| import numpy as np |
| from tensorflow.keras.models import load_model |
| from tensorflow.keras.preprocessing import image |
| import os |
|
|
|
|
| class PredictionPipeline: |
| def __init__(self, filename): |
| self.filename = filename |
|
|
| def predict(self): |
| model = load_model(os.path.join("model", "model.h5")) |
|
|
| imagename = self.filename |
| test_image = image.load_img(imagename, target_size=(150, 150)) |
| test_image = image.img_to_array(test_image) |
| test_image = np.expand_dims(test_image, axis=0) |
| result = np.argmax(model.predict(test_image), axis=1) |
|
|
| if result[0] == 0: |
| prediction = "Cyst" |
| return [{"image": prediction}] |
| elif result[0] == 1: |
| prediction = "Normal" |
| return [{"image": prediction}] |
| elif result[0] == 2: |
| prediction = "Stone" |
| return [{"image": prediction}] |
| else: |
| prediction = "Tumor" |
| return [{"image": prediction}] |
|
|
