app.py

import json
import gradio as gr
import torchvision
from facetorch import FaceAnalyzer
from omegaconf import OmegaConf


cfg = OmegaConf.load("config.merged.yml")
analyzer = FaceAnalyzer(cfg.analyzer)


def inference(path_image):
    response = analyzer.run(
        path_image=path_image,
        batch_size=cfg.batch_size,
        fix_img_size=cfg.fix_img_size,
        return_img_data=cfg.return_img_data,
        include_tensors=cfg.include_tensors,
        path_output=None,
    )
    
    pil_image = torchvision.transforms.functional.to_pil_image(response.img)
    
    fer_dict_str = str({face.indx: face.preds["fer"].label for face in response.faces})
    deepfake_dict_str = str({face.indx: face.preds["deepfake"].label for face in response.faces})
    response_str = str(response)
    
    out_tuple = (pil_image, fer_dict_str, deepfake_dict_str, response_str)
    return out_tuple


title = "facetorch"
description = "Demo for facetorch, a Python library that can detect faces and analyze facial features using deep neural networks. The goal is to gather open sourced face analysis models from the community and optimize them for performance using TorchScrip. Try selecting one of the example images or upload your own."
article = "<p style='text-align: center'><a href='https://github.com/tomas-gajarsky/facetorch' target='_blank'>Github Repo</a></p>"

demo=gr.Interface(
    inference,
    [gr.inputs.Image(label="Input", type="filepath")],
    [gr.outputs.Image(type="pil", label="Output"),
     gr.outputs.Textbox(label="Facial Expression Recognition"),
     gr.outputs.Textbox(label="DeepFake Detection"),
     gr.outputs.Textbox(label="Response")],
    title=title,
    description=description,
    article=article,
    examples=[["./test.jpg"], ["./test2.jpg"], ["./test3.jpg"], ["./test4.jpg"]],
)
demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)