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(lambda: WebScraper())
-        model_runner = gr.State(lambda: ModelRunner())
+        scraper = gr.State(WebScraper)
+        model_runner = gr.State(ModelRunner)
         
         with gr.Row():
             with gr.Column():
@@ -214,47 +214,38 @@ def create_interface():
                 
                 train_btn = gr.Button("Start Training")
                 training_status = gr.Textbox(label="Training Status")
-                training_progress = gr.Textbox(label="Training Progress", value="Epoch 0/10 | Batch 0/0")
-                
+                training_progress = gr.Textbox(label="Training Progress", value="Not started")
+
             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})"
         
-        # 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,
+        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,
             inputs=[scraper],
             outputs=[scraping_progress],
             every=CONFIG["scraping"]["progress_interval"]
         )
         
-        app.load(
-            monitor_training,
+        training_progress.change(
+            update_training_progress,
             outputs=[training_progress],
-            every=1
+            every=CONFIG["training"]["progress_interval"]
         )
-        
+
         # Event handlers
         scrape_btn.click(
             lambda s, q: s.start_scraping(q),
@@ -265,7 +256,7 @@ def create_interface():
         train_btn.click(
             lambda s: train_model(s),
             [scraper],
-            [training_status]
+            [training_status, training_progress]
         )
         
         generate_btn.click(