Update app.py

app.py

@@ -202,8 +202,8 @@ def train_model(scraper, progress=gr.Progress()):
 # ======================
 def create_interface():
     with gr.Blocks() as app:
-        scraper = gr.State(WebScraper)
-        model_runner = gr.State(ModelRunner)
+        scraper = gr.State(lambda: WebScraper())
+        model_runner = gr.State(lambda: ModelRunner())
         
         with gr.Row():
             with gr.Column():
@@ -214,38 +214,47 @@ def create_interface():
                 
                 train_btn = gr.Button("Start Training")
                 training_status = gr.Textbox(label="Training Status")
-                training_progress = gr.Textbox(label="Training Progress", value="Not started")
-
+                training_progress = gr.Textbox(label="Training Progress", value="Epoch 0/10 | Batch 0/0")
+                
             with gr.Column():
                 prompt_input = gr.Textbox(label="Generation Prompt")
                 model_choice = gr.Radio(["Pretrained", "Custom"], label="Model Type", value="Pretrained")
                 generate_btn = gr.Button("Generate Image")
                 output_image = gr.Image(label="Generated Image")
-
-        # Real-time updates using event triggers
-        def update_scraping_progress(scraper):
-            return f"{scraper.scraping_progress:.1f}% ({scraper.scraped_count}/{scraper.total_images})"
         
-        def update_training_progress():
-            if os.path.exists(CONFIG["paths"]["model_save"]):
-                stats = os.stat(CONFIG["paths"]["model_save"])
-                return f"Model size: {stats.st_size//1024}KB"
-            return "No trained model"
-
-        # Set up periodic updates
-        scraping_progress.change(
-            update_scraping_progress,
+        # Scraping monitoring
+        def monitor_scraping(scraper):
+            while True:
+                if hasattr(scraper, 'scraping_progress'):
+                    yield f"{scraper.scraping_progress:.1f}% ({scraper.scraped_count}/{scraper.total_images})"
+                else:
+                    yield "0% (0/0)"
+                time.sleep(CONFIG["scraping"]["progress_interval"])
+        
+        # Training monitoring
+        def monitor_training():
+            while True:
+                if os.path.exists(CONFIG["paths"]["model_save"]):
+                    with open(CONFIG["paths"]["model_save"], 'rb') as f:
+                        stats = os.stat(f.fileno())
+                        yield f"Model size: {stats.st_size//1024}KB"
+                else:
+                    yield "No trained model"
+                time.sleep(1)
+        
+        app.load(
+            monitor_scraping,
             inputs=[scraper],
             outputs=[scraping_progress],
             every=CONFIG["scraping"]["progress_interval"]
         )
         
-        training_progress.change(
-            update_training_progress,
+        app.load(
+            monitor_training,
             outputs=[training_progress],
-            every=CONFIG["training"]["progress_interval"]
+            every=1
         )
-
+        
         # Event handlers
         scrape_btn.click(
             lambda s, q: s.start_scraping(q),
@@ -256,7 +265,7 @@ def create_interface():
         train_btn.click(
             lambda s: train_model(s),
             [scraper],
-            [training_status, training_progress]
+            [training_status]
         )
         
         generate_btn.click(