Spaces:
Sleeping
Sleeping
Update src/streamlit_app.py
Browse files- src/streamlit_app.py +484 -198
src/streamlit_app.py
CHANGED
@@ -247,7 +247,7 @@ def get_llama_suggestion(emotion, tasks, selected_datetime):
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return f"Error generating AI plan: {e}"
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# Page Configuration
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st.set_page_config(
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page_title="π AI Productivity Assistant",
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layout="wide",
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# Custom CSS for enhanced styling
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st.markdown(get_custom_css(), unsafe_allow_html=True)
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#
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st.
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st.session_state.overall_emotion_label = "Neutral"
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# Layout with improved spacing
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col1, col2 = st.columns([1, 1], gap="medium")
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with col1:
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# st.markdown('<div class="emotion-analysis">', unsafe_allow_html=True)
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st.markdown('<h3>π Mood Analysis</h3>', unsafe_allow_html=True)
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emotion_sentence = st.text_area(
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"Describe how you're feeling today:",
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value="",
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height=150,
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help="Your emotional state helps us prioritize tasks more effectively"
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)
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emotion_score,
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emotion_label,
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task["time_remaining"],
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task["complexity"],
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get_emotion_category(emotion_label)
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)
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st.markdown('</div>', unsafe_allow_html=True)
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with st.form("task_form", clear_on_submit=True):
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task_description = st.text_input("Task Description", help="Be specific about what needs to be done")
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col_date, col_time = st.columns(2)
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}
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st.session_state.tasks.append(task)
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st.session_state.task_counter += 1 # Increment counter
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st.success("β
Task Added Successfully!")
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st.markdown('</div>', unsafe_allow_html=True)
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#
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# Sort tasks by priority
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sorted_tasks = sorted(st.session_state.tasks, key=lambda x: x["priority_score"], reverse=True)
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# Create task overview cards
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st.markdown('<div class="task-overview">', unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(
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with col2:
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st.
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# Create
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<div class="task-
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<
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</div>
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</div>
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selected_datetime = datetime.combine(plan_date, plan_time)
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return f"Error generating AI plan: {e}"
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# Page Configuration first
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st.set_page_config(
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page_title="π AI Productivity Assistant",
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layout="wide",
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# Custom CSS for enhanced styling
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st.markdown(get_custom_css(), unsafe_allow_html=True)
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# Show loading screen if models aren't ready
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if not st.session_state.is_ready:
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st.markdown(
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"""
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<div class="loading-container" style="text-align: center; padding: 50px;">
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<div class="loading-spinner"></div>
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<h2>Setting up your AI assistant...</h2>
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<p>This may take a minute. We're downloading the required models.</p>
