app.py

import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import pandas as pd
from enum import Enum
from datetime import datetime, timedelta


plt.style.use('seaborn-v0_8-whitegrid')


# Define content modality types
class ContentModality(Enum):
    TEXT = 1
    IMAGE = 2
    AUDIO = 3
    VIDEO = 4
    INTERACTIVE = 5
    MIXED = 6


# Define columns for FSRS algorithm (from app.py)
columns = ["difficulty", "stability", "retrievability", "delta_t",
           "reps", "lapses", "last_date", "due", "ivl", "cost", "rand"]
col = {key: i for i, key in enumerate(columns)}

first_rating_prob = np.array([0.15, 0.2, 0.6, 0.05])


def moving_average(data, window_size=7):
    """Calculate moving average with the specified window size"""
    weights = np.ones(window_size) / window_size
    return np.convolve(data, weights, mode='valid')


# Spaced Repetition Simulation (from app.py)
def simulate_fsrs(w, request_retention=0.9, deck_size=10000, learn_span=100,
                  max_cost_perday=200, max_ivl=36500, recall_cost=10,
                  forget_cost=30, learn_cost=10, progress=None):
    card_table = np.zeros((len(columns), deck_size))
    card_table[col["due"]] = learn_span
    card_table[col["difficulty"]] = 1e-10
    card_table[col["stability"]] = 1e-10

    review_cnt_per_day = np.zeros(learn_span)
    learn_cnt_per_day = np.zeros(learn_span)
    memorized_cnt_per_day = np.zeros(learn_span)

    def stability_after_success(s, r, d, response):
        hard_penalty = np.where(response == 1, w[15], 1)
        easy_bonus = np.where(response == 3, w[16], 1)
        return s * (1 + np.exp(w[8]) * (11 - d) * np.power(s, -w[9]) * (
                    np.exp((1 - r) * w[10]) - 1) * hard_penalty * easy_bonus)

    def stability_after_failure(s, r, d):
        return np.maximum(0.1, np.minimum(
            w[11] * np.power(d, -w[12]) * (np.power(s + 1, w[13]) - 1) * np.exp((1 - r) * w[14]), s))

    iterator = tqdm(range(learn_span)) if progress is None else range(learn_span)
    for today in iterator:
        if progress is not None:
            progress((today / learn_span) * 0.5)  # Use first half of progress for FSRS

        has_learned = card_table[col["stability"]] > 1e-10
        card_table[col["delta_t"]][has_learned] = today - \
                                                  card_table[col["last_date"]][has_learned]
        card_table[col["retrievability"]][has_learned] = np.power(
            1 + card_table[col["delta_t"]][has_learned] / (9 * card_table[col["stability"]][has_learned]), -1)

        card_table[col["cost"]] = 0
        need_review = card_table[col["due"]] <= today
        card_table[col["rand"]][need_review] = np.random.rand(
            np.sum(need_review))
        forget = card_table[col["rand"]] > card_table[col["retrievability"]]
        card_table[col["cost"]][need_review & forget] = forget_cost
        card_table[col["cost"]][need_review & ~forget] = recall_cost
        true_review = need_review & (
                np.cumsum(card_table[col["cost"]]) <= max_cost_perday)
        card_table[col["last_date"]][true_review] = today

        card_table[col["lapses"]][true_review & forget] += 1
        card_table[col["reps"]][true_review & ~forget] += 1

        card_table[col["stability"]][true_review & forget] = stability_after_failure(
            card_table[col["stability"]][true_review & forget], card_table[col["retrievability"]][true_review & forget],
            card_table[col["difficulty"]][true_review & forget])

        review_ratings = np.random.choice([1, 2, 3], np.sum(true_review & ~forget), p=[0.3, 0.6, 0.1])
        card_table[col["stability"]][true_review & ~forget] = stability_after_success(
            card_table[col["stability"]][true_review & ~forget],
            card_table[col["retrievability"]][true_review & ~forget],
            card_table[col["difficulty"]][true_review & ~forget], review_ratings)

        card_table[col["difficulty"]][true_review & forget] = np.clip(
            card_table[col["difficulty"]][true_review & forget] + 2 * w[6], 1, 10)

        need_learn = card_table[col["due"]] == learn_span
        card_table[col["cost"]][need_learn] = learn_cost
        true_learn = need_learn & (
                np.cumsum(card_table[col["cost"]]) <= max_cost_perday)
        card_table[col["last_date"]][true_learn] = today
        first_ratings = np.random.choice(4, np.sum(true_learn), p=first_rating_prob)
        card_table[col["stability"]][true_learn] = np.choose(
            first_ratings, w[:4])
        card_table[col["difficulty"]][true_learn] = w[4] - \
                                                    w[5] * (first_ratings - 3)

