Update app.py

app.py

@@ -12,12 +12,6 @@ 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"]
@@ -47,34 +41,10 @@ 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()
@@ -117,16 +87,14 @@ def process_zip(zip_file):
                             false_negatives.append((img_name, img_data))
 
                 except Exception as e:
-                    logging.error(f"Error processing image {img_name}: {e}")
+                    print(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:
@@ -345,8 +313,7 @@ def create_gradio_interface():
                     label="Upload Zip (must contain 'real' and 'ai' folders)",
                     file_types=[".zip"],
                     file_count="single",
-                    max_file_size=1024 * 10,  # 10240 MB (10 GB)
-                    preprocess=custom_upload_handler
+                    max_file_size=1024  # 1024 MB (1 GB)
                 )
                 batch_btn = gr.Button("Process Batch", interactive=False)
 
@@ -366,4 +333,23 @@ def create_gradio_interface():
         )
 
         def enable_batch_btn(file):
-            return gr.Button.update(interactive=file is not None)
+            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)