Add inference widget and pipeline configuration

README.md

@@ -1,24 +1,16 @@
 ---
+language: en
 license: mit
 tags:
 - text-classification
-- tensorflow
 - news-classification
 pipeline_tag: text-classification
+inference: true
 widget:
-  - text: "Enter your news headline here"
-datasets:
-- custom_news_dataset
-model-index:
-- name: news-source-classifier
-  results:
-  - task: 
-      type: text-classification
-      name: News Source Classification
-    metrics:
-      - type: accuracy
-        value: 0.82
-        name: Test Accuracy
+- example_title: "Write a news headline to classify"
+  text: "Wife of murdered Minnesota pastor hired 3 men to kill husband after affair: police"
+- example_title: "Another example"
+  text: "Scientists discover breakthrough in renewable energy research"
 ---
 
 # News Source Classifier

pipeline.py

@@ -0,0 +1,36 @@
+# pipeline.py
+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
+
+class NewsClassifierPipeline(Pipeline):
+    def __init__(self):
+        super().__init__()
+        self.model = load_model('news_classifier.h5')
+        with open('tokenizer.json') 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 _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 postprocess(self, model_outputs):
+        return model_outputs

requirements.txt

@@ -1,8 +1,13 @@
-tensorflow==2.13.0
+tensorflow-macos>=2.10.0
+#tensorflow==2.10.0
+transformers>=4.30.0
+torch>=2.0.0
 fastapi==0.68.1
 uvicorn==0.15.0
 numpy>=1.19.2
 pydantic==1.8.2
 python-multipart==0.0.5
 scikit-learn>=0.24.2
-joblib>=1.1.0
+joblib>=1.1.0
+sentencepiece
+protobuf