        card_table[col["ivl"]][true_review | true_learn] = np.clip(np.round(
            9 * card_table[col["stability"]][true_review | true_learn] * (1 / request_retention - 1), 0), 1, max_ivl)
        card_table[col["due"]][true_review | true_learn] = today + \
                                                           card_table[col["ivl"]][true_review | true_learn]

        review_cnt_per_day[today] = np.sum(true_review)
        learn_cnt_per_day[today] = np.sum(true_learn)
        memorized_cnt_per_day[today] = card_table[col["retrievability"]].sum()

    return card_table, review_cnt_per_day, learn_cnt_per_day, memorized_cnt_per_day


# Multimodal Learning Simulation
def simulate_multimodal_srs(
        baseline_retention=0.9,
        modality_weights=[1.0, 1.2, 0.9, 1.3, 1.4, 1.1],
        learning_days=100,
        cards_per_day=20,
        initial_ease=2.5,
        max_ease=3.5,
        min_ease=1.3,
        learning_rate=0.05,
        max_cost_perday=200,
        progress=None
):
    """Simulate the adaptive multimodal spaced repetition system over time."""

    # Initialize tracking arrays
    total_cards = min(cards_per_day * learning_days, 10000)  # Cap to reasonable size
    reviews_per_day = np.zeros(learning_days)
    retention_per_day = np.zeros(learning_days)
    modality_usage = {mod: np.zeros(learning_days) for mod in ContentModality}
    modality_success = {mod: np.zeros(learning_days) for mod in ContentModality}

    # Card state tracking
    card_ease = np.ones(total_cards) * initial_ease
    card_interval = np.ones(total_cards)
    card_due_day = np.zeros(total_cards)
    card_reps = np.zeros(total_cards)

    # When each card is introduced
    card_intro_day = np.zeros(total_cards)
    for i in range(total_cards):
        card_intro_day[i] = i // cards_per_day

    # System's belief about user preferences (starts neutral)
    believed_modality_preference = np.ones(len(ContentModality))

    # User's true preferences (based on input weights)
    true_modality_preference = np.array(modality_weights)

    # Run the simulation
    iterator = tqdm(range(learning_days)) if progress is None else range(learning_days)
    for day in iterator:
        if progress is not None:
            progress(0.5 + (day / learning_days) * 0.5)  # Use second half of progress for multimodal

        # Find cards due today
        due_mask = (card_due_day <= day) & (card_intro_day <= day)
        due_cards = np.where(due_mask)[0]

        # Track daily cost to stay within max_cost_perday
        daily_cost = 0

        reviews_today = 0
        correct_today = 0

        # Randomize review order
        if len(due_cards) > 0:
            np.random.shuffle(due_cards)

        # Process each due card
        for card_id in due_cards:
            # Check if we still have time budget
            if daily_cost >= max_cost_perday:
                break

            reviews_today += 1

            # Choose modality based on current beliefs
            modality_idx = np.random.choice(
                len(ContentModality),
                p=believed_modality_preference / believed_modality_preference.sum()
            )
            modality = ContentModality(modality_idx + 1)

            # Track modality usage
            modality_usage[modality][day] += 1

            # Calculate recall probability based on interval and modality
            recall_prob = np.power(1 + card_interval[card_id] / (9 * card_ease[card_id]), -1)
            mod_factor = true_modality_preference[modality.value - 1]
            recall_prob = min(0.99, recall_prob * mod_factor)

            # Simulate if user remembers card
            remembered = np.random.random() < recall_prob

            if remembered:
                # Success - increase ease factor
                card_ease[card_id] = min(max_ease, card_ease[card_id] + 0.1)
                correct_today += 1
                modality_success[modality][day] += 1
                daily_cost += 10  # Review cost

                # Update interval using SM-2 algorithm with modality
                if card_reps[card_id] == 0:
                    card_interval[card_id] = 1
                elif card_reps[card_id] == 1:
                    card_interval[card_id] = 6
                else:
                    card_interval[card_id] = card_interval[card_id] * card_ease[card_id]

                card_reps[card_id] += 1
            else:
                # Failure - decrease ease factor
                card_ease[card_id] = max(min_ease, card_ease[card_id] - 0.2)
                card_interval[card_id] = 1
                card_reps[card_id] = 0
                daily_cost += 30  # Relearn cost

