Delete predict_intent.py

predict_intent.py

@@ -1,48 +0,0 @@
-import torch
-from transformers import BertTokenizer, BertForSequenceClassification
-from datasets import load_dataset
-from collections import Counter
-
-# Check for CUDA
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load dataset and get correct label names
-dataset = load_dataset("clinc_oos", "plus")
-label_names = dataset["train"].features["intent"].names  # Ensure correct order
-
-# Debugging check
-print(f"Total labels: {len(label_names)}")  # Should print 151
-print("Sample labels:", label_names[:10])  # Print first 10 labels
-
-# Load the trained model
-num_labels = len(label_names)  # Should be 151
-model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_labels)
-model.load_state_dict(torch.load("intent_classifier.pth", map_location=device))
-model.to(device)
-model.eval()
-
-# Load tokenizer
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-
-def predict_intent(sentence):
-    inputs = tokenizer(sentence, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
-    inputs = {key: val.to(device) for key, val in inputs.items()}
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0]
-
-    if predicted_class >= len(label_names):  # Prevent out-of-range errors
-        print(f"Warning: Predicted class {predicted_class} is out of range!")
-        return predicted_class, "Unknown Label"
-
-    return predicted_class, label_names[predicted_class]
-
-# Example usage
-sentence = "I need to attend a meeting but so tired but important"
-predicted_intent, predicted_label_name = predict_intent(sentence)
-print(f"Predicted intent for '{sentence}': {predicted_intent} ({predicted_label_name})")
-
-# # Fix: Count labels correctly from dataset["train"]
-# label_counts = Counter([label_names[label] for label in dataset["train"]["intent"]])  
-# print("Label distribution:", label_counts)  # Print top 10 most common labels