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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| import requests | |
| from PIL import Image | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from io import BytesIO | |
| import base64 | |
| import gradio as gr | |
| # If we use streamlit, this would be exported as a streamlit secret | |
| import os | |
| backend_url = os.environ["BACKEND_SERVER"] | |
| 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 | |
| class ServiceError(Exception): | |
| def __init__(self, status_code): | |
| self.status_code = status_code | |
| def get_images_from_ngrok(prompt): | |
| r = requests.post( | |
| backend_url, | |
| json={"prompt": prompt} | |
| ) | |
| if r.status_code == 200: | |
| images = r.json()["images"] | |
| images = [Image.open(BytesIO(base64.b64decode(img))) for img in images] | |
| return images | |
| else: | |
| raise ServiceError(r.status_code) | |
| def run_inference(prompt): | |
| try: | |
| images = get_images_from_ngrok(prompt) | |
| predictions = compose_predictions(images) | |
| output_title = f""" | |
| <p style="font-size:22px; font-style:bold">Best predictions</p> | |
| <p>We asked our model to generate 128 candidates for your prompt:</p> | |
| <pre> | |
| <b>{prompt}</b> | |
| </pre> | |
| <p>We then used a pre-trained <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP model</a> to score them according to the | |
| similarity of the text and the image representations.</p> | |
| <p>This is the result:</p> | |
| """ | |
| output_description = """ | |
| <p>Read more about the process <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">in our report</a>.<p> | |
| <p style='text-align: center'>Created with <a href="https://github.com/borisdayma/dalle-mini">DALLE·mini</a></p> | |
| """ | |
| except ServiceError: | |
| output_title = f""" | |
| Sorry, there was an error retrieving the images. Please, try again later or <a href="mailto:pcuenca-dalle@guenever.net">contact us here</a>. | |
| """ | |
| predictions = None | |
| output_description = "" | |
| return (output_title, predictions, output_description) | |
| outputs = [ | |
| gr.outputs.HTML(label=""), # To be used as title | |
| gr.outputs.Image(label=''), | |
| gr.outputs.HTML(label=""), # Additional text that appears in the screenshot | |
| ] | |
| description = """ | |
| Welcome to our demo of DALL·E-mini. This project was created on TPU v3-8s during the 🤗 Flax / JAX Community Week. | |
| It reproduces the essential characteristics of OpenAI's DALL·E, at a fraction of the size. | |
| Please, write what you would like the model to generate, or select one of the examples below. | |
| """ | |
| 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=description, | |
| 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_name="0.0.0.0", # Bind to all interfaces (I think) | |
| # server_port=8999 | |
| ).launch() | |