Spaces:
Running
Running
File size: 3,468 Bytes
704ee93 6be3159 704ee93 6be3159 704ee93 a61d80f 704ee93 3584703 704ee93 3584703 704ee93 3584703 704ee93 50b9a44 eb6780a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
#!/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 our <a style="color:blue;" href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">full report</a> for more details on how this works.<p>
<p style='text-align: center'>Created with <a style="color:blue;" href="https://github.com/borisdayma/dalle-mini">DALL·E 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 DALL·E-mini, a text-to-image generation model.
"""
gr.Interface(run_inference,
inputs=[gr.inputs.Textbox(label='Prompt')],
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
).launch()
|