import os # Force Hugging Face to use a writable directory os.environ["HF_HOME"] = "/tmp/huggingface" os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache" import streamlit as st import torch import os from dotenv import load_dotenv from together import Together from transformers import BertTokenizer,DistilBertTokenizer, BertForSequenceClassification, DistilBertForSequenceClassification from datetime import datetime, timedelta import pandas as pd from task_css import get_custom_css # Import the custom CSS function # Load environment variables load_dotenv() # Together AI Client with API key from environment variable client = Together(api_key=os.getenv("TOGETHER_API_KEY", "")) # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load Intent Model intent_model_path = os.path.join(os.path.dirname(__file__), "intent_classifier.pth") num_intent_labels = 151 intent_model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_intent_labels) intent_model.load_state_dict(torch.load(intent_model_path, map_location=device)) intent_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") intent_model.to(device).eval() # Load Emotion Model emotions_model_path = os.path.join(os.path.dirname(__file__), "saved_model") emotions_model = DistilBertForSequenceClassification.from_pretrained(emotions_model_path) emotions_tokenizer = DistilBertTokenizer.from_pretrained(emotions_model_path) emotions_model.to(device).eval() # Emotion Labels 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" ] # Emotion Categories positive_emotions = ["admiration", "amusement", "approval", "caring", "curiosity", "excitement", "gratitude", "joy", "love", "optimism", "pride", "relief", "surprise"] negative_emotions = ["anger", "annoyance", "disappointment", "disapproval", "disgust", "embarrassment", "fear", "grief", "nervousness", "remorse", "sadness"] neutral_emotions = ["realization", "neutral"] # Predict Intent 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] # Mapping Intent IDs to Priorities (0-150) PRIORITY_MAPPING = { 5: [8, 35, 42, 74, 97, 110, 118, 120, 124, 136], # freeze_account, report_lost_card, flight_status, report_fraud, credit_limit, lost_luggage, dispute_charge, overdraft, cancel_reservation, emergency 4: [14, 15, 19, 20, 39, 47, 48, 49, 50, 69, 70, 71, 72], # bill_balance, bill_due, exchange_rate, credit_score, interest_rate, insurance, medical_expenses, appointment_schedule, meeting_schedule, dentist_appointment, doctor_appointment, prescription_refill, pharmacy_hours 3: [33, 34, 41, 51, 56, 57, 62, 66, 77, 78, 85], # hotel_reservation, car_rental, restaurant_reservation, tracking_package, check_in, check_out, traffic_update, directions, smart_home_on, smart_home_off, weather_forecast 2: [0, 1, 3, 6, 9, 13, 16, 17, 21, 25, 27, 28, 36, 40, 45, 52, 61], # restaurant_reviews, shopping_list, what_song, schedule_meeting, translate, play_music, book_hotel, book_flight, gas_prices, exchange_rate, movie_showtimes, recipe, cancel_flight, book_reservation, order_food, car_services, joke 1: [2, 4, 5, 7, 10, 11, 12, 18, 22, 23, 24, 26, 30, 31, 32, 37, 38, 43, 44, 46, 53, 54, 55, 58, 59, 60, 63, 64, 65, 67, 68, 73] # tell_joke, fun_fact, trivia, horoscope, dog_fact, cat_fact, define_word, stock_price, sports_update, lottery_results, currency_conversion, holiday_list, language_learning, random_fact, poem, quote, daily_horoscope, joke_request, music_recommendation, podcast_recommendation, celebrity_gossip, movie_recommendation, TV_show_recommendation, book_recommendation, game_recommendation, radio_recommendation, trivia_game, riddle, name_meaning, birthday_reminder, anniversary_reminder, affirmations } # Find the priority based on predicted_class predicted_intent_score = next((priority for priority, ids in PRIORITY_MAPPING.items() if predicted_class in ids), 1) # Default to 1 if not found return predicted_intent_score # Emotion to Numeric Score Mapping EMOTION_MAPPING = { "admiration": 4, "amusement": 3, "anger": 5, "annoyance": 4, "approval": 3, "caring": 4, "confusion": 