import gradio as gr
from PIL import Image
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
# Function to verify the image file type and resize it if necessary
def preprocess_image(image_path):
# Check if the file exists
if not os.path.exists(image_path):
raise FileNotFoundError(f"No such file: '{image_path}'")
# Get the file extension and make sure it's a valid image format
valid_extensions = ['jpg', 'jpeg', 'png', 'webp']
file_extension = image_path.split('.')[-1].lower()
if file_extension not in valid_extensions:
raise ValueError("Invalid file type. Only JPG, PNG, and WEBP are allowed.")
# Open the image
with Image.open(image_path) as img:
width, height = img.size
# Check if any dimension exceeds 1024 pixels
if width > 1024 or height > 1024:
# Calculate the new size while maintaining aspect ratio
if width > height:
new_width = 1024
new_height = int((new_width / width) * height)
else:
new_height = 1024
new_width = int((new_height / height) * width)
# Resize the image
img_resized = img.resize((new_width, new_height), Image.LANCZOS)
print(f"Resized image to {new_width}x{new_height}.")
# Save the resized image as 'resized_image.jpg'
output_path = 'resized_image.jpg'
img_resized.save(output_path, 'JPEG')
print(f"Resized image saved as {output_path}")
return output_path
else:
print("Image size is within the limit, no resizing needed.")
return image_path
def display_uploaded_image(image_in):
return image_in
def reset_parameters():
return gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0), gr.update(value=0)
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
time.sleep(0.4)
response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
if response.status_code == 201:
time.sleep(0.4)
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)
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:
time.sleep(1)
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",
sources=["upload"],
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
)
with gr.Row():
reset_btn = gr.Button("Reset")
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 = preprocess_image,
inputs = [image],
outputs = [image],
queue = False
)
reset_btn.click(
fn = reset_parameters,
inputs = None,
outputs = [rotate_pitch, rotate_yaw, rotate_roll, blink, eyebrow, wink, pupil_x, pupil_y, aaa, eee, woo, smile],
queue = False
).then(
fn=predict,
inputs=inputs,
outputs=outputs,
show_api=False
)
submit_btn.click(
fn=predict,
inputs=inputs,
outputs=outputs,
show_api=False
)
rotate_pitch.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
rotate_yaw.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
rotate_roll.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
blink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
eyebrow.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
wink.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
pupil_x.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
pupil_y.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
aaa.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
eee.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
woo.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
smile.release(fn=predict, inputs=inputs, outputs=outputs, show_progress="minimal", show_api=False)
demo.queue(api_open=False).launch(share=False, show_error=True, show_api=False)