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</div>
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""",
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unsafe_allow_html=True
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)
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# Load models here
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try:
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# First download pretrained models
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if not os.path.exists("pretrained_models"):
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with st.status("Downloading base models...", expanded=True) as status:
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os.makedirs("pretrained_models", exist_ok=True)
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gdown.download_folder(
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f"https://drive.google.com/drive/folders/{pretrained_folder_id}",
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output="pretrained_models",
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quiet=False
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)
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status.update(label="Base models downloaded!", state="complete")
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# Intent Model Loading
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if not os.path.exists(intent_model_path):
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with st.status("Downloading intent model...", expanded=True) as status:
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output = gdown.download(
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f"https://drive.google.com/uc?id={file_id}",
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intent_model_path,
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quiet=False
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)
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status.update(label="Intent model downloaded!", state="complete")
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# Emotion Model Loading
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if not os.path.exists(emotions_model_path):
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with st.status("Downloading emotion model...", expanded=True) as status:
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os.makedirs(emotions_model_path, exist_ok=True)
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gdown.download_folder(
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f"https://drive.google.com/drive/folders/{emotions_folder_id}",
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output=emotions_model_path,
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quiet=False
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)
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status.update(label="Emotion model downloaded!", state="complete")
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# Load and store intent model
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intent_model = AutoModelForSequenceClassification.from_pretrained(
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"pretrained_models/bert-base-uncased",
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num_labels=num_intent_labels,
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ignore_mismatched_sizes=True, # Add this parameter
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local_files_only=True
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)
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intent_model.load_state_dict(
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torch.load(intent_model_path, map_location=device, weights_only=True)
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)
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st.session_state.models["intent_model"] = intent_model.to(device).eval()
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st.session_state.models["intent_tokenizer"] = AutoTokenizer.from_pretrained(
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"pretrained_models/bert-base-uncased",
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local_files_only=True
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)
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# Load and store emotion model
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emotions_model = AutoModelForSequenceClassification.from_pretrained(
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emotions_model_path,
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ignore_mismatched_sizes=True, # Add this parameter
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local_files_only=True
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)
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st.session_state.models["emotions_model"] = emotions_model.to(device).eval()
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st.session_state.models["emotions_tokenizer"] = AutoTokenizer.from_pretrained(
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emotions_model_path,
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local_files_only=True
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)
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# Set ready state
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st.session_state.is_ready = True
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st.rerun()
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except Exception as e:
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st.error(f"Error loading models: {str(e)}")
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st.stop()
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# Only show main app if models are ready
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if st.session_state.is_ready:
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# Title with custom styling
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st.markdown('<div class="main-header">π― MoodifyTask: AI Task Prioritization & Wellness Assistant</div>', unsafe_allow_html=True)
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# Emotion Labels
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emotion_label_names = [
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"admiration", "amusement", "anger", "annoyance", "approval",
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"caring", "confusion", "curiosity", "desire", "disappointment",
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"disapproval", "disgust", "embarrassment", "excitement", "fear",
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"gratitude", "grief", "joy", "love", "nervousness",
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"optimism", "pride", "realization", "relief", "remorse",
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"sadness", "surprise", "neutral"
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]
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# Emotion Categories
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positive_emotions = ["admiration", "amusement", "approval", "caring", "curiosity", "excitement", "gratitude", "joy", "love", "optimism", "pride", "relief", "surprise"]
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negative_emotions = ["anger", "annoyance", "disappointment", "disapproval", "disgust", "embarrassment", "fear", "grief", "nervousness", "remorse", "sadness"]
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neutral_emotions = ["realization", "neutral"]
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# Predict Intent
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def predict_intent(sentence):
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inputs = st.session_state.models["intent_tokenizer"](
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sentence, return_tensors="pt", padding="max_length", truncation=True, max_length=128
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)
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inputs = {key: val.to(device) for key, val in inputs.items()}
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with torch.no_grad():
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outputs = st.session_state.models["intent_model"](**inputs)
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predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0]
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# Mapping Intent IDs to Priorities (0-150)
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PRIORITY_MAPPING = {
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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
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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
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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
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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
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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]
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380 |
+
# 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
|
381 |
+
}
|
382 |
+
|
383 |
+
# Find the priority based on predicted_class
|
384 |
+
predicted_intent_score = next((priority for priority, ids in PRIORITY_MAPPING.items() if predicted_class in ids), 1) # Default to 1 if not found
|
385 |
+
|
386 |
+
return predicted_intent_score
|
387 |
+
|
388 |
+
# Emotion to Numeric Score Mapping
|
389 |
+
EMOTION_MAPPING = {
|
390 |
+
"admiration": 4, "amusement": 3, "anger": 5, "annoyance": 4, "approval": 3,
|
391 |
+
"caring": 4, "confusion": 3, "curiosity": 3, "desire": 4, "disappointment": 4,
|
392 |
+
"disapproval": 4, "disgust": 5, "embarrassment": 4, "excitement": 5, "fear": 5,
|
393 |
+
"gratitude": 3, "grief": 5, "joy": 5, "love": 5, "nervousness": 4,
|
394 |
+
"optimism": 4, "pride": 4, "realization": 3, "relief": 3, "remorse": 4,
|
395 |
+
"sadness": 5, "surprise": 3, "neutral": 3
|
396 |
+
}
|
397 |
+
|
398 |
+
# Function to get numeric emotion score
|
399 |
+
def get_emotion_score(emotion):
|
400 |
+
return EMOTION_MAPPING.get(emotion.lower(), 3) # Default to 3 if not found
|
401 |
+
# Predict Emotion
|
402 |
+
def predict_emotion(sentence):
|
403 |
+
if not sentence.strip():
|
404 |
+
return 3, "neutral"
|
405 |
+
# Ensure the input is a full sentence
|
406 |
+
if len(sentence.split()) == 1:
|
407 |
+
sentence = f"I feel {sentence}"
|
408 |
+
inputs = st.session_state.models["emotions_tokenizer"](
|
409 |
+
sentence, return_tensors="pt", padding="max_length", truncation=True, max_length=128
|
410 |
)
|
411 |
+
inputs = {key: val.to(device) for key, val in inputs.items() if key != "token_type_ids"}
|
412 |
+
|
413 |
+
with torch.no_grad():
|
414 |
+
outputs = st.session_state.models["emotions_model"](**inputs)
|
415 |
+
predicted_class = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0]
|
416 |
+
|
417 |
+
detected_emotion = emotion_label_names[predicted_class]
|
418 |
+
|
419 |
+
# Manually adjust for stress/pressure-related words
|
420 |
+
