| import tensorflow as tf |
| import numpy as np |
| from PIL import Image |
|
|
| MODEL_PATH = "best_tomato_model.h5" |
| IMG_SIZE = 160 |
|
|
| model = tf.keras.models.load_model(MODEL_PATH) |
|
|
|
|
| def preprocess(image): |
|
|
| image = image.resize((IMG_SIZE, IMG_SIZE)) |
| image = np.array(image) / 255.0 |
| image = np.expand_dims(image, axis=0) |
|
|
| return image |
|
|
|
|
| def load_model_and_predict(image): |
|
|
| img = preprocess(image) |
|
|
| pred = model.predict(img) |
|
|
| class_index = np.argmax(pred) |
| confidence = np.max(pred) |
|
|
| |
| class_names = [ |
| 'Tomato___Bacterial_spot', |
| 'Tomato___Early_blight', |
| 'Tomato___Late_blight', |
| 'Tomato___Leaf_Mold', |
| 'Tomato___Septoria_leaf_spot', |
| 'Tomato___Spider_mites Two-spotted_spider_mite', |
| 'Tomato___Target_Spot', |
| 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', |
| 'Tomato___Tomato_mosaic_virus', |
| 'Tomato___healthy' |
| ] |
|
|
| return class_names[class_index], float(confidence) |
