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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", live=True)
with gr.Column():
output_lbl = gr.Label(value="Output", label="Expression Prediction")
input_img.change(fn=predict, inputs=input_img, outputs=output_lbl)
if __name__ == "__main__":
demo.launch() |