3, "curiosity": 3, "desire": 4, "disappointment": 4, "disapproval": 4, "disgust": 5, "embarrassment": 4, "excitement": 5, "fear": 5, "gratitude": 3, "grief": 5, "joy": 5, "love": 5, "nervousness": 4, "optimism": 4, "pride": 4, "realization": 3, "relief": 3, "remorse": 4, "sadness": 5, "surprise": 3, "neutral": 3 } # Function to get numeric emotion score def get_emotion_score(emotion): return EMOTION_MAPPING.get(emotion.lower(), 3) # Default to 3 if not found # Predict Emotion def predict_emotion(sentence): if not sentence.strip(): return 3, "neutral" # Ensure the input is a full sentence if len(sentence.split()) == 1: sentence = f"I feel {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] detected_emotion = emotion_label_names[predicted_class] # Manually adjust for stress/pressure-related words stress_keywords = ["stress", "stressed", "overwhelmed", "pressure", "tense", "burnout"] if any(word in sentence.lower() for word in stress_keywords): if detected_emotion not in ["sadness", "nervousness"]: detected_emotion = "nervousness" # Change to "sadness" if you prefer emotion_score = get_emotion_score(detected_emotion) if emotion_score is None: emotion_score = 3 # Default neutral score return emotion_score, detected_emotion # Get Emotion Category def get_emotion_category(emotion): if emotion in positive_emotions: return "positive" elif emotion in negative_emotions: return "negative" else: return "neutral" def normalize_priority(priority, min_value=0, max_value=10): return (priority - min_value) / (max_value - min_value) # Normalize between 0-1 # Calculate Task Priority def calculate_priority_score(predicted_intent_score,emotion_score, emotion, time_remaining, complexity, emotion_category): """ Calculate an adaptive priority score for tasks based on intent, emotion, time urgency, and complexity. """ emotion_score = emotion_score if emotion_score is not None else 3 # Normalize time urgency (scale 0 to 1 based on 7 days) time_score = max(0, min(1, 1 - (time_remaining.total_seconds() / (7 * 24 * 3600)))) # Set emotion-based adjustments stress_emotions = ["nervousness", "sadness", "fear"] frustration_emotions = ["anger", "frustration","disappointment","annoyance"] anxiety_emotions = ["anxiety", "uncertainty"] if emotion_category == "negative": if emotion in stress_emotions: # Prioritize **easy, quick** tasks to reduce cognitive load priority = (predicted_intent_score * 0.15) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.45) elif emotion in frustration_emotions: # Prioritize **engaging** tasks (not too easy) but keep urgency in mind priority = (predicted_intent_score * 0.2) + (emotion_score * 0.15) + (time_score * 0.25) + (complexity * 0.4) elif emotion in anxiety_emotions: # Prioritize **urgent, low-complexity** tasks priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.4) + ((10 - complexity) * 0.3) else: # Default for negative emotions: balance urgency and ease priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.4) elif emotion_category == "positive": # If the user is in a **good mood**, favor challenging, high-impact tasks priority = (predicted_intent_score * 0.35) + (emotion_score * 0.2) + (time_score * 0.25) + (complexity * 0.2) else: # Neutral emotion # Keep a balance between difficulty and urgency priority = (predicted_intent_score * 0.3) + (emotion_score * 0.2) + (time_score * 0.2) + (complexity * 0.3) return normalize_priority(priority) # Ensure no negative priority values # AI-Generated Plan Based on Start Time from datetime import datetime def get_llama_suggestion(emotion, tasks, selected_datetime): """Generate AI plan based on full datetime instead of just time""" # Sort tasks by priority (higher priority first) sorted_tasks = sorted(tasks, key=lambda x: x["priority_score"], reverse=True) # Filter tasks based on selected datetime filtered_tasks = [ task for task in sorted_tasks if task["due_date_time"] >= selected_datetime ] if not filtered_tasks: well_being_prompts = { "nervousness": "Suggest mindfulness exercises and short relaxation techniques.", "sadness": "Suggest comforting activities like journaling or light exercise.", "anger": "Suggest ways to channel frustration productively.", "joy": "Suggest ways to maintain productivity while feeling good.", "neutral": "Suggest general relaxation activities like listening to music." } well_being_prompt = f""" The user is feeling {emotion}. They have no tasks scheduled after {selected_datetime.strftime('%B %d, %I:%M %p')}. {well_being_prompts.get(emotion, 'Provide general well-being tips.')