Spaces:
Restarting
on
L40S
Restarting
on
L40S
File size: 5,413 Bytes
16c783e |
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 |
import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
inputs = []
inputs.append(gr.Image(
label="Image", type="filepath"
))
inputs.append(gr.Slider(
label="Rotate Pitch", info='''Rotation pitch: Adjusts the up and down tilt of the face''', value=0,
minimum=-20, maximum=20
))
inputs.append(gr.Slider(
label="Rotate Yaw", info='''Rotation yaw: Adjusts the left and right turn of the face''', value=0,
minimum=-20, maximum=20
))
inputs.append(gr.Slider(
label="Rotate Roll", info='''Rotation roll: Adjusts the tilt of the face to the left or right''', value=0,
minimum=-20, maximum=20
))
inputs.append(gr.Slider(
label="Blink", info='''Blink: Controls the degree of eye closure''', value=0,
minimum=-20, maximum=5
))
inputs.append(gr.Slider(
label="Eyebrow", info='''Eyebrow: Adjusts the height and shape of the eyebrows''', value=0,
minimum=-10, maximum=15
))
inputs.append(gr.Number(
label="Wink", info='''Wink: Controls the degree of one eye closing''', value=0
))
inputs.append(gr.Slider(
label="Pupil X", info='''Pupil X: Adjusts the horizontal position of the pupils''', value=0,
minimum=-15, maximum=15
))
inputs.append(gr.Slider(
label="Pupil Y", info='''Pupil Y: Adjusts the vertical position of the pupils''', value=0,
minimum=-15, maximum=15
))
inputs.append(gr.Slider(
label="Aaa", info='''AAA: Controls the mouth opening for 'aaa' sound''', value=0,
minimum=-30, maximum=120
))
inputs.append(gr.Slider(
label="Eee", info='''EEE: Controls the mouth shape for 'eee' sound''', value=0,
minimum=-20, maximum=15
))
inputs.append(gr.Slider(
label="Woo", info='''WOO: Controls the mouth shape for 'woo' sound''', value=0,
minimum=-20, maximum=15
))
inputs.append(gr.Slider(
label="Smile", info='''Smile: Adjusts the degree of smiling''', value=0,
minimum=-0.3, maximum=1.3
))
inputs.append(gr.Number(
label="Src Ratio", info='''Source ratio''', value=1
))
inputs.append(gr.Slider(
label="Sample Ratio", info='''Sample ratio''', value=1,
minimum=-0.2, maximum=1.2
))
inputs.append(gr.Slider(
label="Crop Factor", info='''Crop factor''', value=1.7,
minimum=1.5, maximum=2.5
))
inputs.append(gr.Dropdown(
choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp"
))
inputs.append(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
))
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']
outputs = []
outputs.append(gr.Image())
expected_outputs = len(outputs)
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
headers = {'Content-Type': 'application/json'}
payload = {"input": {}}
parsed_url = urlparse(str(request.url))
base_url = parsed_url.scheme + "://" + parsed_url.netloc
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)
difference_outputs = expected_outputs - len(processed_outputs)
# If less outputs than expected, hide the extra ones
if difference_outputs > 0:
extra_outputs = [gr.update(visible=False)] * difference_outputs
processed_outputs.extend(extra_outputs)
# If more outputs than expected, cap the outputs to the expected number
elif difference_outputs < 0:
processed_outputs = processed_outputs[:difference_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}")
title = "Demo for expression-editor cog image by fofr"
model_description = "None"
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title=title,
description=model_description,
allow_flagging="never",
)
app.launch(share=True)
|