekly schedule (Models vs Days), then flip between '  
-        'Models→Texters or Texters→Models. Maintains original days order, even with merged headers.'
-    ),
-    allow_flagging='never'
-)
+# -- Gradio UI definition --
+def main():
+    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='Conversion Direction')
+        ],
+        outputs=gr.Dataframe(label='Converted Schedule'),
+        title='7-Day Schedule Converter',
+        description=('Upload a 7-column weekly schedule (Models vs Days) and flip ' 
+                     'between Models→Texters or Texters→Models with explicit iteration.'),
+        allow_flagging='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)
+    main()