} """ try: response = client.chat.completions.create( messages=[{"role": "user", "content": well_being_prompt}], model="meta-llama/Llama-3.3-70B-Instruct-Turbo", temperature=0.7, ) return response.choices[0].message.content except Exception as e: return f"Error generating well-being tips: {e}" # Prepare the prompt with more detailed datetime information task_details = "\n".join([ f"- {task['description']} (Priority: {task['priority_score']:.2f}, Complexity: {task['complexity']}, Due: {task['due_date_time'].strftime('%B %d, %I:%M %p')})" for task in filtered_tasks ]) prompt = f""" The user is feeling {emotion}. They need a structured productivity plan starting from {selected_datetime.strftime('%B %d, %I:%M %p')}, not the current time. Their prioritized tasks (due on or after the selected time), sorted by priority score: {task_details} Please provide: 1. A detailed schedule with specific times for each task 2. Strategic breaks based on task complexity and emotional state 3. Wellness activities that complement their current emotion 4. Tips for managing tasks effectively given their emotional state 5. Suggestions for handling high-priority tasks first while maintaining well-being """ try: response = client.chat.completions.create( messages=[{"role": "user", "content": prompt}], model="meta-llama/Llama-3.3-70B-Instruct-Turbo", temperature=0.7, ) return response.choices[0].message.content except Exception as e: return f"Error generating AI plan: {e}" # Initialize Session State if "is_ready" not in st.session_state: st.session_state.is_ready = False st.session_state.models = {} # Initialize models dict immediately st.session_state.tasks = [] st.session_state.task_counter = 0 st.session_state.overall_emotion = None st.session_state.overall_emotion_label = "Neutral" # Page Configuration first st.set_page_config( page_title="🚀 AI Productivity Assistant", layout="wide", page_icon="🎯" ) # Custom CSS for enhanced styling st.markdown(get_custom_css(), unsafe_allow_html=True) # Show loading screen if models aren't ready if not st.session_state.is_ready: st.markdown( """

Setting up your AI assistant...

This may take a minute. We're downloading the required models.

""", unsafe_allow_html=True ) # Load models here try: # First download pretrained models if not os.path.exists("pretrained_models"): with st.status("Downloading base models...", expanded=True) as status: os.makedirs("pretrained_models", exist_ok=True) gdown.download_folder( f"https://drive.google.com/drive/folders/{pretrained_folder_id}", output="pretrained_models", quiet=False ) status.update(label="Base models downloaded!", state="complete") # Intent Model Loading if not os.path.exists(intent_model_path): with st.status("Downloading intent model...", expanded=True) as status: output = gdown.download( f"https://drive.google.com/uc?id={file_id}", intent_model_path, quiet=False ) status.update(label="Intent model downloaded!", state="complete") # Emotion Model Loading if not os.path.exists(emotions_model_path): with st.status("Downloading emotion model...", expanded=True) as status: os.makedirs(emotions_model_path, exist_ok=True) gdown.download_folder( f"https://drive.google.com/drive/folders/{emotions_folder_id}", output=emotions_model_path, quiet=False ) status.update(label="Emotion model downloaded!", state="complete") # Load and store intent model intent_model = AutoModelForSequenceClassification.from_pretrained( "pretrained_models/bert-base-uncased", num_labels=num_intent_labels, ignore_mismatched_sizes=True, # Add