Gradio UI skeleton for experimentation.

Former-commit-id: cf46af3085c3164460d5f5709e995052e1882fb0

app/sample_images/readme.txt

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+These images were generated by one of our checkpoints, as responses to the prompt "snowy mountains by the sea".

app/ui_gradio.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+from PIL import Image
+import gradio as gr
+
+def compose_predictions(images, caption=None):
+    increased_h = 0 if caption is None else 48
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (len(images)*w, h + increased_h))
+    for i, img_ in enumerate(images):
+        img.paste(img_, (i*w, increased_h))
+
+    if caption is not None:
+        draw = ImageDraw.Draw(img)
+        font = ImageFont.truetype("/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40)
+        draw.text((20, 3), caption, (255,255,255), font=font)
+    return img
+
+def compose_predictions_grid(images):
+    cols = 4
+    rows = len(images) // cols
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (w * cols, h * rows))
+    for i, img_ in enumerate(images):
+        row = i // cols
+        col = i % cols
+        img.paste(img_, (w * col, h * row))
+    return img
+
+def top_k_predictions_real(prompt, num_candidates=32, k=8):
+    images = hallucinate(prompt, num_images=num_candidates)
+    images = clip_top_k(prompt, images, k=num_preds)
+    return images
+
+def top_k_predictions(prompt, num_candidates=32, k=8):
+    images = []
+    for i in range(k):
+        image = Image.open(f"sample_images/image_{i}.jpg")
+        images.append(image)
+    return images
+
+def run_inference(prompt, num_images=32, num_preds=8):
+    images = top_k_predictions(prompt, num_candidates=num_images, k=num_preds)
+    predictions = compose_predictions(images)
+    output_title = 'This would be an html string to serve as title for the outputs.'
+    output_description = 'This is another random piece of html'
+    return (output_title, predictions, output_description)
+
+outputs = [
+    gr.outputs.HTML(label=""),      # To be used as title
+    gr.outputs.Image(label='Top predictions'),
+    gr.outputs.HTML(label=""),      # Additional text that appears in the screenshot
+]
+
+gr.Interface(run_inference, 
+    inputs=[gr.inputs.Textbox(label='Prompt')], #, gr.inputs.Slider(1,64,1,8, label='Candidates to generate'), gr.inputs.Slider(1,8,1,1, label='Best predictions to show')], 
+    outputs=outputs, 
+    title='DALL·E mini',
+    description='This is a demo of the DALLE-mini model trained with Jax/Flax on TPU v3-8s during the HuggingFace Community Week',
+    article="<p style='text-align: center'> DALLE·mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
+    layout='vertical',
+    theme='huggingface',
+    examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']],
+    allow_flagging=False,
+    live=False,
+    server_port=8999
+).launch(
+    share=True     # Creates temporary public link if true
+)