Update app.py

app.py

@@ -1,7 +1,13 @@
+import huggingface_hub as hf_hub
+# Shim missing APIs removed in huggingface_hub >= 0.26.0
+if not hasattr(hf_hub, "cached_download"):
+    hf_hub.cached_download = hf_hub.hf_hub_download
+if not hasattr(hf_hub, "model_info"):
+    hf_hub.model_info = hf_hub.get_model_info
+
 import gradio as gr
 import torch
 import tempfile
-import spaces
 from diffusers import StableVideoDiffusionPipeline
 from diffusers.utils import export_to_video
 
@@ -13,7 +19,6 @@ pipe = StableVideoDiffusionPipeline.from_pretrained(
     MODEL, torch_dtype=torch.float16
 ).to("cuda")
 
-@spaces.GPU(duration=300)
 def infer(first_image, last_image, prompt, guidance=7.5, frames=25):
     # Generate the in-between frames
     video = pipe(
@@ -23,10 +28,10 @@ def infer(first_image, last_image, prompt, guidance=7.5, frames=25):
         guidance_scale=guidance,
         num_frames=frames
     ).frames
-    # Export as MP4 to a temp file
+    # Export to a temporary MP4 file
     mp4_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
     export_to_video(video, mp4_path, fps=15)
-    return mp4_path  # Gradio will auto-encode this to base64 for the API
+    return mp4_path  # Gradio will auto-encode to base64 for the API
 
 # Build a minimal Gradio interface
 demo = gr.Interface(
@@ -43,4 +48,4 @@ demo = gr.Interface(
 )
 
 # Enable the REST API
-demo.queue(concurrency_count=1).launch(show_api=True, ssr_mode=False)
+demo.queue(concurrency_count=1).launch(show_api=True)