Delete test.py

test.py

@@ -1,75 +0,0 @@
-"""
-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()