            # Update due date
            card_due_day[card_id] = day + max(1, int(card_interval[card_id]))

            # Update belief about modality effectiveness
            update_vector = np.zeros(len(ContentModality))
            update_vector[modality.value - 1] = learning_rate * (1 if remembered else -1)
            believed_modality_preference += update_vector

            # Ensure beliefs are positive
            believed_modality_preference = np.maximum(0.1, believed_modality_preference)

        # Add new cards if we have budget left
        new_cards_today = 0
        for i in range(total_cards):
            if card_intro_day[i] == day:
                if daily_cost + 10 <= max_cost_perday:  # Check if we can afford to learn
                    daily_cost += 10  # Learn cost
                    new_cards_today += 1
                else:
                    # Postpone introduction if no time left today
                    card_intro_day[i] += 1

        # Calculate daily stats
        if reviews_today > 0:
            retention_per_day[day] = correct_today / reviews_today
        else:
            retention_per_day[day] = 0

        reviews_per_day[day] = reviews_today

    # Calculate effectiveness per modality
    modality_effectiveness = {}
    for mod in ContentModality:
        usage = modality_usage[mod]
        success = modality_success[mod]

        effectiveness = np.zeros(learning_days)
        for i in range(learning_days):
            if usage[i] > 0:
                effectiveness[i] = success[i] / usage[i]

        modality_effectiveness[mod] = effectiveness

    # Calculate average retention rate at the end
    final_retention = np.mean(retention_per_day[max(0, learning_days - 10):])

    return {
        'reviews_per_day': reviews_per_day,
        'retention_per_day': retention_per_day,
        'modality_usage': modality_usage,
        'modality_effectiveness': modality_effectiveness,
        'final_modality_beliefs': believed_modality_preference,
        'true_modality_preference': true_modality_preference,
        'final_retention': final_retention
    }


def run_combined_simulation(
        # FSRS parameters
        fsrs_weights,
        retrievability,
        stability,
        difficulty,
        # Multimodal parameters
        text_weight,
        image_weight,
        audio_weight,
        video_weight,
        interactive_weight,
        mixed_weight,
        # Shared parameters
        target_retention,
        learning_time,
        learning_days,
        deck_size,
        max_ivl,
        recall_cost,
        forget_cost,
        learn_cost,
        learning_rate,
        progress=gr.Progress()
):
    """Run both simulations and generate combined output"""
    np.random.seed(42)  # For reproducibility

    # Parse FSRS weights
    weights_str = ",".join([fsrs_weights, retrievability, stability, difficulty]).replace('[', '').replace(']', '')
    w = list(map(lambda x: float(x.strip()), weights_str.split(',')))

    # Calculate max cost per day in seconds
    max_cost_perday = int(learning_time) * 60

    # Run FSRS simulation
    (card_table,
     fsrs_review_cnt,
     fsrs_learn_cnt,
     fsrs_memorized_cnt) = simulate_fsrs(w,
                                         request_retention=float(target_retention),
                                         deck_size=int(deck_size),
                                         learn_span=int(learning_days),
                                         max_cost_perday=max_cost_perday,
                                         max_ivl=int(max_ivl),
                                         recall_cost=int(recall_cost),
                                         forget_cost=int(forget_cost),
                                         learn_cost=int(learn_cost),
                                         progress=progress)

    # Run multimodal simulation
    modality_weights = [
        float(text_weight),
        float(image_weight),
        float(audio_weight),
        float(video_weight),
        float(interactive_weight),
        float(mixed_weight)
    ]

    multi_results = simulate_multimodal_srs(
        baseline_retention=float(target_retention),
        modality_weights=modality_weights,
        learning_days=int(learning_days),
        cards_per_day=int(deck_size) // int(learning_days),
        initial_ease=2.5,
        learning_rate=float(learning_rate),
        max_cost_perday=max_cost_perday,
        progress=progress
    )

    # Create visualization plots
    plots = create_combined_plots(
        fsrs_review_cnt,
        fsrs_learn_cnt,
        fsrs_memorized_cnt,
        multi_results,
        int(learning_days)
    )

    # Generate recommendations
    recommendations = generate_recommendations(
        fsrs_review_cnt,
        multi_results,
        int(learning_days),
        target_retention,
        modality_weights
    )

    return plots + [recommendations]


def create_combined_plots(fsrs_review_cnt, fsrs_learn_cnt, fsrs_memorized_cnt, multi_results, learning_days):
    """Create visualization plots from both simulation results"""

