Update app.py

app.py

@@ -12,6 +12,12 @@ import numpy as np
 import urllib.request
 import base64
 from io import BytesIO
+import logging
+from tqdm import tqdm
+
+# Set up logging
+logging.basicConfig(filename='app.log', level=logging.DEBUG, 
+                    format='%(asctime)s - %(levelname)s - %(message)s')
 
 MODEL_NAME = "cmckinle/sdxl-flux-detector"
 LABELS = ["AI", "Real"]
@@ -41,10 +47,34 @@ class AIDetector:
         
         return label, results
 
+def custom_upload_handler(file):
+    try:
+        logging.info(f"Starting upload of file: {file.name}")
+        file_size = os.path.getsize(file.name)
+        logging.info(f"File size: {file_size} bytes")
+        
+        # Read and process the file in chunks
+        chunk_size = 1024 * 1024  # 1MB chunks
+        total_chunks = file_size // chunk_size + (1 if file_size % chunk_size > 0 else 0)
+        
+        with open(file.name, 'rb') as f:
+            for chunk in tqdm(range(total_chunks), desc="Uploading"):
+                data = f.read(chunk_size)
+                if not data:
+                    break
+                logging.debug(f"Processed chunk {chunk+1} of {total_chunks}")
+        
+        logging.info("File upload completed successfully")
+        return file
+    except Exception as e:
+        logging.error(f"Error during file upload: {str(e)}")
+        raise gr.Error(f"Upload failed: {str(e)}")
+
 def process_zip(zip_file):
     temp_dir = tempfile.mkdtemp()
     
     try:
+        logging.info(f"Starting to process zip file: {zip_file.name}")
         # Validate zip structure
         with zipfile.ZipFile(zip_file.name, 'r') as z:
             file_list = z.namelist()
@@ -87,14 +117,16 @@ def process_zip(zip_file):
                             false_negatives.append((img_name, img_data))
 
                 except Exception as e:
-                    print(f"Error processing image {img_name}: {e}")
+                    logging.error(f"Error processing image {img_name}: {e}")
                 
                 processed_images += 1
                 gr.Progress(processed_images / total_images)
         
+        logging.info("Zip file processing completed successfully")
         return evaluate_model(labels, preds, false_positives, false_negatives)
     
     except Exception as e:
+        logging.error(f"Error processing zip file: {str(e)}")
         raise gr.Error(f"Error processing zip file: {str(e)}")
     
     finally:
@@ -313,7 +345,8 @@ def create_gradio_interface():
                     label="Upload Zip (must contain 'real' and 'ai' folders)",
                     file_types=[".zip"],
                     file_count="single",
-                    max_file_size=1024  # 1024 MB (1 GB)
+                    max_file_size=1024 * 10,  # 10240 MB (10 GB)
+                    preprocess=custom_upload_handler
                 )
                 batch_btn = gr.Button("Process Batch", interactive=False)
 
@@ -333,23 +366,4 @@ def create_gradio_interface():
         )
 
         def enable_batch_btn(file):
-            return gr.Button.update(interactive=file is not None)
-
-        zip_file.upload(
-            enable_batch_btn,
-            zip_file,
-            batch_btn
-        )
-
-        batch_btn.click(
-            process_zip,
-            zip_file,
-            [output_acc, output_roc, output_report, output_plots, output_fp_fn],
-            api_name="batch_process"
-        )
-
-    return app
-
-if __name__ == "__main__":
-    app = create_gradio_interface()
-    app.launch(show_api=False, max_threads=24)
+            return gr.Button.update(interactive=file is not None)