Create test.py

test.py

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+"""
+Support Ticket Classifier -Inference Script
+Author: Adedayo Adebayo
+Date: 2026-03-14
+"""
+
+import torch
+import logging
+from typing import List, Dict, Any
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_inference_tools(model_path: str):
+    logger.info(f"Loading resources from: {model_path}")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    model.to(device)
+    model.eval()
+    return tokenizer, model, device
+
+def get_predictions(texts: List[str], tokenizer, model, device) -> List[Dict[str, Any]]:
+    id2label = model.config.id2label
+    
+    inputs = tokenizer(
+        texts, 
+        padding=True, 
+        truncation=True, 
+        max_length=128, 
+        return_tensors="pt"
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        confidences, predictions = torch.max(probs, dim=-1)
+
+    results = []
+    for text, pred_id, conf in zip(texts, predictions, confidences):
+        results.append({
+            "text": text,
+            "category": id2label[pred_id.item()],
+            "confidence": conf.item()
+        })
+    return results
+
+def main():
+    MODEL_PATH = "Adedayo2000/BERTweet-Support-Ticket-Classifier" 
+    
+    input_ticket = [
+        "My account is locked,help!.",
+        "The latest update broke the export feature on the dashboard.",
+        "The app is crashing when I try to upload a file.",
+        "How do I change the notification settings for my team?",
+        "I need to reset my password but the reset link isn't working.",
+        "The new UI is confusing and hard to navigate.",
+        "I was double charged for my subscription this month, please assist."
+    ]
+    
+    tokenizer, model, device = load_inference_tools(MODEL_PATH)
+    
+    logger.info("Classifying tickets...")
+    predictions = get_predictions(input_ticket, tokenizer, model, device)
+
+    print(f"{'TICKET PREVIEW':<60} | {'CATEGORY':<15} | {'CONF'}")
+    
+    for res in predictions:
+        preview = (res['text'][:40] + '...') if len(res['text']) > 40 else res['text']
+        print(f"{preview:<60} | {res['category']:<15} | {res['confidence']:.2%}")
+
+if __name__ == "__main__":
+    main()