Update app.py

app.py

@@ -1,48 +1,46 @@
 import pandas as pd
 import gradio as gr
 
+# Core conversion logic
 def convert_schedule(file, direction):
-    # Read the uploaded Excel file; use the first column as index
-    df = pd.read_excel(file.name if hasattr(file, 'name') else file, index_col=0)
+    # Read the uploaded Excel file (first sheet) with first column as index
+    df = pd.read_excel(file.name, index_col=0)
 
     if direction == "A to B":
-        # A->B: models in rows, texters in cells -> texters as rows, models as cell values
-        df_reset = df.reset_index()
-        model_col = df_reset.columns[0]
-        df_melt = df_reset.melt(id_vars=[model_col], var_name='Header', value_name='Texter')
-        df_melt = df_melt.dropna(subset=['Texter'])
-        df_pivot = df_melt.pivot(index='Texter', columns='Header', values=model_col)
-        df_pivot = df_pivot.reindex(columns=df.columns).fillna("")
-        result = df_pivot
+        # Models in rows → Texters as rows
+        df_reset = df.reset_index().rename(columns={df.index.name or df.columns[0]: "Model"})
+        df_melt = df_reset.melt(id_vars=["Model"], var_name="Header", value_name="Texter")
+        df_melt = df_melt.dropna(subset=["Texter"])
+        # Use pivot_table to handle duplicate entries gracefully
+        pivot = df_melt.pivot_table(
+            index="Texter", columns="Header", values="Model", aggfunc="first"
+        )
+        result = pivot.reindex(columns=df.columns).fillna("")
     else:
-        # B->A: texters in rows, models in cells -> models as rows, texters as cell values
-        df_reset = df.reset_index()
-        texter_col = df_reset.columns[0]
-        df_melt = df_reset.melt(id_vars=[texter_col], var_name='Header', value_name='Model')
-        df_melt = df_melt.dropna(subset=['Model'])
-        df_pivot = df_melt.pivot(index='Model', columns='Header', values=texter_col)
-        df_pivot = df_pivot.reindex(columns=df.columns).fillna("")
-        result = df_pivot
+        # Texters in rows → Models as rows
+        df_reset = df.reset_index().rename(columns={df.index.name or df.columns[0]: "Texter"})
+        df_melt = df_reset.melt(id_vars=["Texter"], var_name="Header", value_name="Model")
+        df_melt = df_melt.dropna(subset=["Model"])
+        # Use pivot_table to handle duplicate entries gracefully
+        pivot = df_melt.pivot_table(
+            index="Model", columns="Header", values="Texter", aggfunc="first"
+        )
+        result = pivot.reindex(columns=df.columns).fillna("")
 
-    # Save to Excel for download
-    out_file = "converted_schedule.xlsx"
-    result.to_excel(out_file)
-    return result, out_file
+    return result
 
 # Build Gradio interface
 iface = gr.Interface(
     fn=convert_schedule,
     inputs=[
-        gr.File(label="Upload Schedule Excel (Format A or B)"),
+        gr.File(label="Upload Schedule (.xlsx)", file_count="single", type="file"),
         gr.Radio(["A to B", "B to A"], label="Conversion Direction")
     ],
-    outputs=[
-        gr.Dataframe(label="Converted Schedule"),
-        gr.File(label="Download Converted Excel")
-    ],
-    title="Schedule Format Converter",
-    description="Dynamically convert between two schedule formats: models-as-rows ↔ texters-as-rows."
+    outputs=gr.Dataframe(label="Converted Schedule"),
+    title="Dynamic Schedule Converter",
+    description="Upload an Excel file in Format A or B and convert between models-as-rows ↔ texters-as-rows."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    # Ensure compatibility with Hugging Face Spaces
+    iface.launch(server_name="0.0.0.0", server_port=7860)