this parameter local_files_only=True ) intent_model.load_state_dict( torch.load(intent_model_path, map_location=device, weights_only=True) ) st.session_state.models["intent_model"] = intent_model.to(device).eval() st.session_state.models["intent_tokenizer"] = AutoTokenizer.from_pretrained( "pretrained_models/bert-base-uncased", local_files_only=True ) # Load and store emotion model emotions_model = AutoModelForSequenceClassification.from_pretrained( emotions_model_path, ignore_mismatched_sizes=True, # Add this parameter local_files_only=True ) st.session_state.models["emotions_model"] = emotions_model.to(device).eval() st.session_state.models["emotions_tokenizer"] = AutoTokenizer.from_pretrained( emotions_model_path, local_files_only=True ) # Set ready state st.session_state.is_ready = True st.rerun() except Exception as e: st.error(f"Error loading models: {str(e)}") st.stop() # Only show main app if models are ready if st.session_state.is_ready: # Title with custom styling st.markdown('
🎯 MoodifyTask: AI Task Prioritization & Wellness Assistant
', unsafe_allow_html=True) # Emotion Labels 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" ] # Emotion Categories positive_emotions = ["admiration", "amusement", "approval", "caring", "curiosity", "excitement", "gratitude", "joy", "love", "optimism", "pride", "relief", "surprise"] negative_emotions = ["anger", "annoyance", "disappointment", "disapproval", "disgust", "embarrassment", "fear", "grief", "nervousness", "remorse", "sadness"] neutral_emotions = ["realization", "neutral"] # Predict Intent def predict_intent(sentence): inputs = st.session_state.models["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 = st.session_state.models["intent_model"](**inputs) predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0] # Mapping Intent IDs to Priorities (0-150) PRIORITY_MAPPING = { 5: [8, 35, 42, 74, 97, 110, 118, 120, 124, 136], # freeze_account, report_lost_card, flight_status, report_fraud, credit_limit, lost_luggage, dispute_charge, overdraft, cancel_reservation, emergency 4: [14, 15, 19, 20, 39, 47, 48, 49, 50, 69, 70, 71, 72], # bill_balance, bill_due, exchange_rate, credit_score, interest_rate, insurance, medical_expenses, appointment_schedule, meeting_schedule, dentist_appointment, doctor_appointment, prescription_refill, pharmacy_hours 3: [33, 34, 41, 51, 56, 57, 62, 66, 77, 78, 85], # hotel_reservation, car_rental, restaurant_reservation, tracking_package, check_in, check_out, traffic_update, directions, smart_home_on, smart_home_off, weather_forecast 2: [0, 1, 3, 6, 9, 13, 16, 17, 21, 25, 27, 28, 36, 40, 45, 52, 61], # restaurant_reviews, shopping_list, what_song, schedule_meeting, translate, play_music, book_hotel, book_flight, gas_prices, exchange_rate, movie_showtimes, recipe, cancel_flight, book_reservation, order_food, car_services, joke 1: [2, 4, 5, 7, 10, 11, 12, 18, 22, 23, 24, 26, 30, 31, 32, 37, 38, 43, 44, 46, 53, 54, 55, 58, 59, 60, 63, 64, 65, 67, 68, 73] # tell_joke, fun_fact, trivia, horoscope, dog_fact, cat_fact, define_word, stock_price, sports_update, lottery_results, currency_conversion, holiday_list, language_learning, random_fact, poem, quote, daily_horoscope, joke_request, music_recommendation, podcast_recommendation, celebrity_gossip, movie_recommendation, TV_show_recommendation, book_recommendation, game_recommendation, radio_recommendation, trivia_game, riddle, name_meaning, birthday_reminder, anniversary_reminder, affirmations } # Find the priority based on predicted_class predicted_intent_score = next((priority for priority, ids in PRIORITY_MAPPING.items() if predicted_class in ids), 1) # Default to 1 if not found return predicted_intent_score # Emotion to Numeric Score Mapping EMOTION_MAPPING = { "admiration": 4, "amusement": 3, "anger": 5, "annoyance": 4, "approval": 3, "caring": 4, "confusion": 3, "curiosity": 3, "desire": 4, "disappointment": 4, "disapproval": 4, "disgust": 5, "embarrassment": 4, "excitement": 5, "fear": 5, "gratitude": 3, "grief": 