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import gradio as gr |
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from fastai.vision.all import * |
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from transformers import AutoImageProcessor, AutoModelForImageClassification |
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from PIL import Image |
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import requests |
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import face_recognition |
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learn_inf = load_learner("export.pkl") |
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processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection") |
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model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection") |
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def extract_face(image)-> Image.Image: |
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face_locations = face_recognition.face_locations(image) |
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if face_locations: |
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top, right, bottom, left = face_locations[0] |
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face_image = Image.fromarray(image[top:bottom, left:right]) |
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return face_image |
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else: |
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return image |
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def predict(value) -> str: |
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image = extract_face(Image.fromarray(value)).convert("L") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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return model.config.id2label[predicted_class_idx] |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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input_img = gr.Image(label="Input", sources="webcam") |
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with gr.Column(): |
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output_lbl = gr.Label(value="Output", label="Expression Prediction") |
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input_img.stream(fn=predict, inputs=input_img, outputs=output_lbl, time_limit=15, stream_every=0.1, concurrency_limit=30) |
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if __name__ == "__main__": |
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demo.launch() |