Upload usage_example.py with huggingface_hub

usage_example.py

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+
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import json
+
+# Download model and class names from Hugging Face
+model_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="model.keras")
+class_names_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="class_names.json")
+
+# Load model and class names
+model = tf.keras.models.load_model(model_path)
+with open(class_names_path, 'r') as f:
+    class_names = json.load(f)
+
+def preprocess_image(image_path):
+    """Preprocess image for model prediction"""
+    image = Image.open(image_path).convert('RGB')
+    image = image.resize((224, 224))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def predict_brain_tumor(image_path):
+    """Predict brain tumor from MRI image"""
+    image = preprocess_image(image_path)
+    predictions = model.predict(image)
+    predicted_idx = np.argmax(predictions[0])
+    confidence = float(predictions[0][predicted_idx])
+    
+    return {
+        "predicted_class": class_names[predicted_idx],
+        "confidence": confidence,
+        "all_predictions": {class_names[i]: float(predictions[0][i]) for i in range(len(class_names))}
+    }
+
+# Example usage:
+# result = predict_brain_tumor("path/to/your/mri_image.jpg")
+# print(f"Prediction: {result['predicted_class']} (Confidence: {result['confidence']:.2%})")