5, "joy": 5, "love": 5, "nervousness": 4, "optimism": 4, "pride": 4, "realization": 3, "relief": 3, "remorse": 4, "sadness": 5, "surprise": 3, "neutral": 3 } # Function to get numeric emotion score def get_emotion_score(emotion): return EMOTION_MAPPING.get(emotion.lower(), 3) # Default to 3 if not found # Predict Emotion def predict_emotion(sentence): if not sentence.strip(): return 3, "neutral" # Ensure the input is a full sentence if len(sentence.split()) == 1: sentence = f"I feel {sentence}" inputs = st.session_state.models["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 = st.session_state.models["emotions_model"](**inputs) predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0] detected_emotion = emotion_label_names[predicted_class] # Manually adjust for stress/pressure-related words stress_keywords = ["stress", "stressed", "overwhelmed", "pressure", "tense", "burnout"] if any(word in sentence.lower() for word in stress_keywords): if detected_emotion not in ["sadness", "nervousness"]: detected_emotion = "nervousness" # Change to "sadness" if you prefer emotion_score = get_emotion_score(detected_emotion) if emotion_score is None: emotion_score = 3 # Default neutral score return emotion_score, detected_emotion # Get Emotion Category def get_emotion_category(emotion): if emotion in positive_emotions: return "positive" elif emotion in negative_emotions: return "negative" else: return "neutral" def normalize_priority(priority, min_value=0, max_value=10): return (priority - min_value) / (max_value - min_value) # Normalize between 0-1 # Calculate Task Priority def calculate_priority_score(predicted_intent_score,emotion_score, emotion, time_remaining, complexity, emotion_category): """ Calculate an adaptive priority score for tasks based on intent, emotion, time urgency, and complexity. """ emotion_score = emotion_score if emotion_score is not None else 3 # Normalize time urgency (scale 0 to 1 based on 7 days) time_score = max(0, min(1, 1 - (time_remaining.total_seconds() / (7 * 24 * 3600)))) # Set emotion-based adjustments stress_emotions = ["nervousness", "sadness", "fear"] frustration_emotions = ["anger", "frustration","disappointment","annoyance"] anxiety_emotions = ["anxiety", "uncertainty"] if emotion_category == "negative": if emotion in stress_emotions: # Prioritize **easy, quick** tasks to reduce cognitive load priority = (predicted_intent_score * 0.15) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.45) elif emotion in frustration_emotions: # Prioritize **engaging** tasks (not too easy) but keep urgency in mind priority = (predicted_intent_score * 0.2) + (emotion_score * 0.15) + (time_score * 0.25) + (complexity * 0.4) elif emotion in anxiety_emotions: # Prioritize **urgent, low-complexity** tasks priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.4) + ((10 - complexity) * 0.3) else: # Default for negative emotions: balance urgency and ease priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.4) elif emotion_category == "positive": # If the user is in a **good mood**, favor challenging, high-impact tasks priority = (predicted_intent_score * 0.35) + (emotion_score * 0.2) + (time_score * 0.25) + (complexity * 0.2) else: # Neutral emotion # Keep a balance between difficulty and urgency priority = (predicted_intent_score * 0.3) + (emotion_score * 0.2) + (time_score * 0.2) + (complexity * 0.3) return normalize_priority(priority) # Ensure no negative priority values # AI-Generated Plan Based on Start Time from datetime import datetime def get_llama_suggestion(emotion, tasks, selected_datetime): """Generate AI plan based on full datetime instead of just time""" # Sort tasks by priority (higher priority first) sorted_tasks = sorted(tasks, key=lambda x: x["priority_score"], reverse=True) # Filter tasks based on selected datetime filtered_tasks = [ task for task in sorted_tasks if task["due_date_time"] >= selected_datetime ] if not filtered_tasks: well_being_prompts = { "nervousness": "Suggest mindfulness exercises and short relaxation techniques.", "sadness": "Suggest comforting activities like journaling or light exercise.", "anger": "Suggest ways to channel frustration productively.", "joy": "Suggest ways to maintain productivity while feeling good.", "neutral": "Suggest general relaxation activities like listening to music." } well_being_prompt = f""" The user is feeling {emotion}. They have no tasks scheduled after {selected_datetime.strftime('%B %d, %I:%M %p')}. {well_being_prompts.get(emotion, 'Provide general well-being tips.')