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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=False, show_error=True) | |