Create predict.py

predict.py

@@ -0,0 +1,24 @@
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import json
+
+# Load configuration
+with open('../config/config.json') as f:
+    config = json.load(f)
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('../model')
+tokenizer = AutoTokenizer.from_pretrained(config['model_name'])
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=-1)
+    return prediction.item()
+
+# Example usage
+text = "Example text for prediction"
+prediction = predict(text)
+print(f"Prediction: {prediction}")