Create scripts/main.py

scripts/main.py

@@ -0,0 +1,40 @@
+# scripts/main.py
+from src.preprocessing import preprocess_text
+from src.utils import load_model_and_tokenizer
+from src.feature_engineering import tokenize_texts
+import torch
+import logging
+from src.config import MODEL_PATH
+
+def setup_logging():
+    logging.basicConfig(filename="logs/app.log", level=logging.INFO, 
+                        format="%(asctime)s - %(levelname)s - %(message)s")
+
+def predict_text(text, model, tokenizer):
+    """Predict category for input text."""
+    setup_logging()
+    cleaned_text = preprocess_text(text)
+    encodings = tokenizer([cleaned_text], truncation=True, padding=True, max_length=128, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**encodings)
+        logits = outputs.logits
+        pred_label = torch.argmax(logits, dim=1).item()
+    label_map = {0: "Electronics", 1: "Household", 2: "Books", 3: "Clothing & Accessories"}
+    logging.info(f"Predicted {label_map[pred_label]} for text: {text[:50]}...")
+    return label_map[pred_label]
+
+def main():
+    model, tokenizer = load_model_and_tokenizer(MODEL_PATH)
+    while True:
+        text = input("Enter text (or 'quit' to exit): ")
+        if text.lower() == "quit":
+            break
+        try:
+            prediction = predict_text(text, model, tokenizer)
+            print(f"Predicted Category: {prediction}")
+        except Exception as e:
+            logging.error(f"Error predicting: {e}")
+            print("Error processing input. Try again.")
+
+if __name__ == "__main__":
+    main()