Update gradio_app.py

gradio_app.py

@@ -1,24 +1,35 @@
-from huggingface_hub import InferenceClient
+import gradio as gr
 
-client = InferenceClient(
-    provider="fal-ai",
-    api_key="",
-)
+def infer(text_prompt, action_prompt):
+    from huggingface_hub import InferenceClient
 
-# output is a PIL.Image object
-image = client.text_to_image(
-    "Astronaut riding a horse",
-    model="multimodalart/isometric-skeumorphic-3d-bnb",
-)
+    inf_client = InferenceClient(
+        provider="fal-ai"
+    )
 
-from gradio_client import Client, handle_file
+    # output is a PIL.Image object
+    image = inf_client.text_to_image(
+        text_prompt,
+        model="multimodalart/isometric-skeumorphic-3d-bnb",
+    )
 
-client = Client("Lightricks/ltx-video-distilled")
-result = client.predict(
-		prompt="The creature from the image starts to move",
+    import tempfile
+
+    # Create a temporary PNG file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
+        temp_image_path = tmp_file.name
+        image.save(temp_image_path, format="PNG")
+
+    print(f"✅ Image saved temporarily at: {temp_image_path}")
+
+    from gradio_client import Client, handle_file
+
+    gr_client = Client("Lightricks/ltx-video-distilled")
+    result = gr_client.predict(
+		prompt=action_prompt,
 		negative_prompt="worst quality, inconsistent motion, blurry, jittery, distorted",
-		input_image_filepath=handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),
-		input_video_filepath="Hello!!",
+		input_image_filepath=handle_file(temp_image_path),
+		input_video_filepath=None,
 		height_ui=512,
 		width_ui=704,
 		mode="image-to-video",
@@ -29,5 +40,27 @@ result = client.predict(
 		ui_guidance_scale=1,
 		improve_texture_flag=True,
 		api_name="/image_to_video"
-)
-print(result)
+    )
+    print(result)
+
+    return temp_image_path, result
+
+with gr.Blocks() as demo:
+
+    with gr.Column():
+
+        login = gr.LoginButton()
+
+        text_prompt = gr.Textbox(label="Icon label")
+        action_prompt = gr.Textbox(label="Action label")
+        submit_btn = gr.Button("Submit")
+        image_out = gr.Image()
+        video_out = gr.Video()
+
+    submit_btn.click(
+        fn = infer,
+        inputs = [text_prompt, action_prompt],
+        outputs = [image_out, video_out]
+    )
+
+demo.launch(show_error=True)