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import os
# Set the Keras backend. You can choose from "tensorflow", "jax", or "torch".
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
from huggingface_hub import hf_hub_download
from PIL import Image
import numpy as np
import warnings
# Suppress TensorFlow startup messages for a cleaner user experience
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
warnings.filterwarnings('ignore', category=FutureWarning)
class BrainTumorClassifier:
"""
A simple, self-contained tool to load the Shepard's Gift model
and classify brain MRI images.
Usage:
classifier = BrainTumorClassifier()
result = classifier.predict("path/to/your/image.jpg")
print(result)
"""
def __init__(self, repo_id="VeduCo/D-Shepard-p1-57", filename="ShepardsGift_final_model.keras"):
self._IMAGE_SIZE = (224, 224)
self._CLASS_NAMES = ['glioma', 'meningioma', 'no_tumor', 'pituitary']
try:
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
self.model = keras.saving.load_model(model_path, compile=False) # compile=False for faster loading
except Exception as e:
self.model = None
print(f"❌ Critical Error: Could not load the model. Please check your internet connection. Error: {e}")
def _preprocess_image(self, image_path: str) -> np.ndarray:
"""Internal helper to prepare an image for the model."""
input_image = Image.open(image_path).convert("RGB")
img = input_image.resize(self._IMAGE_SIZE)
img_array = keras.utils.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0
return img_array
def predict(self, image_path: str) -> dict:
"""
The main function. Takes a file path to an image and returns a
dictionary with the prediction and confidence scores.
"""
if self.model is None:
return {"error": "Model is not loaded. Initialization failed."}
try:
processed_image = self._preprocess_image(image_path)
predictions = self.model.predict(processed_image, verbose=0)
score = keras.ops.softmax(predictions[0])
best_class_index = np.argmax(score)
best_class = self._CLASS_NAMES[best_class_index]
confidence = float(score[best_class_index])
return {
"predicted_class": best_class,
"confidence": f"{confidence*100:.2f}%",
}
except FileNotFoundError:
return {"error": f"Image file not found at path: {image_path}"}
except Exception as e:
return {"error": f"An error occurred during prediction: {e}"}
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