Update app.py

app.py

@@ -3,18 +3,31 @@ import numpy as np
 import time
 
 # define core fn, which returns a generator {steps} times before returning the image
-def fake_diffusion(steps):
-    for _ in range(steps):
-        time.sleep(1)
-        image = np.random.random((600, 600, 3))
-        yield image
-    image = "https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"
-    yield image
+def fake_diffusion(image, steps):
+    original_image = image.astype(np.float32) / 255.0
+    noisy_image = original_image + np.random.normal(0, 1, original_image.shape)
+    noisy_image = np.clip(noisy_image, 0, 1)
 
+    for i in range(steps):
+        time.sleep(0.2) # Shorter sleep for faster demo
+        # Simulate denoising: gradually revert to the original image
+        progress = (i + 1) / steps
+        denoised_step = (1 - progress) * noisy_image + progress * original_image
+        denoised_step = np.clip(denoised_step, 0, 1)
+        yield (denoised_step * 255).astype(np.uint8)
 
-demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs="image")
+demo = gr.Interface(
+    fake_diffusion,
+    inputs=[
+        gr.Image(type="numpy", label="Input Image", value="https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"),
+        gr.Slider(1, 20, 10, label="Denoising Steps")
+    ],
+    outputs=gr.Image(type="numpy", label="Denoised Image"),
+    title="Fake Image Diffusion Demo",
+    description="Upload an image and see a fake diffusion process denoise it step-by-step. Adjust the number of denoising steps using the slider."
+)
 
 # define queue - required for generators
 demo.queue()
 
-demo.launch()
+demo.launch()