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3e93e14 1afce9a 97f491b 3e93e14 a356dcd 3e93e14 a356dcd 3e93e14 a356dcd 3e93e14 1afce9a 3e93e14 1afce9a 97f491b 1afce9a 3e93e14 97f491b | 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 | import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import os
# CT scans are grayscale — all three RGB channels are nearly identical.
# This threshold is the max allowed std-dev of inter-channel pixel differences.
# True CT scans score ~0–8; colour photos score ~20–80+.
GRAYSCALE_THRESHOLD = 15.0
def _is_ct_like(img_array):
"""Return True if the image looks like a grayscale CT scan."""
r = img_array[:, :, 0].astype(float)
g = img_array[:, :, 1].astype(float)
b = img_array[:, :, 2].astype(float)
max_channel_diff = max(np.std(r - g), np.std(r - b), np.std(g - b))
return max_channel_diff < GRAYSCALE_THRESHOLD
class PredictionPipeline:
def __init__(self, filename, model=None):
self.filename = filename
self._model = model
def predict(self):
# Use pre-loaded model if provided, otherwise load from disk
if self._model is not None:
model = self._model
else:
keras_path = os.path.join("artifacts", "training", "model.keras")
h5_path = os.path.join("artifacts", "training", "model.h5")
model_path = keras_path if os.path.isfile(keras_path) else h5_path
model = load_model(model_path)
img = image.load_img(self.filename, target_size=(224, 224))
img_array = image.img_to_array(img) # shape (224, 224, 3), values 0–255
# Reject non-CT images before they reach the model
if not _is_ct_like(img_array):
return [{"image": "InvalidImage"}]
img_input = np.expand_dims(img_array, axis=0) / 255.0
predictions = model.predict(img_input)
class_idx = int(np.argmax(predictions, axis=1)[0])
confidence = float(np.max(predictions))
return [{"image": "Tumor" if class_idx == 1 else "Normal",
"confidence": round(confidence, 4)}]
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