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handler.py

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+
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+import io
+
+class InferenceHandler:
+    def __init__(self):
+        self.model = tf.saved_model.load("pneumonia_cnn_saved_model")
+        self.class_names = ['PNEUMONIA', 'NORMAL']
+        self.infer = self.model.signatures['serving_default']
+
+    def preprocess(self, image):
+        img = np.array(image.convert('L'))  # Grayscale
+        img = cv2.resize(img, (150, 150))
+        img = img / 255.0
+        img = img.reshape(1, 150, 150, 1).astype(np.float32)
+        return img
+
+    def __call__(self, inputs):
+        # inputs: dict with 'image' key (e.g., uploaded image)
+        image = Image.open(io.BytesIO(inputs['image']))
+        img_array = self.preprocess(image)
+        prediction = self.infer(tf.convert_to_tensor(img_array))['dense_1'].numpy()
+        class_id = (prediction > 0.5).astype("int32")[0][0]
+        return {"prediction": self.class_names[class_id], "probability": float(prediction[0][0])}