Update pipeline to resolve tranformer issue

pipeline.py

@@ -2,6 +2,7 @@ from transformers import PreTrainedModel, PretrainedConfig
 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 os
 import numpy as np
 import json
 
@@ -17,15 +18,35 @@ class NewsClassifier(PreTrainedModel):
     
     def __init__(self, config):
         super().__init__(config)
-        self.model = load_model('./news_classifier.h5')
-        with open('./tokenizer.json', 'r') as f:
+        model_path = os.path.join(os.path.dirname(__file__), 'news_classifier.h5')
+        tokenizer_path = os.path.join(os.path.dirname(__file__), 'tokenizer.json')
+        
+        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 forward(self, text_input):
+        if isinstance(text_input, str):
+            sequences = self.tokenizer.texts_to_sequences([text_input])
+        else:
+            sequences = self.tokenizer.texts_to_sequences(text_input)
             
-    def forward(self, inputs):
-        sequences = self.tokenizer.texts_to_sequences([inputs])
         padded = pad_sequences(sequences, maxlen=self.config.max_length)
         predictions = self.model.predict(padded)
-        scores = predictions[0]
-        label = "foxnews" if scores[0] > 0.5 else "nbc"
-        return {"label": label, "score": float(scores[0] if label == "foxnews" else 1 - scores[0])}
+        
+        results = []
+        for score in predictions:
+            label = "foxnews" if score[0] > 0.5 else "nbc"
+            results.append({
+                "label": label,
+                "score": float(score[0] if label == "foxnews" else 1 - score[0])
+            })
+        
+        return results[0] if isinstance(text_input, str) else results
+
+    @classmethod
+    def from_pretrained(cls, model_path, **kwargs):
+        config = NewsClassifierConfig.from_pretrained(model_path)
+        model = cls(config)
+        return model