stress_keywords = ["stress", "stressed", "overwhelmed", "pressure", "tense", "burnout"]
|
421 |
+
if any(word in sentence.lower() for word in stress_keywords):
|
422 |
+
if detected_emotion not in ["sadness", "nervousness"]:
|
423 |
+
detected_emotion = "nervousness" # Change to "sadness" if you prefer
|
424 |
+
|
425 |
+
emotion_score = get_emotion_score(detected_emotion)
|
426 |
+
if emotion_score is None:
|
427 |
+
emotion_score = 3 # Default neutral score
|
428 |
+
|
429 |
+
return emotion_score, detected_emotion
|
430 |
+
|
431 |
+
|
432 |
+
# Get Emotion Category
|
433 |
+
def get_emotion_category(emotion):
|
434 |
+
if emotion in positive_emotions:
|
435 |
+
return "positive"
|
436 |
+
elif emotion in negative_emotions:
|
437 |
+
return "negative"
|
438 |
+
else:
|
439 |
+
return "neutral"
|
440 |
|
441 |
+
|
442 |
+
def normalize_priority(priority, min_value=0, max_value=10):
|
443 |
+
return (priority - min_value) / (max_value - min_value) # Normalize between 0-1
|
444 |
+
|
445 |
+
# Calculate Task Priority
|
446 |
+
def calculate_priority_score(predicted_intent_score,emotion_score, emotion, time_remaining, complexity, emotion_category):
|
447 |
+
"""
|
448 |
+
Calculate an adaptive priority score for tasks based on intent, emotion, time urgency, and complexity.
|
449 |
+
"""
|
450 |
+
emotion_score = emotion_score if emotion_score is not None else 3
|
451 |
+
# Normalize time urgency (scale 0 to 1 based on 7 days)
|
452 |
+
time_score = max(0, min(1, 1 - (time_remaining.total_seconds() / (7 * 24 * 3600))))
|
453 |
+
|
454 |
+
# Set emotion-based adjustments
|
455 |
+
stress_emotions = ["nervousness", "sadness", "fear"]
|
456 |
+
frustration_emotions = ["anger", "frustration","disappointment","annoyance"]
|
457 |
+
anxiety_emotions = ["anxiety", "uncertainty"]
|
458 |
|
459 |
+
|
460 |
+
if emotion_category == "negative":
|
461 |
+
if emotion in stress_emotions:
|
462 |
+
# Prioritize **easy, quick** tasks to reduce cognitive load
|
463 |
+
priority = (predicted_intent_score * 0.15) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.45)
|
464 |
|
465 |
+
elif emotion in frustration_emotions:
|
466 |
+
# Prioritize **engaging** tasks (not too easy) but keep urgency in mind
|
467 |
+
priority = (predicted_intent_score * 0.2) + (emotion_score * 0.15) + (time_score * 0.25) + (complexity * 0.4)
|
468 |
+
|
469 |
+
elif emotion in anxiety_emotions:
|
470 |
+
# Prioritize **urgent, low-complexity** tasks
|
471 |
+
priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.4) + ((10 - complexity) * 0.3)
|
472 |
+
|
473 |
+
else:
|
474 |
+
# Default for negative emotions: balance urgency and ease
|
475 |
+
priority = (predicted_intent_score * 0.2) + (emotion_score * 0.1) + (time_score * 0.3) + ((10 - complexity) * 0.4)
|
476 |
+
|
477 |
+
elif emotion_category == "positive":
|
478 |
+
# If the user is in a **good mood**, favor challenging, high-impact tasks
|
479 |
+
priority = (predicted_intent_score * 0.35) + (emotion_score * 0.2) + (time_score * 0.25) + (complexity * 0.2)
|
480 |
+
|
481 |
+
else: # Neutral emotion
|
482 |
+
# Keep a balance between difficulty and urgency
|
483 |
+
priority = (predicted_intent_score * 0.3) + (emotion_score * 0.2) + (time_score * 0.2) + (complexity * 0.3)
|
484 |
+
|
485 |
+
return normalize_priority(priority) # Ensure no negative priority values
|
486 |
+
|
487 |
+
|
488 |
+
|
489 |
+
|
490 |
+
# AI-Generated Plan Based on Start Time
|
491 |
+
from datetime import datetime
|
492 |
+
|
493 |
+
def get_llama_suggestion(emotion, tasks, selected_datetime):
|
494 |
+
"""Generate AI plan based on full datetime instead of just time"""
|
495 |
+
# Sort tasks by priority (higher priority first)
|
496 |
+
sorted_tasks = sorted(tasks, key=lambda x: x["priority_score"], reverse=True)
|
497 |
+
|
498 |
+
# Filter tasks based on selected datetime
|
499 |
+
filtered_tasks = [
|
500 |
+
task for task in sorted_tasks
|
501 |
+
if task["due_date_time"] >= selected_datetime
|
502 |
+
]
|
503 |
+
|
504 |
+
if not filtered_tasks:
|
505 |
+
well_being_prompts = {
|
506 |
+
"nervousness": "Suggest mindfulness exercises and short relaxation techniques.",
|
507 |
+
"sadness": "Suggest comforting activities like journaling or light exercise.",
|
508 |
+
"anger": "Suggest ways to channel frustration productively.",
|
509 |
+
"joy": "Suggest ways to maintain productivity while feeling good.",
|
510 |
+
"neutral": "Suggest general relaxation activities like listening to music."
|
511 |
}
|
512 |
+
well_being_prompt = f"""
|
513 |
+
The user is feeling {emotion}.
|
514 |
+
They have no tasks scheduled after {selected_datetime.strftime('%B %d, %I:%M %p')}.
|
515 |
+
{well_being_prompts.get(emotion, 'Provide general well-being tips.')}
|
516 |
+
"""
|
517 |
+
try:
|
518 |
+
response = client.chat.completions.create(
|
519 |
+
messages=[{"role": "user", "content": well_being_prompt}],
|
520 |
+
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
|
521 |
+
temperature=0.7,
|
522 |
+
)
|
523 |
+
return response.choices[0].message.content
|
524 |
+
except Exception as e:
|
525 |
+
return f"Error generating well-being tips: {e}"
|
526 |
+
|
527 |
+
# Prepare the prompt with more detailed datetime information
|
528 |
+
task_details = "\n".join([
|
529 |
+
f"- {task['description']} (Priority: {task['priority_score']:.2f}, Complexity: {task['complexity']}, Due: {task['due_date_time'].strftime('%B %d, %I:%M %p')})"
|
530 |
+
for task in filtered_tasks
|
531 |
+
])
|
532 |
+
|
533 |
+
prompt = f"""
|
534 |
+
The user is feeling {emotion}.
|
535 |
+
They need a structured productivity plan starting from {selected_datetime.strftime('%B %d, %I:%M %p')}, not the current time.
