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Running
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L40S
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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)
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