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23b40a9 5d7a2e7 23b40a9 5d7a2e7 23b40a9 5d7a2e7 23b40a9 5d7a2e7 23b40a9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import io
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers
from fastapi import FastAPI, File, UploadFile, Form, HTTPException
from PIL import Image
app = FastAPI(title="DyslexiaLens Prediction API")
# ─── RE-REGISTER CUSTOM ARCHITECTURE COMPONENTS ───
# CHANGED: Swapped .saving to .utils to match the tf.keras wrapper ecosystem
@keras.utils.register_keras_serializable(package="Custom")
class AdaptiveContrastNorm(layers.Layer):
def __init__(self, epsilon: float = 1e-6, **kwargs):
super().__init__(**kwargs)
self.epsilon = epsilon
def build(self, input_shape):
channels = input_shape[-1]
self.gamma = self.add_weight(name='gamma', shape=(1, 1, 1, channels), initializer='ones', trainable=True)
self.beta = self.add_weight(name='beta', shape=(1, 1, 1, channels), initializer='zeros', trainable=True)
super().build(input_shape)
def call(self, x, training=None):
axes = [1, 2]
mu = tf.reduce_mean(x, axis=axes, keepdims=True)
sigma = tf.math.reduce_std(x, axis=axes, keepdims=True) + self.epsilon
return self.gamma * ((x - mu) / sigma) + self.beta
def get_config(self):
config = super().get_config()
config.update({'epsilon': self.epsilon})
return config
# CHANGED: Swapped .saving to .utils here as well
@keras.utils.register_keras_serializable(package="Custom")
class MaskedHuberLoss(keras.losses.Loss):
def __init__(self, delta: float = 0.5, **kwargs):
super().__init__(**kwargs)
self.delta = delta
self._huber_fn = keras.losses.Huber(delta=delta, reduction='none')
def call(self, y_true, y_pred):
y_true = tf.cast(tf.reshape(y_true, [-1, 1]), tf.float32)
y_pred = tf.cast(tf.reshape(y_pred, [-1, 1]), tf.float32)
mask = tf.cast(y_true > 0.0, tf.float32)
per_sample = self._huber_fn(y_true, y_pred)
masked = per_sample * tf.squeeze(mask, axis=-1)
return tf.reduce_sum(masked) / (tf.reduce_sum(mask) + 1e-8)
def get_config(self):
config = super().get_config()
config.update({'delta': self.delta})
return config
# ─── GLOBAL WEIGHT LOADING ───
MODEL_PATH = "dyslexialens_model.keras"
model = None
@app.on_event("startup")
def load_model():
global model
try:
model = keras.models.load_model(
MODEL_PATH,
custom_objects={
'AdaptiveContrastNorm': AdaptiveContrastNorm,
'MaskedHuberLoss': MaskedHuberLoss
}
)
print("Model successfully loaded onto CPU context.")
except Exception as e:
print(f"Error loading Keras model: {str(e)}")
@app.get("/")
def health_check():
return {"status": "online", "model": "DyslexiaLens Late Fusion Pipeline ready"}
@app.post("/predict")
async def predict(
stroke_density: float = Form(...),
center_of_mass_x: float = Form(...),
center_of_mass_y: float = Form(...),
bounding_box_ratio: float = Form(...),
stroke_transitions: float = Form(...),
horizontal_symmetry: float = Form(...),
file: UploadFile = File(...)
):
if model is None:
raise HTTPException(status_code=503, detail="Model is loading or uninitialized.")
try:
contents = await file.read()
image = Image.open(io.BytesIO(contents)).convert('L')
image = image.resize((128, 128), Image.BILINEAR)
img_array = np.array(image, dtype=np.float32) / 255.0
img_tensor = np.expand_dims(img_array, axis=(0, -1))
feature_vector = np.array([
stroke_density, center_of_mass_x, center_of_mass_y,
bounding_box_ratio, stroke_transitions, horizontal_symmetry
], dtype=np.float32).reshape(1, 6)
predictions = model.predict({
'image_input': img_tensor,
'feature_input': feature_vector
})
clf_probability = float(predictions[0][0][0])
severity_score = float(predictions[1][0][0])
is_dyslexia = clf_probability >= 0.40
return {
"status": "success",
"prediction": {
"has_dyslexia": is_dyslexia,
"dyslexia_probability": round(clf_probability, 4),
"severity_score": round(severity_score, 4)
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Inference Failure: {str(e)}") |