Create model.py

model.py

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+import tensorflow as tf
+from fastapi import FastAPI
+from PIL import Image
+import numpy as np
+
+class BrainTumorDetector:
+    def __init__(self, model_path):
+        self.model = tf.keras.models.load_model(model_path)
+
+    def predict(self, image: Image.Image):
+        image = image.resize((224, 224))  # Adjust size as per your model
+        image_array = np.array(image) / 255.0  # Normalize
+        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+        predictions = self.model.predict(image_array)
+        return predictions
+
+# Initialize the model detector (this path will be relative to your model repository)
+detector = BrainTumorDetector("path/to/your/model.h5")