} """ try: response = client.chat.completions.create( messages=[{"role": "user", "content": well_being_prompt}], model="meta-llama/Llama-3.3-70B-Instruct-Turbo", temperature=0.7, ) return response.choices[0].message.content except Exception as e: return f"Error generating well-being tips: {e}" # Prepare the prompt with more detailed datetime information task_details = "\n".join([ f"- {task['description']} (Priority: {task['priority_score']:.2f}, Complexity: {task['complexity']}, Due: {task['due_date_time'].strftime('%B %d, %I:%M %p')})" for task in filtered_tasks ]) prompt = f""" The user is feeling {emotion}. They need a structured productivity plan starting from {selected_datetime.strftime('%B %d, %I:%M %p')}, not the current time. Their prioritized tasks (due on or after the selected time), sorted by priority score: {task_details} Please provide: 1. A detailed schedule with specific times for each task 2. Strategic breaks based on task complexity and emotional state 3. Wellness activities that complement their current emotion 4. Tips for managing tasks effectively given their emotional state 5. Suggestions for handling high-priority tasks first while maintaining well-being """ try: response = client.chat.completions.create( messages=[{"role": "user", "content": prompt}], model="meta-llama/Llama-3.3-70B-Instruct-Turbo", temperature=0.7, ) return response.choices[0].message.content except Exception as e: return f"Error generating AI plan: {e}" # Layout with improved spacing col1, col2 = st.columns([1, 1], gap="medium") with col1: # st.markdown('
', unsafe_allow_html=True) st.markdown('

🌟 Mood Analysis

', unsafe_allow_html=True) emotion_sentence = st.text_area( "Describe how you're feeling today:", value="", height=150, help="Your emotional state helps us prioritize tasks more effectively" ) if emotion_sentence: emotion_score, emotion_label = predict_emotion(emotion_sentence) st.session_state.overall_emotion = emotion_score st.session_state.overall_emotion_label = emotion_label st.markdown(f'
Detected Emotion: {emotion_label}
', unsafe_allow_html=True) # Emotion-based task reprioritization for task in st.session_state.tasks: task["priority_score"] = calculate_priority_score( task["predicted_intent_score"], emotion_score, emotion_label, task["time_remaining"], task["complexity"], get_emotion_category(emotion_label) ) st.markdown('
', unsafe_allow_html=True) with col2: # st.markdown('
', unsafe_allow_html=True) st.markdown('

📅 Add New Task

', unsafe_allow_html=True) with st.form("task_form", clear_on_submit=True): task_description = st.text_input("Task Description", help="Be specific about what needs to be done") col_date, col_time = st.columns(2) with col_date: due_date = st.date_input("Due Date") with col_time: due_time = st.time_input("Due Time") complexity = st.slider( "Task Complexity (1-10)", 1, 10, 5, help="Higher complexity may affect task priority" ) submitted = st.form_submit_button("➕ Add Task") if submitted and task_description and due_date and due_time: due_date_time = datetime.combine(due_date, due_time) time_remaining = due_date_time - datetime.now() predicted_intent_score = predict_intent(task_description) task = { "id": st.session_state.task_counter, # Add unique ID "description": task_description, "due_date_time": due_date_time, "time_remaining": time_remaining, "complexity": complexity, "predicted_intent_score": predicted_intent_score, "predicted_emotion": st.session_state.overall_emotion, "predicted_label_name": st.session_state.overall_emotion_label, "priority_score": calculate_priority_score( predicted_intent_score, st.session_state.overall_emotion, st.session_state.overall_emotion_label, time_remaining, complexity, get_emotion_category(st.session_state.overall_emotion_label) ), "completed": False } st.session_state.tasks.append(task) st.session_state.task_counter += 1 # Increment counter st.success("✅ Task Added Successfully!") st.markdown('
