Update app.py

app.py

@@ -21,7 +21,7 @@ def load_file_bytes_to_df(file) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
         # decide by filename or by sniffing bytes
         name = getattr(file, "name", "") or ""
         # Basic heuristic for CSV vs Excel
-        if name.lower().endswith(".csv") or b"," in content[:200]:
+        if name.lower().endswith(".csv") or (isinstance(content, (bytes, bytearray)) and b"," in content[:200]):
             df = pd.read_csv(io.BytesIO(content))
         else:
             df = pd.read_excel(io.BytesIO(content))
@@ -37,7 +37,7 @@ def load_file_bytes_to_df(file) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
     return df, None
 
 def simple_nl_to_action(df: pd.DataFrame, query: str):
-    """Same simple NL parser as before (returns DataFrame or (None, message))."""
+    """Very small NL parser that recognizes a few common requests and returns a DataFrame or (None, message)."""
     q = (query or "").strip().lower()
     if q == "":
         return None, "Please type a question like: 'show columns', 'show first 5 rows', 'describe column sales', or 'filter where Region = India and Year >= 2021'."
@@ -103,7 +103,6 @@ def process(file, query):
     # Load into memory-only DataFrame
     df, err = load_file_bytes_to_df(file)
     if err:
-        # Ensure any partial objects are removed
         try:
             del file
         except Exception:
@@ -114,16 +113,15 @@ def process(file, query):
     # Run the NLP-to-action
     try:
         res, msg = simple_nl_to_action(df, query)
-        # Convert DataFrame result if any to a safe small object (head) to limit memory/time
         if isinstance(res, pd.DataFrame):
-            out_df = res.copy()  # shallow copy that we will return
+            out_df = res.copy()
         else:
             out_df = None
     except Exception as e:
         out_df = None
         msg = f"Error while processing: {e}"
 
-    # --- IMPORTANT: Remove references to large objects immediately ---
+    # Remove references to large objects immediately
     try:
         del df
         del file
@@ -140,9 +138,7 @@ def process(file, query):
 def clear_all():
     """
     Returns Gradio update objects that clear inputs and outputs.
-    This helps remove file from the browser UI and server-side.
     """
-    # gradio update helpers: set inputs/outputs back to empty values
     return (
         gr.File.update(value=None),
         gr.Textbox.update(value=""),
@@ -159,13 +155,12 @@ with gr.Blocks() as demo:
     with gr.Row():
         submit = gr.Button("Run query")
         clear_btn = gr.Button("Clear / Reset (remove uploaded file & results)")
-    output_table = gr.Dataframe(headers="auto", label="Result table")
+    # Use headers=None to be compatible with the Gradio version in Spaces
+    output_table = gr.Dataframe(headers=None, label="Result table")
     status = gr.Textbox(label="Status / Messages", interactive=False)
 
     submit.click(fn=process, inputs=[file_input, query_input], outputs=[output_table, status])
-    # Clear button clears UI (and removes server-side references)
     clear_btn.click(fn=clear_all, inputs=None, outputs=[file_input, query_input, output_table, status])
 
 if __name__ == "__main__":
-    # Do not enable "share" or persistent caching here; default launch is fine for Spaces
     demo.launch()