Upload gradio_web_server.py

gradio_web_server.py

@@ -155,28 +155,48 @@ def add_text(state, text, image, image_process_mode, request: gr.Request):
 
 	
 def batch_process_images(folder_path, textbox, model_selector, temperature, top_p, max_output_tokens, request: gr.Request):
-    print("calling batch_process_images")
-
-    for filename in os.listdir(folder_path):
-        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
-            image_path = os.path.join(folder_path, filename)
-            with Image.open(image_path) as image:
-                state = default_conversation.copy()
-                state, _, _, _, _, _, _, _, _ = add_text(state, textbox, image, "Default", request)
-
-                # Call http_bot and iterate over the generator
-                response_text = ""
-                for state_update in http_bot(state, model_selector, temperature, top_p, max_output_tokens, request):
-                    # Update state and extract response text
-                    state, chatbot_output, *_ = state_update
-                    response_text = chatbot_output
-
-                # Save the final response to a file
-                try:
-                    with open(os.path.splitext(image_path)[0] + '.txt', 'w') as f:
-                        f.write(response_text[0][1])
-                except Exception as e:
-                    print(f"An error occurred: {e}")
+    print("Starting batch processing of images")
+
+    # Initialize counters and timer
+    image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
+    total_images = len(image_files)
+    processed_images = 0
+    total_processing_time = 0
+
+    # Process each image file
+    for filename in image_files:
+        image_path = os.path.join(folder_path, filename)
+        start_time = time.time()
+        
+        with Image.open(image_path) as image:
+            state = default_conversation.copy()
+            state, _, _, _, _, _, _, _, _ = add_text(state, textbox, image, "Default", request)
+
+            # Call http_bot and iterate over the generator
+            response_text = ""
+            for state_update in http_bot(state, model_selector, temperature, top_p, max_output_tokens, request):
+                # Update state and extract response text
+                state, chatbot_output, *_ = state_update
+                response_text = chatbot_output
+
+            # Save the final response to a file
+            try:
+                with open(os.path.splitext(image_path)[0] + '.txt', 'w') as f:
+                    f.write(response_text[0][1])
+            except Exception as e:
+                print(f"An error occurred: {e}")
+
+        # Update processing information
+        processed_images += 1
+        processing_time = time.time() - start_time
+        total_processing_time += processing_time
+        average_processing_time = total_processing_time / processed_images
+        images_left = total_images - processed_images
+        eta_seconds = average_processing_time * images_left
+        eta = datetime.timedelta(seconds=int(eta_seconds))
+
+        # Display progress information
+        print(f"{processed_images}/{total_images} images processed, {images_left} left, average process time {average_processing_time :.2f} seconds, ETA: {str(eta)}")
 
     return "Batch processing completed."