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 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 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 ) 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("""

Duplicate this Space

to skip the queue and enjoy faster inference on the GPU of your choice

""") 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 ) 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 ) 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)