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from tensorflow.keras.models import load_model |
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from PIL import Image |
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import numpy as np |
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model = load_model("my_model.h5") |
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emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"] |
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def preprocess(image): |
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image = image.convert("L").resize((48, 48)) |
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arr = np.array(image) / 255.0 |
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arr = np.expand_dims(arr, axis=(0, -1)) |
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return arr |
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def predict(image): |
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img = preprocess(image) |
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pred = model.predict(img) |
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label = emotions[np.argmax(pred)] |
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return {"label": label, "score": float(np.max(pred))} |
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