updated inference code
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1ee1f001f8
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@ -21,10 +21,10 @@ async fn get_predict() -> ApiResult {
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let result = Python::with_gil(|py| -> PyResult<()> {
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let result = Python::with_gil(|py| -> PyResult<()> {
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let predict: Py<PyAny> = PyModule::from_code_bound(py, MODULE, "model.py", "model")?
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let predict: Py<PyAny> = PyModule::from_code_bound(py, MODULE, "model.py", "model")?
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.getattr("predict")?.into();
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.getattr("predict")?.into();
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//TODO: Return result
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//TODO: Get result and return it
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Ok(())
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Ok(())
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});
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});
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//TODO: Return results
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Ok(Response::builder().body(Body::from(""))?)
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Ok(Response::builder().body(Body::from(""))?)
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}
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}
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@ -1,12 +1,30 @@
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import io
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import io
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import tensorflow as tf
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import numpy as np
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import os
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from PIL import Image
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from PIL import Image
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import tensorflow as tf
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MODEL_NAME = "model.keras"
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def preprocess_image_bytes(bytes):
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image = Image.open(io.BytesIO(bytes))
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image = np.array(image)
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image = np.expand_dims(image, axis=0)
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return image
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def process_model_bytes(bytes):
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open(MODEL_NAME, "wb").write(bytes)
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model = tf.keras.models.load_model(MODEL_NAME)
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os.remove(MODEL_NAME)
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return model
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def predict(model_bytes, image_bytes):
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def predict(model_bytes, image_bytes):
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model_file = io.BytesIO(model_bytes)
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image = preprocess_image_bytes(image_bytes)
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model = tf.keras.models.load(model_file)
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model = process_model_bytes(model_bytes)
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image = Image.open(io.BytesIO(image_bytes))
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return model.predict(image)
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return model.predict(image)
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