Update app.py

app.py

@@ -4,57 +4,50 @@ import tempfile
 import os
 
 # ---------------------------------------------------
-# Auto column detection
+# Detect columns automatically
 # ---------------------------------------------------
 def detect_columns(df):
 
-    cols = [c.strip().lower() for c in df.columns]
+    name_col = None
+    dob_col = None
 
-    name_col, dob_col = None, None
-
-    for original, c in zip(df.columns, cols):
+    for col in df.columns:
+        c = col.strip().lower()
 
         if any(k in c for k in ["name", "student"]):
-            name_col = original
+            name_col = col
 
         if any(k in c for k in ["dob", "birth", "date"]):
-            dob_col = original
+            dob_col = col
 
     return name_col, dob_col
 
 
 # ---------------------------------------------------
-# Read file safely
+# Main Function
 # ---------------------------------------------------
-def read_file(uploaded_file):
-
-    if isinstance(uploaded_file, dict):
-        file_path = uploaded_file["name"]
-    else:
-        file_path = uploaded_file
+def group_by_birth_month(file_path):
 
-    if file_path.endswith((".xlsx", ".xls")):
-        return pd.read_excel(file_path)
-    else:
-        return pd.read_csv(file_path)
+    try:
+        if file_path is None:
+            return pd.DataFrame({"Error": ["Upload a file first"]}), None
 
+        # Read file
+        if file_path.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(file_path)
+        else:
+            df = pd.read_csv(file_path)
 
-# ---------------------------------------------------
-# Main Processing
-# ---------------------------------------------------
-def group_by_birth_month(uploaded_file):
-
-    try:
-        df = read_file(uploaded_file)
         df.columns = df.columns.str.strip()
 
         name_col, dob_col = detect_columns(df)
 
         if name_col is None or dob_col is None:
-            return pd.DataFrame(
-                {"Error": ["Name/DOB column not detected"]}
-            ), None
+            return pd.DataFrame({
+                "Error": ["Name/DOB column not detected"]
+            }), None
 
+        # Convert DOB
         df[dob_col] = pd.to_datetime(
             df[dob_col],
             errors="coerce",
@@ -83,7 +76,7 @@ def group_by_birth_month(uploaded_file):
 
         grouped = grouped.sort_values("Birth_Month")
 
-        # ✅ IMPORTANT FIX — create persistent file
+        # ✅ Create downloadable CSV
         output_path = os.path.join(
             tempfile.gettempdir(),
             "birth_month_grouped.csv"
@@ -91,39 +84,43 @@ def group_by_birth_month(uploaded_file):
 
         grouped.to_csv(output_path, index=False)
 
-        # Returning filepath ENABLES download button
-        return grouped, output_path
+        # ⭐ CRITICAL FIX: return as LIST
+        return grouped, [output_path]
 
     except Exception as e:
         return pd.DataFrame({"Error": [str(e)]}), None
 
 
 # ---------------------------------------------------
-# UI
+# Gradio UI
 # ---------------------------------------------------
 with gr.Blocks() as demo:
 
     gr.Markdown("""
-    # 🎂 Student Birth Month Grouping
-    Upload CSV/Excel containing Name and DOB columns.
+    # 🎂 Student Birth Month Grouping (HF Stable Version)
+
+    Upload CSV or Excel containing Name and Date of Birth.
     """)
 
     file_input = gr.File(
-        label="Upload Student List",
-        file_types=[".csv", ".xlsx", ".xls"],
-        type="filepath"
+        label="Upload Student File",
+        type="filepath",
+        file_types=[".csv", ".xlsx", ".xls"]
     )
 
     btn = gr.Button("Generate Month-wise List")
 
-    output_table = gr.Dataframe(label="Grouped Students")
+    table_output = gr.Dataframe(label="Grouped Students")
 
-    download_file = gr.File(label="⬇ Download Grouped CSV")
+    download_output = gr.File(
+        label="⬇ Download CSV Result",
+        file_count="single"
+    )
 
     btn.click(
         group_by_birth_month,
         inputs=file_input,
-        outputs=[output_table, download_file]
+        outputs=[table_output, download_output]
     )
 
 demo.launch()