Rewrite app with custom UI using InferenceClient

gr.load() does not support the text-to-video pipeline type.
Use huggingface_hub.InferenceClient.text_to_video() directly
with a custom Gradio interface instead.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

app.py

@@ -1,20 +1,34 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
+import tempfile
 
-HEADER = """
-# Wan 2.2 — Text to Video Generator
-
-Generate videos from text prompts using **Wan-AI/Wan2.2-T2V-A14B**.
-Powered by the **fal-ai** inference provider. Sign in with your Hugging Face account to get started.
-"""
+MODEL_ID = "Wan-AI/Wan2.2-T2V-A14B"
+PROVIDER = "fal-ai"
 
 EXAMPLES = [
     "A golden retriever running through a sunlit meadow in slow motion, cinematic lighting",
     "A futuristic city at night with flying cars and neon lights reflecting off wet streets",
     "An astronaut floating in space with Earth in the background, gentle camera rotation",
-    "Ocean waves crashing on a rocky coastline at sunset, dramatic sky with orange and purple clouds",
+    "Ocean waves crashing on a rocky coastline at sunset, dramatic sky with purple clouds",
     "A steaming cup of coffee on a wooden table with rain falling outside the window",
 ]
 
+
+def generate_video(prompt, oauth_token: gr.OAuthToken | None = None):
+    if oauth_token is None:
+        raise gr.Error("Please sign in with your Hugging Face account first.")
+    if not prompt or not prompt.strip():
+        raise gr.Error("Please enter a text prompt.")
+
+    client = InferenceClient(provider=PROVIDER, token=oauth_token.token)
+    video_bytes = client.text_to_video(prompt, model=MODEL_ID)
+
+    tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    tmp.write(video_bytes)
+    tmp.close()
+    return tmp.name
+
+
 with gr.Blocks(fill_height=True, title="Wan 2.2 Text-to-Video") as demo:
 
     with gr.Sidebar():
@@ -26,21 +40,29 @@ with gr.Blocks(fill_height=True, title="Wan 2.2 Text-to-Video") as demo:
         button = gr.LoginButton("Sign in")
         gr.Markdown("---")
         gr.Markdown(
-            "**Model:** [Wan-AI/Wan2.2-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B)\n\n"
-            "**Provider:** fal-ai"
+            f"**Model:** [{MODEL_ID}](https://huggingface.co/{MODEL_ID})\n\n"
+            f"**Provider:** {PROVIDER}"
         )
 
-    gr.Markdown(HEADER)
-    gr.load(
-        "models/Wan-AI/Wan2.2-T2V-A14B",
-        accept_token=button,
-        provider="fal-ai",
-    )
-    gr.Markdown("### Example Prompts")
-    gr.Dataframe(
-        value=[[e] for e in EXAMPLES],
-        headers=["Prompt idea — copy and paste above"],
-        interactive=False,
+    gr.Markdown(
+        "# Wan 2.2 — Text to Video Generator\n\n"
+        f"Generate videos from text prompts using **{MODEL_ID}**. "
+        "Sign in with your Hugging Face account to get started."
     )
 
-demo.launch(theme=gr.themes.Soft(primary_hue="indigo", secondary_hue="purple"))
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Describe the video you want to generate...",
+                lines=3,
+            )
+            generate_btn = gr.Button("Generate Video", variant="primary")
+            gr.Examples(examples=EXAMPLES, inputs=prompt)
+        with gr.Column():
+            video_output = gr.Video(label="Generated Video")
+
+    generate_btn.click(fn=generate_video, inputs=prompt, outputs=video_output)
+    prompt.submit(fn=generate_video, inputs=prompt, outputs=video_output)
+
+demo.launch(ssr_mode=False)