', unsafe_allow_html=True) # Task List with Improved Visualization if st.session_state.tasks: st.markdown('

📌 Task Priority List

', unsafe_allow_html=True) # Sort tasks by priority sorted_tasks = sorted(st.session_state.tasks, key=lambda x: x["priority_score"], reverse=True) # Create task overview cards st.markdown('
', unsafe_allow_html=True) col1, col2 = st.columns(2) with col1: st.markdown(f'
{len(sorted_tasks)}
Total Tasks
', unsafe_allow_html=True) # with col2: # high_priority = len([t for t in sorted_tasks if t["priority_score"] > 0.7]) # st.markdown(f'
{high_priority}
High Priority
', unsafe_allow_html=True) with col2: today = datetime.now() due_today = len([t for t in sorted_tasks if t["due_date_time"].date() == today.date()]) st.markdown(f'
{due_today}
Due Today
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) # Display tasks with priority-based styling for idx, task in enumerate(sorted_tasks): priority_class = "high-priority" if task["priority_score"] > 0.7 else "medium-priority" # Create a single row for task and buttons task_container = st.container() with task_container: cols = st.columns([0.8, 0.1, 0.1]) # Task content in first column with cols[0]: st.markdown(f"""
{task["description"]} Priority: {task["priority_score"]:.2f}
Due: {task["due_date_time"].strftime("%d %b, %I:%M %p")} Complexity: {task["complexity"]}
""", unsafe_allow_html=True) st.session_state.editing_task_id = None # Edit button with cols[1]: if st.button("✏️", key=f"edit_{idx}", help="Edit task"): st.session_state.editing_task_id = idx # Delete button with cols[2]: if st.button("🗑️", key=f"delete_{idx}", help="Delete task"): st.session_state.tasks.pop(idx) st.success("Task deleted!") st.rerun() # Show edit form below the task if being edited if st.session_state.editing_task_id == idx: with st.form(key=f"edit_form_{idx}"): col1, col2 = st.columns(2) with col1: new_description = st.text_input("Description", value=task["description"]) new_complexity = st.slider("Complexity", 1, 10, value=task["complexity"]) with col2: new_due_date = st.date_input("Due Date", value=task["due_date_time"].date()) new_due_time = st.time_input("Due Time", value=task["due_date_time"].time()) col1, col2 = st.columns(2) with col1: if st.form_submit_button("💾 Save"): # Update task task["description"] = new_description task["due_date_time"] = datetime.combine(new_due_date, new_due_time) task["time_remaining"] = task["due_date_time"] - datetime.now() task["complexity"] = new_complexity # Recalculate priority task["priority_score"] = calculate_priority_score( task["predicted_intent_score"], task["predicted_emotion"], task["predicted_label_name"], task["time_remaining"], task["complexity"], get_emotion_category(task["predicted_label_name"]) ) st.session_state.editing_task_id = None st.success("Task updated!") st.rerun() with col2: if st.form_submit_button("❌ Cancel"): st.session_state.editing_task_id = None st.rerun() # AI Plan Section if st.session_state.tasks: st.markdown('
', unsafe_allow_html=True) st.markdown('

⏰ AI Task Planning

', unsafe_allow_html=True) col_date, col_time = st.columns(2) with col_date: plan_date = st.date_input("Select Plan Date", datetime.now().date()) with col_time: plan_time = st.time_input("Select Plan Start Time", datetime.now().time()) selected_datetime = datetime.combine(plan_date, plan_time) if st.button("📅 Generate AI Plan"): suggestion = get_llama_suggestion( st.session_state.overall_emotion_label, st.session_state.tasks, selected_datetime # Pass full datetime object ) st.markdown(f'
{suggestion}
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True)