import torch from transformers import BertTokenizer, BertForSequenceClassification, DistilBertForSequenceClassification from datetime import datetime device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load the intent classifier model and tokenizer num_intent_labels = 151 # Set the correct number of labels for the intent classifier intent_model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_intent_labels) intent_model.load_state_dict(torch.load("intent_classifier.pth")) intent_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") intent_model.to(device) intent_model.eval() # Load the emotions model and tokenizer emotions_model = DistilBertForSequenceClassification.from_pretrained("./saved_model") emotions_tokenizer = BertTokenizer.from_pretrained("./saved_model") emotions_model.to(device) emotions_model.eval() # Define the label names for emotions emotion_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_intent(sentence): inputs = intent_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 = intent_model(**inputs) predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0] return predicted_class def predict_emotion(sentence): inputs = emotions_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 = emotions_model(**inputs) predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0] return predicted_class, emotion_label_names[predicted_class] def calculate_priority_score(intent, emotion, time_remaining): # Example priority score calculation intent_weight = 0.4 emotion_weight = 0.3 time_weight = 0.3 # Normalize time_remaining to a score between 0 and 1 time_score = max(0, min(1, 1 - (time_remaining.total_seconds() / (24 * 3600)))) # Calculate priority score priority_score = (intent * intent_weight) + (emotion * emotion_weight) + (time_score * time_weight) return priority_score def prioritize_task(task_description, due_date_time, predicted_emotion, predicted_label_name): predicted_intent = predict_intent(task_description) # Calculate time remaining until the due date and time due_date_time = datetime.strptime(due_date_time, "%Y-%m-%d %H:%M:%S") time_remaining = due_date_time - datetime.now() priority_score = calculate_priority_score(predicted_intent, predicted_emotion, time_remaining) return { "description": task_description, "due_date_time": due_date_time, "time_remaining": time_remaining, "predicted_intent": predicted_intent, "predicted_emotion": predicted_emotion, "predicted_label_name": predicted_label_name, "priority_score": priority_score } # Example tasks tasks = [ {"description": "Finish the report by tomorrow.", "due_date_time": "2025-03-02 09:00:00"}, {"description": "meeting", "due_date_time": "2025-03-02 12:00:00"}, {"description": "listen to music.", "due_date_time": "2025-03-02 15:00:00"}, {"description": "daily linkedin queens game.", "due_date_time": "2025-03-02 18:00:00"}, {"description": "prepare ppt", "due_date_time": "2025-03-02 21:00:00"} ] # Overall emotion sentence emotion_sentence = "I am feeling very tired and stressed now" predicted_emotion, predicted_label_name = predict_emotion(emotion_sentence) # Prioritize tasks prioritized_tasks = [] for task in tasks: prioritized_tasks.append(prioritize_task(task["description"], task["due_date_time"], predicted_emotion, predicted_label_name)) # Reorder tasks based on priority score (descending order) prioritized_tasks.sort(key=lambda x: x["priority_score"], reverse=True) # Print prioritized tasks for task in prioritized_tasks: print(f"Task Description: '{task['description']}'") print(f"Due Date and Time: {task['due_date_time']}") print(f"Time Remaining: {task['time_remaining']}") print(f"Predicted Intent: {task['predicted_intent']}") print(f"Predicted Emotion: {task['predicted_emotion']} ({task['predicted_label_name']})") print(f"Priority Score: {task['priority_score']:.4f}") print()