Fix deployment issues

README.md

@@ -3,7 +3,6 @@ language: en
 license: mit
 tags:
 - text-classification
-- news-classification
 pipeline_tag: text-classification
 inference: true
 widget:

pipeline.py

@@ -7,23 +7,23 @@ import tensorflow as tf
 import json
 
 class NewsClassifierPipeline(Pipeline):
-    def __init__(self, model_path="news_classifier.h5", tokenizer_path="tokenizer.json"):
+    def __init__(self):
         super().__init__()
-        self.model = load_model(model_path)
-        with open(tokenizer_path, "r") as f:
+        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, inputs):
-        sequences = self.tokenizer.texts_to_sequences([inputs])
-        return pad_sequences(sequences, maxlen=128)
+    def preprocess(self, text):
+        sequence = self.tokenizer.texts_to_sequences([text])
+        padded = pad_sequences(sequence, maxlen=128)
+        return padded
 
     def _forward(self, inputs):
-        preprocessed = self.preprocess(inputs)
-        predictions = self.model.predict(preprocessed)
+        predictions = self.model.predict(inputs)
         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])}]
+        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

requirements.txt

@@ -1,9 +1,5 @@
-#tensorflow-macos>=2.10.0
 tensorflow>=2.10.0
 transformers>=4.30.0
 torch>=2.0.0
 numpy>=1.19.2
 scikit-learn>=0.24.2
-fastapi==0.68.1
-uvicorn==0.15.0
-pydantic==1.8.2