| import gradio as gr | |
| from fastai.vision.all import * | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| from PIL import Image | |
| import requests | |
| learn_inf = load_learner("export.pkl") | |
| processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection") | |
| model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection") | |
| def predict(value) -> str: | |
| image = Image.fromarray(value).convert("L").convert("RGB") | |
| inputs = processor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class_idx = logits.argmax(-1).item() | |
| return model.config.id2label[predicted_class_idx] | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(label="Input", sources="webcam") | |
| with gr.Column(): | |
| output_lbl = gr.Label(value="Output", label="Expression Prediction") | |
| input_img.stream(fn=predict, inputs=input_img, outputs=output_lbl,concurrency_limit=20,time_limit=20,stream_every=0.1) | |
| if __name__ == "__main__": | |
| demo.launch() |