Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,558 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+import os
+from dotenv import load_dotenv
+from together import Together
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, 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
+import gdown
+
+# Set environment variable for offline mode
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+
+# 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 = "intent_classifier.pth"
+# Extract file ID from Google Drive URL
+file_id = "1_GDGvV3MVvBguIsjMyDLg3RxUV_gnFAY"
+num_intent_labels = 151  # Moved this up before model creation
+
+# Load Emotion Model
+emotions_model_path = "./saved_model"
+emotions_folder_id = "1gYWkbC_XBw_GZjsfwXvubHFil4BCq_gH"
+
+# Add new pretrained model ID
+pretrained_folder_id = "13t_EB2LFhRIwb3dkKDtA0O5NXXZBoG-j"
+
+# 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(
+        """
+        <div class="loading-container" style="text-align: center; padding: 50px;">
+            <div class="loading-spinner"></div>
+            <h2>Setting up your AI assistant...</h2>
+            <p>This may take a minute. We're downloading the required models.</p>
+        </div>
+        """,
+        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('<div class="main-header">🎯 MoodifyTask: AI Task Prioritization & Wellness Assistant</div>', 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('<div class="emotion-analysis">', unsafe_allow_html=True)
+        st.markdown('<h3>🌟 Mood Analysis</h3>', 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'<div class="emotion-badge">Detected Emotion: {emotion_label}</div>', 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('</div>', unsafe_allow_html=True)
+
+    with col2:
+        # st.markdown('<div class="task-input">', unsafe_allow_html=True)
+        st.markdown('<h3>📅 Add New Task</h3>', 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('</div>', unsafe_allow_html=True)
+
+    # Task List with Improved Visualization
+    if st.session_state.tasks:
+        st.markdown('<h3>📌 Task Priority List</h3>', 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('<div class="task-overview">', unsafe_allow_html=True)
+        col1, col2 = st.columns(2)
+        with col1:
+            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)
+        # with col2:
+        #     high_priority = len([t for t in sorted_tasks if t["priority_score"] > 0.7])
+        #     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)
+        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'<div class="metric-card"><div class="metric-value">{due_today}</div><div class="metric-label">Due Today</div></div>', unsafe_allow_html=True)
+        st.markdown('</div>', 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"""
+                        <div class="priority-task {priority_class}">
+                            <div class="task-content">
+                                <div class="task-header">
+                                    <span class="task-title">{task["description"]}</span>
+                                    <span class="priority-score">Priority: {task["priority_score"]:.2f}</span>
+                                </div>
+                                <div class="task-details">
+                                    <span class="task-stat">Due: {task["due_date_time"].strftime("%d %b, %I:%M %p")}</span>
+                                    <span class="task-stat">Complexity: {task["complexity"]}</span>
+                                </div>
+                            </div>
+                        </div>
+                    """, 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('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h3>⏰ AI Task Planning</h3>', 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'<div class="info-box">{suggestion}</div>', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))