limit to max 4 seconds input on shared UI

app.py

@@ -81,6 +81,41 @@ snapshot_download(
     local_dir = "./models/stable-video-diffusion-img2vid-xt"  
 )
 
+is_shared_ui = True if "fffiloni/SVFR-demo" in os.environ['SPACE_ID'] else False
+from moviepy.editor import VideoFileClip
+
+def process_video(input_path):
+    # Create a temporary folder
+    temp_folder = "temp_video_output"
+    os.makedirs(temp_folder, exist_ok=True)
+
+    # Get file extension
+    file_extension = os.path.splitext(input_path)[1].lower()
+
+    # Convert to MP4 if not already MP4
+    if file_extension != ".mp4":
+        mp4_path = os.path.join(temp_folder, "converted.mp4")
+        clip = VideoFileClip(input_path)
+        clip.write_videofile(mp4_path, codec="libx264")
+        clip.close()
+    else:
+        mp4_path = input_path  # Already MP4
+
+    # Load video and trim to 4 seconds if necessary
+    output_path = os.path.join(temp_folder, "trimmed.mp4")
+    with VideoFileClip(mp4_path) as clip:
+        if clip.duration > 4:
+            trimmed_clip = clip.subclip(0, 4)  # Trim to 4 seconds
+            trimmed_clip.write_videofile(output_path, codec="libx264")
+            trimmed_clip.close()
+        else:
+            # If no trimming is needed, copy the file to output_path
+            if mp4_path != output_path:
+                shutil.copy(mp4_path, output_path)
+
+    # Return the output path
+    return output_path, temp_folder
+
 BASE_DIR = '.'
 
 config = OmegaConf.load("./config/infer.yaml")
@@ -359,6 +394,9 @@ def random_seed():
 
 def infer(lq_sequence, task_name, mask, seed, progress=gr.Progress(track_tqdm=True)):
     
+    if is_shared_ui:
+        lq_sequence, temp_input_folder = process_video(lq_sequence)
+    
     unique_id = str(uuid.uuid4())
     output_dir = f"results_{unique_id}"
 
@@ -396,10 +434,18 @@ def infer(lq_sequence, task_name, mask, seed, progress=gr.Progress(track_tqdm=Tr
             face_region_video = None
         
         print(output_video_path,face_region_video_path)
+
+        if is_shared_ui:
+            # Clean up temporary input folder
+            shutil.rmtree(temp_input_folder)
         torch.cuda.empty_cache()
         return face_region_video_path,output_video_path
     
     except subprocess.CalledProcessError as e:
+
+        if is_shared_ui
+            # Clean up temporary input folder
+            shutil.rmtree(temp_input_folder)
         torch.cuda.empty_cache()
         raise gr.Error(f"Error during inference: {str(e)}")