Update app.py

app.py

@@ -3,43 +3,34 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 
 INFO = """
-# Inference Provider (fal-ai)
-This Space calls **Wan-AI/Wan2.2-T2V-A14B-Diffusers** through the **fal-ai** provider.
-Click *Sign in* (top-left) so your Hugging Face token is used for the call.
+### Wan2.2 T2V via Inference Providers (fal-ai)
+- Click **Sign in** (left) or set a Space secret `HF_TOKEN`.
+- This Space exposes an HTTP API at `/api/predict`.
 """
 
 def t2v(prompt: str, fps: int = 12, request: gr.Request | None = None):
-    """
-    Returns a local MP4 path for Gradio to serve.
-    Authentication:
-      - If the user clicked 'Sign in', Gradio attaches `Authorization: Bearer hf_...`
-        to the request; we reuse that token with InferenceClient.
-      - Fallback: HF_TOKEN env var (useful for private Spaces / testing).
-    """
-    # extract Bearer token if present
+    # 1) get token from Login or Space secret
     token = None
     if request and "authorization" in request.headers:
         auth = request.headers["authorization"]
         if isinstance(auth, str) and auth.lower().startswith("bearer "):
             token = auth.split(" ", 1)[1]
     token = token or os.getenv("HF_TOKEN")
-
     if not token:
-        raise gr.Error("No token found. Click 'Sign in' or set HF_TOKEN env var in the Space.")
+        raise gr.Error("No token found. Click 'Sign in' OR set a Space secret HF_TOKEN.")
 
-    client = InferenceClient(provider="fal-ai", api_key=token)  # SDK routes to the provider
-    # NOTE: Providers usually return raw bytes for text-to-video
+    # 2) call provider-backed model
+    client = InferenceClient(provider="fal-ai", api_key=token)
     video_bytes = client.text_to_video(
         prompt,
         model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
     )
 
-    # save to a temp file for Gradio to serve
+    # 3) save to temp mp4 for Gradio to serve
     tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
     tmp.write(video_bytes)
-    tmp.flush()
-    tmp.close()
-    return tmp.name  # Gradio will map this to /file=... URL
+    tmp.flush(); tmp.close()
+    return tmp.name
 
 with gr.Blocks(fill_height=True) as demo:
     with gr.Sidebar():
@@ -48,10 +39,11 @@ with gr.Blocks(fill_height=True) as demo:
     gr.Interface(
         fn=t2v,
         inputs=[gr.Textbox(label="Prompt"), gr.Slider(4, 24, value=12, step=1, label="FPS")],
-        outputs=gr.Video(label="Output"),
-        title="Wan2.2 T2V via fal-ai",
-        api_name="predict",            # <= creates /api/predict
-        allow_flagging="never",
+        outputs=gr.Video(label="Video"),
+        api_name="predict",           # <-- creates /api/predict
+        title="Wan2.2 Text-to-Video"
     )
 
-demo.launch()
+if __name__ == "__main__":
+    # Disable SSR to avoid the “Stopping Node.js server...” loop
+    demo.launch(ssr_mode=False)