Spaces:
Running
on
L40S
Running
on
L40S
File size: 7,787 Bytes
16c783e 9613d25 16c783e 49771b5 16c783e 9613d25 6d90ae1 8299c8e 9613d25 8299c8e 6d90ae1 293de33 6d90ae1 293de33 9613d25 6d90ae1 9613d25 6d90ae1 8299c8e 5082c3c 8299c8e 6d90ae1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
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 = '''
#top{position: fixed;}
'''
with gr.Blocks() as demo:
with gr.Column():
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.Row():
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.Row():
blink = gr.Slider(
label="Blink", value=0,
minimum=-20, maximum=5
)
eyebrow = gr.Slider(
label="Eyebrow", value=0,
minimum=-10, maximum=15
)
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.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.Accordion("More Settings", open=False):
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; 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-md.svg" alt="Duplicate this Space">
</a> 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]
submit_btn.click(
fn=predict,
inputs=inputs,
outputs=outputs,
)
rotate_pitch.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
rotate_yaw.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
rotate_roll.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
blink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
eyebrow.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
wink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
pupil_x.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
pupil_y.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
aaa.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
eee.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
woo.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
smile.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal")
demo.launch(share=False, show_error=True) |