|
536 |
+
|
537 |
+
Their prioritized tasks (due on or after the selected time), sorted by priority score:
|
538 |
+
{task_details}
|
539 |
+
|
540 |
+
Please provide:
|
541 |
+
1. A detailed schedule with specific times for each task
|
542 |
+
2. Strategic breaks based on task complexity and emotional state
|
543 |
+
3. Wellness activities that complement their current emotion
|
544 |
+
4. Tips for managing tasks effectively given their emotional state
|
545 |
+
5. Suggestions for handling high-priority tasks first while maintaining well-being
|
546 |
+
"""
|
547 |
+
|
548 |
+
try:
|
549 |
+
response = client.chat.completions.create(
|
550 |
+
messages=[{"role": "user", "content": prompt}],
|
551 |
+
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
|
552 |
+
temperature=0.7,
|
553 |
+
)
|
554 |
+
return response.choices[0].message.content
|
555 |
+
except Exception as e:
|
556 |
+
return f"Error generating AI plan: {e}"
|
557 |
|
|
|
|
|
|
|
|
|
558 |
|
559 |
+
# Layout with improved spacing
|
560 |
+
col1, col2 = st.columns([1, 1], gap="medium")
|
561 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
562 |
with col1:
|
563 |
+
# st.markdown('<div class="emotion-analysis">', unsafe_allow_html=True)
|
564 |
+
st.markdown('<h3>π Mood Analysis</h3>', unsafe_allow_html=True)
|
565 |
+
emotion_sentence = st.text_area(
|
566 |
+
"Describe how you're feeling today:",
|
567 |
+
value="",
|
568 |
+
height=150,
|
569 |
+
help="Your emotional state helps us prioritize tasks more effectively"
|
570 |
+
)
|
571 |
+
|
572 |
+
if emotion_sentence:
|
573 |
+
emotion_score, emotion_label = predict_emotion(emotion_sentence)
|
574 |
+
st.session_state.overall_emotion = emotion_score
|
575 |
+
st.session_state.overall_emotion_label = emotion_label
|
576 |
+
|
577 |
+
st.markdown(f'<div class="emotion-badge">Detected Emotion: {emotion_label}</div>', unsafe_allow_html=True)
|
578 |
+
|
579 |
+
# Emotion-based task reprioritization
|
580 |
+
for task in st.session_state.tasks:
|
581 |
+
task["priority_score"] = calculate_priority_score(
|
582 |
+
task["predicted_intent_score"],
|
583 |
+
emotion_score,
|
584 |
+
emotion_label,
|
585 |
+
task["time_remaining"],
|
586 |
+
task["complexity"],
|
587 |
+
get_emotion_category(emotion_label)
|
588 |
+
)
|
589 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
590 |
+
|
591 |
with col2:
|
592 |
+
# st.markdown('<div class="task-input">', unsafe_allow_html=True)
|
593 |
+
st.markdown('<h3>π
Add New Task</h3>', unsafe_allow_html=True)
|
594 |
+
with st.form("task_form", clear_on_submit=True):
|
595 |
+
task_description = st.text_input("Task Description", help="Be specific about what needs to be done")
|
596 |
+
col_date, col_time = st.columns(2)
|
597 |
+
|
598 |
+
with col_date:
|
599 |
+
due_date = st.date_input("Due Date")
|
600 |
+
|
601 |
+
with col_time:
|
602 |
+
due_time = st.time_input("Due Time")
|
603 |
+
|
604 |
+
complexity = st.slider(
|
605 |
+
"Task Complexity (1-10)",
|
606 |
+
1, 10, 5,
|
607 |
+
help="Higher complexity may affect task priority"
|
608 |
+
)
|
609 |
+
|
610 |
+
submitted = st.form_submit_button("β Add Task")
|
611 |
+
|
612 |
+
if submitted and task_description and due_date and due_time:
|
613 |
+
due_date_time = datetime.combine(due_date, due_time)
|
614 |
+
time_remaining = due_date_time - datetime.now()
|
615 |
+
predicted_intent_score = predict_intent(task_description)
|
616 |
+
|
617 |
+
task = {
|
618 |
+
"id": st.session_state.task_counter, # Add unique ID
|
619 |
+
"description": task_description,
|
620 |
+
"due_date_time": due_date_time,
|
621 |
+
"time_remaining": time_remaining,
|
622 |
+
"complexity": complexity,
|
623 |
+
"predicted_intent_score": predicted_intent_score,
|
624 |
+
"predicted_emotion": st.session_state.overall_emotion,
|
625 |
+
"predicted_label_name": st.session_state.overall_emotion_label,
|
626 |
+
"priority_score": calculate_priority_score(
|
627 |
+
predicted_intent_score,
|
628 |
+
st.session_state.overall_emotion,
|
629 |
+
st.session_state.overall_emotion_label,
|
630 |
+
time_remaining,
|
631 |
+
complexity,
|
632 |
+
get_emotion_category(st.session_state.overall_emotion_label)
|
633 |
+
),
|
634 |
+
"completed": False
|
635 |
+
}
|
636 |
+
|
637 |
+
st.session_state.tasks.append(task)
|
638 |
+
st.session_state.task_counter += 1 # Increment counter
|
639 |
+
st.success("β
Task Added Successfully!")
