Delete predict_emotions.py

predict_emotions.py

@@ -1,48 +0,0 @@
-import torch
-from transformers import BertTokenizer, DistilBertForSequenceClassification
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load the trained model and tokenizer
-try:
-    model = DistilBertForSequenceClassification.from_pretrained("./saved_model")
-    tokenizer = BertTokenizer.from_pretrained("./saved_model")
-except Exception as e:
-    print(f"Error loading model or tokenizer: {e}")
-    exit()
-
-model.to(device)
-model.eval()
-
-# Define the sentences
-sentences = [
-    "I am so happy today!",
-    "This is the worst day ever.",
-    "I feel so loved and appreciated.",
-    "I am really angry right now.",
-    "I am so done cant take this anymore",
-    "i have to finish this report by tomorrow but so tired",
-    "let's do it",
-    "i have got this,, yayyyy",
-    "energetic",
-    "worst tired lazy",
-    "I am feeling very sad and lonely."
-]
-
-# Define the label names
-label_names = ["admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion", "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism", "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"]
-
-def predict_emotion(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() if key != "token_type_ids"}
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0]
-
-    return predicted_class, label_names[predicted_class]
-
-# Predict emotions for the sentences
-for sentence in sentences:
-    predicted_emotion, predicted_label_name = predict_emotion(sentence)
-    print(f"Predicted emotion for '{sentence}': {predicted_emotion} ({predicted_label_name})")