| from transformers import Pipeline |
| from tensorflow.keras.models import load_model |
| from tensorflow.keras.preprocessing.text import tokenizer_from_json |
| from tensorflow.keras.preprocessing.sequence import pad_sequences |
| import numpy as np |
| import tensorflow as tf |
| import json |
|
|
| class NewsClassifierPipeline(Pipeline): |
| def __init__(self): |
| super().__init__() |
| self.model = load_model('./news_classifier.h5') |
| with open('./tokenizer.json', 'r') as f: |
| tokenizer_data = json.load(f) |
| self.tokenizer = tokenizer_from_json(tokenizer_data) |
|
|
| def preprocess(self, text): |
| sequence = self.tokenizer.texts_to_sequences([text]) |
| padded = pad_sequences(sequence, maxlen=128) |
| return padded |
|
|
| def _forward(self, inputs): |
| predictions = self.model.predict(inputs) |
| scores = tf.nn.softmax(predictions, axis=1).numpy() |
| label = np.argmax(scores, axis=1)[0] |
| return [{"label": "foxnews" if label == 0 else "nbc", "score": float(scores[0][label])}] |
|
|
| def postprocess(self, model_outputs): |
| return model_outputs |
|
|