|
640 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
641 |
+
|
642 |
+
# Task List with Improved Visualization
|
643 |
+
if st.session_state.tasks:
|
644 |
+
st.markdown('<h3>π Task Priority List</h3>', unsafe_allow_html=True)
|
645 |
+
|
646 |
+
# Sort tasks by priority
|
647 |
+
sorted_tasks = sorted(st.session_state.tasks, key=lambda x: x["priority_score"], reverse=True)
|
648 |
|
649 |
+
# Create task overview cards
|
650 |
+
st.markdown('<div class="task-overview">', unsafe_allow_html=True)
|
651 |
+
col1, col2 = st.columns(2)
|
652 |
+
with col1:
|
653 |
+
st.markdown(f'<div class="metric-card"><div class="metric-value">{len(sorted_tasks)}</div><div class="metric-label">Total Tasks</div></div>', unsafe_allow_html=True)
|
654 |
+
# with col2:
|
655 |
+
# high_priority = len([t for t in sorted_tasks if t["priority_score"] > 0.7])
|
656 |
+
# st.markdown(f'<div class="metric-card"><div class="metric-value">{high_priority}</div><div class="metric-label">High Priority</div></div>', unsafe_allow_html=True)
|
657 |
+
with col2:
|
658 |
+
today = datetime.now()
|
659 |
+
due_today = len([t for t in sorted_tasks if t["due_date_time"].date() == today.date()])
|
660 |
+
st.markdown(f'<div class="metric-card"><div class="metric-value">{due_today}</div><div class="metric-label">Due Today</div></div>', unsafe_allow_html=True)
|
661 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
662 |
+
|
663 |
+
# Display tasks with priority-based styling
|
664 |
+
for idx, task in enumerate(sorted_tasks):
|
665 |
+
priority_class = "high-priority" if task["priority_score"] > 0.7 else "medium-priority"
|
666 |
|
667 |
+
# Create a single row for task and buttons
|
668 |
+
task_container = st.container()
|
669 |
+
with task_container:
|
670 |
+
cols = st.columns([0.8, 0.1, 0.1])
|
671 |
+
|
672 |
+
# Task content in first column
|
673 |
+
with cols[0]:
|
674 |
+
st.markdown(f"""
|
675 |
+
<div class="priority-task {priority_class}">
|
676 |
+
<div class="task-content">
|
677 |
+
<div class="task-header">
|
678 |
+
<span class="task-title">{task["description"]}</span>
|
679 |
+
<span class="priority-score">Priority: {task["priority_score"]:.2f}</span>
|
680 |
+
</div>
|
681 |
+
<div class="task-details">
|
682 |
+
<span class="task-stat">Due: {task["due_date_time"].strftime("%d %b, %I:%M %p")}</span>
|
683 |
+
<span class="task-stat">Complexity: {task["complexity"]}</span>
|
684 |
+
</div>
|
685 |
</div>
|
686 |
</div>
|
687 |
+
""", unsafe_allow_html=True)
|
688 |
+
st.session_state.editing_task_id = None
|
689 |
+
# Edit button
|
690 |
+
with cols[1]:
|
691 |
+
if st.button("βοΈ", key=f"edit_{idx}", help="Edit task"):
|
692 |
+
st.session_state.editing_task_id = idx
|
693 |
+
|
694 |
+
# Delete button
|
695 |
+
with cols[2]:
|
696 |
+
if st.button("ποΈ", key=f"delete_{idx}", help="Delete task"):
|
697 |
+
st.session_state.tasks.pop(idx)
|
698 |
+
st.success("Task deleted!")
