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Running
on
L40S
Running
on
L40S
import gradio as gr | |
from urllib.parse import urlparse | |
import requests | |
import time | |
import os | |
from utils.gradio_helpers import parse_outputs, process_outputs | |
def display_uploaded_image(image_in): | |
return image_in | |
names = ['image', 'rotate_pitch', 'rotate_yaw', 'rotate_roll', 'blink', 'eyebrow', 'wink', 'pupil_x', 'pupil_y', 'aaa', 'eee', 'woo', 'smile', 'src_ratio', 'sample_ratio', 'crop_factor', 'output_format', 'output_quality'] | |
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)): | |
headers = {'Content-Type': 'application/json'} | |
payload = {"input": {}} | |
base_url = "http://0.0.0.0:7860" | |
for i, key in enumerate(names): | |
value = args[i] | |
if value and (os.path.exists(str(value))): | |
value = f"{base_url}/file=" + value | |
if value is not None and value != "": | |
payload["input"][key] = value | |
response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload) | |
if response.status_code == 201: | |
follow_up_url = response.json()["urls"]["get"] | |
response = requests.get(follow_up_url, headers=headers) | |
while response.json()["status"] != "succeeded": | |
if response.json()["status"] == "failed": | |
raise gr.Error("The submission failed!") | |
response = requests.get(follow_up_url, headers=headers) | |
time.sleep(1) | |
if response.status_code == 200: | |
json_response = response.json() | |
#If the output component is JSON return the entire output response | |
if(outputs[0].get_config()["name"] == "json"): | |
return json_response["output"] | |
predict_outputs = parse_outputs(json_response["output"]) | |
processed_outputs = process_outputs(predict_outputs) | |
return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0] | |
else: | |
if(response.status_code == 409): | |
raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.") | |
raise gr.Error(f"The submission failed! Error: {response.status_code}") | |
css = ''' | |
#col-container{max-width: 720px;margin: 0 auto;} | |
''' | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown("# Expression Editor") | |
gr.Markdown("Demo for expression-editor cog image by fofr") | |
with gr.Row(): | |
with gr.Column(): | |
image = gr.Image( | |
label="Input image", | |
type="filepath", | |
height=180 | |
) | |
with gr.Tab("HEAD"): | |
with gr.Column(): | |
rotate_pitch = gr.Slider( | |
label="Rotate Up-Down", | |
value=0, | |
minimum=-20, maximum=20 | |
) | |
rotate_yaw = gr.Slider( | |
label="Rotate Left-Right turn", | |
value=0, | |
minimum=-20, maximum=20 | |
) | |
rotate_roll = gr.Slider( | |
label="Rotate Left-Right tilt", value=0, | |
minimum=-20, maximum=20 | |
) | |
with gr.Tab("EYES"): | |
with gr.Column(): | |
eyebrow = gr.Slider( | |
label="Eyebrow", value=0, | |
minimum=-10, maximum=15 | |
) | |
with gr.Row(): | |
blink = gr.Slider( | |
label="Blink", value=0, | |
minimum=-20, maximum=5 | |
) | |
wink = gr.Slider( | |
label="Wink", value=0, | |
minimum=0, maximum=25 | |
) | |
with gr.Row(): | |
pupil_x = gr.Slider( | |
label="Pupil X", value=0, | |
minimum=-15, maximum=15 | |
) | |
pupil_y = gr.Slider( | |
label="Pupil Y", value=0, | |
minimum=-15, maximum=15 | |
) | |
with gr.Tab("MOUTH"): | |
with gr.Column(): | |
with gr.Row(): | |
aaa = gr.Slider( | |
label="Aaa", value=0, | |
minimum=-30, maximum=120 | |
) | |
eee = gr.Slider( | |
label="Eee", value=0, | |
minimum=-20, maximum=15 | |
) | |
woo = gr.Slider( | |
label="Woo", value=0, | |
minimum=-20, maximum=15 | |
) | |
smile = gr.Slider( | |
label="Smile", value=0, | |
minimum=-0.3, maximum=1.3 | |
) | |
with gr.Tab("More Settings"): | |
with gr.Column(): | |
src_ratio = gr.Number( | |
label="Src Ratio", info='''Source ratio''', value=1 | |
) | |
sample_ratio = gr.Slider( | |
label="Sample Ratio", info='''Sample ratio''', value=1, | |
minimum=-0.2, maximum=1.2 | |
) | |
crop_factor = gr.Slider( | |
label="Crop Factor", info='''Crop factor''', value=1.7, | |
minimum=1.5, maximum=2.5 | |
) | |
output_format = gr.Dropdown( | |
choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp" | |
) | |
output_quality = gr.Number( | |
label="Output Quality", info='''Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.''', value=95 | |
) | |
submit_btn = gr.Button("Submit") | |
with gr.Column(): | |
result_image = gr.Image(elem_id="top") | |
gr.HTML(""" | |
<div style="display: flex; flex-direction: column;justify-content: center; align-items: center; text-align: center;"> | |
<p style="display: flex;gap: 6px;"> | |
<a href="https://huggingface.co/spaces/fffiloni/expression-editor?duplicate=true"> | |
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" alt="Duplicate this Space"> | |
</a> | |
</p> | |
<p>to skip the queue and enjoy faster inference on the GPU of your choice </p> | |
</div> | |
""") | |
inputs = [image, rotate_pitch, rotate_yaw, rotate_roll, blink, eyebrow, wink, pupil_x, pupil_y, aaa, eee, woo, smile, src_ratio, sample_ratio, crop_factor, output_format, output_quality] | |
outputs = [result_image] | |
image.upload( | |
fn = display_uploaded_image, | |
inputs = [image], | |
outputs = [result_image], | |
queue = False | |
) | |
submit_btn.click( | |
fn=predict, | |
inputs=inputs, | |
outputs=outputs, | |
concurrency_limit=4 | |
) | |
rotate_pitch.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
rotate_yaw.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
rotate_roll.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
blink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
eyebrow.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
wink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
pupil_x.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
pupil_y.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
aaa.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
eee.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
woo.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
smile.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", concurrency_limit=2) | |
demo.queue().launch(share=False, show_error=True) |