code for running space

README.md

@@ -1,7 +1,7 @@
 ---
-title: Pixel Cnn Mnist
-emoji: 📚
-colorFrom: gray
+title: Pixel CNN MNIST
+emoji: 👨‍🎨
+colorFrom: indigo
 colorTo: pink
 sdk: gradio
 app_file: app.py

app.py

@@ -0,0 +1,63 @@
+from cProfile import label
+import glob
+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+import numpy as np
+
+pixel_cnn = from_pretrained_keras("keras-io/pixel-cnn-mnist")
+
+
+def infer(batch):
+    pixels = np.zeros(shape=(batch,) + (pixel_cnn.input_shape)[1:])
+    batch, rows, cols, channels = pixels.shape
+
+    # Iterate over the pixels because generation has to be done sequentially pixel by pixel.
+    for row in range(rows):
+        for col in range(cols):
+            for channel in range(channels):
+                # Feed the whole array and retrieving the pixel value probabilities for the next
+                # pixel.
+                probs = pixel_cnn.predict(pixels)[:, row, col, channel]
+                # Use the probabilities to pick pixel values and append the values to the image
+                # frame.
+                pixels[:, row, col, channel] = tf.math.ceil(
+                    probs - tf.random.uniform(probs.shape)
+                )
+
+    for i, pic in enumerate(pixels):
+        tf.keras.preprocessing.image.save_img(
+            "/tmp/generated_image_{}.png".format(i), deprocess_image(np.squeeze(pic, -1))
+        )
+    return glob.glob("/tmp/generated*")
+
+
+def deprocess_image(x):
+    # Stack the single channeled black and white image to RGB values.
+    x = np.stack((x, x, x), 2)
+    # Undo preprocessing
+    x *= 255.0
+    # Convert to uint8 and clip to the valid range [0, 255]
+    x = np.clip(x, 0, 255).astype("uint8")
+    return x
+
+
+article = """<center>
+Authors: Space by <a href='https://twitter.com/johko990' target='_blank'><b>Johannes Kolbe</b></a>, model by İhsan Soydemir after an example by ADMoreau at 
+<a href='https://keras.io/examples/generative/pixelcnn/' target='_blank'><b>keras.io</b></a> <br>
+<a href='https://arxiv.org/abs/1606.05328' target='_blank'><b>Original paper</b></a> by van den Oord et al."""
+
+
+description = """Image generation using a CNN. The model is trained on MNIST data, so the generation capabilities are limited to MNIST like images. <br>
+Just use the slider to set how many images should be created.<br>
+The execution might take some time."""
+
+
+Iface = gr.Interface(
+    fn=infer,
+    inputs=gr.inputs.Slider(minimum=1, maximum=20, default=4, step=1, label="Number of images to generate"),
+    outputs=gr.outputs.Carousel(["image"]),
+    title="PixelCNN - MNIST Image Generation",
+    article=article,
+    description=description,
+).launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,4 @@
+tensorflow >=2.6.0
+gradio
+huggingface_hub
+jinja2