modified pipeline and config according to model summary

config.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ae409354ee5a0f6edfd67b5b838c072be95c352a1e1faca73a2473ee8ac15253
-size 286
+oid sha256:ca780bf53be3df893073f50ad0e8218deae4f6c157eb3184480b43e3e86841fb
+size 323

pipeline.py

@@ -10,14 +10,16 @@ class NewsClassifierConfig(PretrainedConfig):
     
     def __init__(
         self,
-        max_length=128,
-        vocab_size=10000,
-        hidden_size=64,
+        max_length=41,  # Modified to match model input shape
+        vocab_size=74934,  # Modified based on embedding layer size
+        embedding_dim=128,  # Added to match model architecture
+        hidden_size=64,  # Matches final LSTM layer
         num_labels=2,
         **kwargs
     ):
         self.max_length = max_length
         self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
         self.hidden_size = hidden_size
         self.num_labels = num_labels
         super().__init__(**kwargs)
@@ -28,26 +30,34 @@ class NewsClassifier(PreTrainedModel):
 
     def __init__(self, config):
         super().__init__(config)
+        self.model = None
+        self.tokenizer = None
+        
+    def post_init(self):
+        """Load model and tokenizer after initialization"""
         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 forward(self, text_input):
+        if not self.model or not self.tokenizer:
+            self.post_init()
+            
         if isinstance(text_input, str):
             text_input = [text_input]
             
         sequences = self.tokenizer.texts_to_sequences(text_input)
         padded = pad_sequences(sequences, maxlen=self.config.max_length)
-        predictions = self.model.predict(padded)
+        predictions = self.model.predict(padded, verbose=0)
         
         results = []
         for pred in predictions:
-            label = "foxnews" if pred[0] > 0.5 else "nbc"
-            score = float(pred[0] if label == "foxnews" else 1 - pred[0])
+            score = float(pred[1])
+            label = "foxnews" if score > 0.5 else "nbc"
             results.append({
                 "label": label,
-                "score": score
+                "score": score if label == "foxnews" else 1 - score
             })
         
         return results[0] if len(text_input) == 1 else results