Update app.py

app.py

@@ -18,7 +18,7 @@ def format_size(size_bytes):
         if size_bytes < 1024.0:
             return f"{size_bytes:.2f} {unit}"
         size_bytes /= 1024.0
-    return f"{size_bytes:.2f} PB" # Fallback
+    return f"{size_bytes:.2f} PB"
 
 def list_files(sort_by="Date (Newest)"):
     """Fetch files from the bucket, extract accurate metadata, and sort."""
@@ -28,7 +28,6 @@ def list_files(sort_by="Date (Newest)"):
         
         data = []
         for f in files:
-            # Skip directories
             if f.get("type") == "directory":
                 continue
                 
@@ -37,17 +36,21 @@ def list_files(sort_by="Date (Newest)"):
             size_str = format_size(raw_size)
             
             # --- AGGRESSIVE DATE EXTRACTION ---
-            # Different storage backends (like S3/Xet) use different dictionary keys. 
-            # We check all possible variations to ensure we catch the Upload Date.
             raw_date = None
-            date_keys = ["last_modified", "LastModified", "updated", "updatedAt", "client_modified", "created_at"]
             
+            # Prioritizing CREATED date keys as requested, falling back to modified keys
+            date_keys = [
+                "created", "created_at", "CreatedAt", "CreationDate", "ctime", "date_created",
+                "last_modified", "LastModified", "updated", "updatedAt", "client_modified"
+            ]
+            
+            # 1. Check the primary file metadata dictionary
             for k in date_keys:
                 if k in f and f[k] is not None:
                     raw_date = f[k]
                     break
             
-            # If missing, attempt to ping the file directly for deeper metadata
+            # 2. If still missing, ping info() directly for deeper hidden keys
             if raw_date is None:
                 try:
                     info = fs.info(f["name"])
@@ -67,15 +70,14 @@ def list_files(sort_by="Date (Newest)"):
             
         df = pd.DataFrame(data)
         if not df.empty:
-            # Safely parse dates, converting valid dates to datetime objects and invalid ones to NaT
-            dt_col = pd.to_datetime(df["_raw_date"], errors='coerce').dt.tz_localize(None)
+            # Safely parse dates. utc=True prevents timezone conflicts from turning valid dates into "Unknown" (NaT)
+            dt_col = pd.to_datetime(df["_raw_date"], errors='coerce', utc=True).dt.tz_localize(None)
             df["_raw_date"] = dt_col
             
             # Format display column, filling NaT back to "Unknown"
-            df["Last Modified"] = dt_col.dt.strftime('%Y-%m-%d %H:%M:%S').fillna("Unknown")
+            df["Date Created"] = dt_col.dt.strftime('%Y-%m-%d %H:%M:%S').fillna("Unknown")
             
             # --- EXPLICIT SORTING ---
-            # We sort against the raw hidden values, NOT the formatted strings
             if sort_by == "Name (A-Z)":
                 df = df.sort_values(by="File Name", ascending=True)
             elif sort_by == "Name (Z-A)":
@@ -91,14 +93,14 @@ def list_files(sort_by="Date (Newest)"):
                 
             # Clean up raw columns and enforce column order
             df = df.drop(columns=["_raw_date", "_raw_size"])
-            df = df[["File Name", "Last Modified", "Size"]]
+            df = df[["File Name", "Date Created", "Size"]]
             
         file_names = df["File Name"].tolist() if not df.empty else []
         return df, gr.update(choices=file_names)
         
     except Exception as e:
         print(f"Error fetching files: {e}")
-        return pd.DataFrame(columns=["File Name", "Last Modified", "Size"]), gr.update(choices=[])
+        return pd.DataFrame(columns=["File Name", "Date Created", "Size"]), gr.update(choices=[])
 
 def download_file(filename):
     """Download the selected file from the bucket to a temporary local file."""
@@ -137,9 +139,9 @@ with gr.Blocks() as app:
                 interactive=True
             )
             
-    # Interactive table
+    # Interactive table (Headers updated)
     file_table = gr.Dataframe(
-        headers=["File Name", "Last Modified", "Size"],
+        headers=["File Name", "Date Created", "Size"],
         interactive=False,
         label="Bucket Contents"
     )
@@ -156,11 +158,8 @@ with gr.Blocks() as app:
     file_output = gr.File(label="Ready for Download")
     
     # --- EVENT TRIGGERS ---
-    # Trigger refresh
     refresh_btn.click(fn=list_files, inputs=[sort_dropdown], outputs=[file_table, file_dropdown])
-    # Trigger sort instantly when dropdown changes
     sort_dropdown.change(fn=list_files, inputs=[sort_dropdown], outputs=[file_table, file_dropdown])
-    # Load data automatically on startup
     app.load(fn=list_files, inputs=[sort_dropdown], outputs=[file_table, file_dropdown])
 
 if __name__ == "__main__":