Update app.py

app.py

@@ -1,154 +1,50 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+import importlib.util
+import os
+import sys
+from pathlib import Path
 
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+import gradio as gr
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+WAN_APP_PATH = Path(__file__).parent / "Wan-2.2-5B" / "app.py"
 
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+def _resolve_server_host() -> str:
+    """Mirror official Spaces template behavior for host selection."""
+    return os.getenv("GRADIO_SERVER_HOST") or os.getenv("SERVER_HOST") or "0.0.0.0"
 
-        result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+def _resolve_server_port() -> int:
+    """Use Spaces-provided port variables or default to 7860."""
+    return int(os.getenv("GRADIO_SERVER_PORT") or os.getenv("SERVER_PORT") or "7860")
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+def _load_wan_demo():
+    """Load the Wan Blocks demo from Wan-2.2-5B/app.py via importlib."""
+    if not WAN_APP_PATH.exists():
+        raise FileNotFoundError(f"Wan app not found at {WAN_APP_PATH}")
 
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+    spec = importlib.util.spec_from_file_location("wan_space_app", WAN_APP_PATH)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
 
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+    if not hasattr(module, "demo"):
+        raise AttributeError("Wan app module does not expose a `demo` Blocks object.")
+    return module.demo
 
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
 
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
+# Hugging Face looks for a top-level `demo` Blocks object.
+demo = _load_wan_demo()
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name=_resolve_server_host(),
+        server_port=_resolve_server_port(),
+        theme=gr.themes.Soft(),
+        css=".gradio-container {max-width: 1200px; margin: auto;}",
+        footer_links=["gradio", "settings"],
+        allowed_paths=[str(Path.cwd())],
+        ssr_mode=False,
+    )