Attempt to fix pipeline inference issue

config.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:81c482c90c48b46c5f596d6668d2156f438723e160ffe30628880b28fdfc38ab
-size 241
+oid sha256:81be9128702e1663bf6780233ee168b71b7299c2e42438fbb4a9129bcda41b18
+size 265

pipeline.py

@@ -1,35 +1,29 @@
 from transformers import Pipeline
-import tensorflow as tf
 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 json
 import numpy as np
+import tensorflow as tf
+import json
 
 class NewsClassifierPipeline(Pipeline):
-    def __init__(self):
+    def __init__(self, model_path="news_classifier.h5", tokenizer_path="tokenizer.json"):
         super().__init__()
-        self.model = load_model('./news_classifier.h5')
-        with open('tokenizer.json') as f:
+        self.model = load_model(model_path)
+        with open(tokenizer_path, "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)
-        return padded
+    def preprocess(self, inputs):
+        sequences = self.tokenizer.texts_to_sequences([inputs])
+        return pad_sequences(sequences, maxlen=128)
 
-    def _forward(self, texts):
-        processed = self.preprocess(texts)
-        predictions = self.model.predict(processed)
-        scores = tf.nn.softmax(predictions, axis=1)
-        
-        predicted_class = np.argmax(predictions)
-        score = float(np.max(scores))
-        
-        label = 'foxnews' if predicted_class == 0 else 'nbc'
-        
-        return [{'label': label, 'score': score}]
+    def _forward(self, inputs):
+        preprocessed = self.preprocess(inputs)
+        predictions = self.model.predict(preprocessed)
+        scores = tf.nn.softmax(predictions, axis=1).numpy()
+        label = np.argmax(scores)
+        return [{"label": "foxnews" if label == 0 else "nbc", "score": float(scores[0, label])}]
 
     def postprocess(self, model_outputs):
-        return model_outputs
+        return model_outputs