|
699 |
+
st.rerun()
|
700 |
+
|
701 |
+
# Show edit form below the task if being edited
|
702 |
+
if st.session_state.editing_task_id == idx:
|
703 |
+
with st.form(key=f"edit_form_{idx}"):
|
704 |
+
col1, col2 = st.columns(2)
|
705 |
+
with col1:
|
706 |
+
new_description = st.text_input("Description", value=task["description"])
|
707 |
+
new_complexity = st.slider("Complexity", 1, 10, value=task["complexity"])
|
708 |
+
with col2:
|
709 |
+
new_due_date = st.date_input("Due Date", value=task["due_date_time"].date())
|
710 |
+
new_due_time = st.time_input("Due Time", value=task["due_date_time"].time())
|
711 |
+
|
712 |
+
col1, col2 = st.columns(2)
|
713 |
+
with col1:
|
714 |
+
if st.form_submit_button("πΎ Save"):
|
715 |
+
# Update task
|
716 |
+
task["description"] = new_description
|
717 |
+
task["due_date_time"] = datetime.combine(new_due_date, new_due_time)
|
718 |
+
task["time_remaining"] = task["due_date_time"] - datetime.now()
|
719 |
+
task["complexity"] = new_complexity
|
720 |
+
|
721 |
+
# Recalculate priority
|
722 |
+
task["priority_score"] = calculate_priority_score(
|
723 |
+
task["predicted_intent_score"],
|
724 |
+
task["predicted_emotion"],
|
725 |
+
task["predicted_label_name"],
|
726 |
+
task["time_remaining"],
|
727 |
+
task["complexity"],
|
728 |
+
get_emotion_category(task["predicted_label_name"])
|
729 |
+
)
|
730 |
+
st.session_state.editing_task_id = None
|
731 |
+
st.success("Task updated!")
|
732 |
+
st.rerun()
|
733 |
+
|
734 |
+
with col2:
|
735 |
+
if st.form_submit_button("β Cancel"):
|
736 |
+
st.session_state.editing_task_id = None
|
737 |
+
st.rerun()
|
738 |
+
|
739 |
+
# AI Plan Section
|
740 |
+
if st.session_state.tasks:
|
741 |
+
st.markdown('<div class="custom-card">', unsafe_allow_html=True)
|
742 |
+
st.markdown('<h3>β° AI Task Planning</h3>', unsafe_allow_html=True)
|
743 |
+
|
744 |
+
col_date, col_time = st.columns(2)
|
745 |
+
|
746 |
+
with col_date:
|
747 |
+
plan_date = st.date_input("Select Plan Date", datetime.now().date())
|
748 |
+
|
749 |
+
with col_time:
|
750 |
+
plan_time = st.time_input("Select Plan Start Time", datetime.now().time())
|
751 |
+
|
752 |
+
selected_datetime = datetime.combine(plan_date, plan_time)
|
|
|
753 |
|
754 |
+
if st.button("π
Generate AI Plan"):
|
755 |
+
suggestion = get_llama_suggestion(
|
756 |
+
st.session_state.overall_emotion_label,
|
757 |
+
st.session_state.tasks,
|
758 |
+
selected_datetime # Pass full datetime object
|
759 |
+
)
|
760 |
+
st.markdown(f'<div class="info-box">{suggestion}</div>', unsafe_allow_html=True)
|
761 |
+
st.markdown('</div>', unsafe_allow_html=True)
|