Update app_seedvr.py

app_seedvr.py

@@ -8,18 +8,13 @@ import gradio as gr
 import cv2
 
 # --- SERVER LOGIC INTEGRATION ---
-# This section ensures we can import and use the SeedVR engine directly.
 try:
-    # We need the SeedVRServer class which handles the inference logic.
     from api.seedvr_server import SeedVRServer
 except ImportError as e:
     print(f"FATAL ERROR: Could not import SeedVRServer. Details: {e}")
-    # The application cannot run without the server logic.
     raise
 
 # --- INITIALIZATION ---
-# Create a single, persistent instance of the server.
-# This clones the repo and downloads models only once at startup.
 server = SeedVRServer()
 
 # --- HELPER FUNCTIONS ---
@@ -40,7 +35,6 @@ def _extract_first_frame(video_path: str) -> Optional[str]:
         success, image = vid_cap.read()
         vid_cap.release()
         if not success: return None
-        
         image_path = Path(video_path).with_suffix(".jpg")
         cv2.imwrite(str(image_path), image)
         return str(image_path)
@@ -49,18 +43,12 @@ def _extract_first_frame(video_path: str) -> Optional[str]:
         return None
 
 def on_file_upload(file_obj):
-    """
-    Callback triggered when a user uploads a file.
-    It checks if the file is a video and suggests an appropriate `sp_size`.
-    """
+    """Callback triggered when a user uploads a file."""
     if file_obj is None:
-        return 1 # Default to 1 if file is cleared
-    
+        return 1
     if _is_video(file_obj.name):
-        # For videos, suggest a default value suitable for multi-GPU
         return gr.update(value=4, interactive=True)
     else:
-        # For images, lock the value to 1
         return gr.update(value=1, interactive=False)
 
 # --- CORE INFERENCE FUNCTION ---
@@ -73,11 +61,10 @@ def run_inference_ui(
     progress=gr.Progress(track_tqdm=True)
 ):
     """
-    The main callback function for Gradio. This is a generator (`yield`)
-    to allow for real-time UI updates during the long-running task.
+    The main callback function for Gradio, using generators (`yield`)
+    for real-time UI updates.
     """
     # 1. Initial State & Validation
-    # On start, disable the button, clear previous results, and make the log visible.
     yield (
         gr.update(interactive=False, value="Processing... 🚀"),
         gr.update(value=None, visible=False),
@@ -88,23 +75,21 @@ def run_inference_ui(
 
     if not input_file_path:
         gr.Warning("Please upload a media file first.")
-        # Re-enable button and hide outputs
         yield (
             gr.update(interactive=True, value="Restore Media"),
             None, None, None, gr.update(visible=False)
         )
         return
 
-    # Use a simple list to act as a log buffer that can be updated by a callback
     log_buffer = ["▶ Starting inference process...\n"]
     yield gr.update(), None, None, None, ''.join(log_buffer)
 
+    # CORREÇÃO APLICADA AQUI
     def progress_callback(step: float, desc: str):
         """A simple callback to append messages to our log buffer."""
-        # This function can be passed to the backend if it supports it.
-        # For now, we'll call it manually from this UI function.
         log_buffer.append(f"⏳ [{int(step*100)}%] {desc}\n")
-        progress.update(amount=step, desc=desc)
+        # A chamada correta para a API de progresso do Gradio
+        progress(step, desc=desc)
 
     was_input_video = _is_video(input_file_path)
 
@@ -113,15 +98,14 @@ def run_inference_ui(
         progress_callback(0.1, "Calling backend engine...")
         yield gr.update(), None, None, None, ''.join(log_buffer)
 
-        # Call the server's direct inference method. This is a blocking call.
         video_result_path = server.run_inference_direct(
             file_path=input_file_path,
-            seed=42, # Using a fixed seed as requested
+            seed=42,
             res_h=int(resolution),
-            res_w=int(resolution), # Set width equal to height
+            res_w=int(resolution),
             sp_size=int(sp_size),
             fps=float(fps) if fps and fps > 0 else None,
-            progress=progress, # Pass the Gradio progress object
+            progress=progress,
         )
         
         progress_callback(1.0, "Inference complete! Processing final output...")
@@ -132,12 +116,11 @@ def run_inference_ui(
         if was_input_video:
             final_video = video_result_path
             log_buffer.append(f"✅ Video result is ready.\n")
-        else: # If input was an image
+        else:
             final_image = _extract_first_frame(video_result_path)
-            final_video = video_result_path # Also provide the 1-frame video
+            final_video = video_result_path
             log_buffer.append(f"✅ Image result extracted from video.\n")
         
-        # Final yield to show the results and re-enable the button
         yield (
             gr.update(interactive=True, value="Restore Media"),
             gr.update(value=final_image, visible=final_image is not None),
@@ -153,7 +136,6 @@ def run_inference_ui(
         import traceback
         traceback.print_exc()
         
-        # Yield an error state and re-enable the button
         yield (
             gr.update(interactive=True, value="Restore Media"),
             None, None, None,
@@ -162,7 +144,6 @@ def run_inference_ui(
 
 
 # --- GRADIO UI LAYOUT ---
-
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Restoration") as demo:
     # Header
     gr.Markdown(
@@ -173,13 +154,10 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Res
         </div>
         """
     )
-
     with gr.Row():
-        # --- Left Column: Inputs & Controls ---
         with gr.Column(scale=1):
             gr.Markdown("### 1. Upload Media")
             input_media = gr.File(label="Input File (Video or Image)", type="filepath")
-            
             gr.Markdown("### 2. Configure Settings")
             with gr.Accordion("Generation Parameters", open=True):
                 resolution_select = gr.Dropdown(
@@ -188,37 +166,22 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Res
                     value="480",
                     info="The output height and width will be set to this value."
                 )
-                
                 sp_size_slider = gr.Slider(
                     label="Sequence Parallelism (sp_size)",
                     minimum=1, maximum=16, step=1, value=4,
                     info="For multi-GPU videos. This will be set to 1 for images."
                 )
-
                 fps_out = gr.Number(label="Output FPS (for Videos)", value=24, precision=0, info="Set to 0 to use the original FPS.")
-
             run_button = gr.Button("Restore Media", variant="primary", icon="✨")
-
-        # --- Right Column: Outputs ---
         with gr.Column(scale=2):
             gr.Markdown("### 3. Results")
-            
-            # Log window
             log_window = gr.Textbox(
-                label="Inference Log 📝",
-                lines=8,
-                max_lines=15,
-                interactive=False,
-                visible=False, # Starts hidden
-                autoscroll=True,
+                label="Inference Log 📝", lines=8, max_lines=15,
+                interactive=False, visible=False, autoscroll=True,
             )
-
-            # Output components start hidden and are made visible upon completion
             output_image = gr.Image(label="Image Result", show_download_button=True, type="filepath", visible=False)
             output_video = gr.Video(label="Video Result", visible=False)
             output_download = gr.File(label="Download Full Result (Video)", visible=False)
-
-    # --- Footer ---
     gr.Markdown(
         """
         ---
@@ -227,16 +190,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Res
         """
     )
     
-    # --- Event Handlers ---
-    
-    # When a file is uploaded, automatically adjust the sp_size slider
-    input_media.upload(
-        fn=on_file_upload,
-        inputs=[input_media],
-        outputs=[sp_size_slider]
-    )
+    input_media.upload(fn=on_file_upload, inputs=[input_media], outputs=[sp_size_slider])
     
-    # When the "Restore Media" button is clicked, run the main inference function
     run_button.click(
         fn=run_inference_ui,
         inputs=[input_media, resolution_select, sp_size_slider, fps_out],