    # Ensure smooth window size is reasonable
    smooth_window = min(7, learning_days // 10)
    if smooth_window < 2:
        smooth_window = 2

    # Plot 1: Review Counts Comparison
    fig1 = plt.figure(figsize=(10, 6))
    ax = fig1.add_subplot(111)

    if len(fsrs_review_cnt) > smooth_window:
        ax.plot(moving_average(fsrs_review_cnt, smooth_window), 'b-',
                label='Standard SRS Reviews')
    else:
        ax.plot(fsrs_review_cnt, 'b-', label='Standard SRS Reviews')

    if len(multi_results['reviews_per_day']) > smooth_window:
        ax.plot(moving_average(multi_results['reviews_per_day'], smooth_window), 'r-',
                label='Multimodal SRS Reviews')
    else:
        ax.plot(multi_results['reviews_per_day'], 'r-', label='Multimodal SRS Reviews')

    ax.set_xlabel('Day')
    ax.set_ylabel('Number of Reviews')
    ax.set_title('Review Counts: Standard vs Multimodal SRS')
    ax.legend()

    # Plot 2: Retention & Memorization
    fig2 = plt.figure(figsize=(10, 6))
    ax1 = fig2.add_subplot(111)

    if len(multi_results['retention_per_day']) > smooth_window:
        ax1.plot(moving_average(multi_results['retention_per_day'], smooth_window), 'g-',
                 label='Multimodal Retention Rate')
    else:
        ax1.plot(multi_results['retention_per_day'], 'g-', label='Multimodal Retention Rate')

    ax1.set_xlabel('Day')
    ax1.set_ylabel('Retention Rate')
    ax1.set_ylim(0, 1.0)
    ax1.legend(loc='upper left')

    ax2 = ax1.twinx()
    ax2.plot(fsrs_memorized_cnt, 'b--', label='Standard SRS Cumulative Memorized')
    ax2.set_ylabel('Cumulative Memorized Items')
    ax2.legend(loc='upper right')

    ax1.set_title('Retention Rate & Memorized Items')

    # Plot 3: Modality Effectiveness
    fig3 = plt.figure(figsize=(10, 6))
    ax = fig3.add_subplot(111)

    for mod in ContentModality:
        effectiveness = multi_results['modality_effectiveness'][mod]
        if len(effectiveness) > smooth_window:
            smooth_eff = moving_average(effectiveness, smooth_window)
            ax.plot(range(len(smooth_eff)), smooth_eff, label=mod.name)
        else:
            ax.plot(effectiveness, label=mod.name)

    ax.set_xlabel('Day')
    ax.set_ylabel('Success Rate')
    ax.set_ylim(0, 1.0)
    ax.set_title('Modality Effectiveness Over Time')
    ax.legend()

    # Plot 4: Modality Usage Over Time
    fig4 = plt.figure(figsize=(10, 6))
    ax = fig4.add_subplot(111)

    modality_data = []
    mod_labels = []

    for mod in ContentModality:
        usage_data = multi_results['modality_usage'][mod]
        if len(usage_data) > smooth_window:
            modality_data.append(moving_average(usage_data, smooth_window))
        else:
            modality_data.append(usage_data)
        mod_labels.append(mod.name)

    modality_data = np.array(modality_data)

    # Create stacked area plot
    x = range(len(modality_data[0]))
    ax.stackplot(x, modality_data, labels=mod_labels)

    ax.set_xlabel('Day')
    ax.set_ylabel('Number of Reviews')
    ax.set_title('Modality Distribution Over Time')
    ax.legend()

    return [fig1, fig2, fig3, fig4]


def generate_recommendations(fsrs_review_cnt, multi_results, learning_days, target_retention, modality_weights):
    """Generate personalized recommendations based on simulation results"""

    # Find most effective modalities
    modality_avg_effectiveness = {}
    for mod in ContentModality:
        effectiveness = multi_results['modality_effectiveness'][mod]
        # Calculate average of last 25% of days to get mature effectiveness
        start_idx = max(0, int(learning_days * 0.75))
        avg_eff = np.mean(effectiveness[start_idx:]) if len(effectiveness) > start_idx else np.mean(effectiveness)
        modality_avg_effectiveness[mod] = avg_eff

    # Sort modalities by effectiveness
    sorted_modalities = sorted(modality_avg_effectiveness.items(), key=lambda x: x[1], reverse=True)

    # Analyze review patterns
    avg_reviews_std = np.mean(fsrs_review_cnt)
    peak_reviews_std = np.max(fsrs_review_cnt)
    avg_reviews_multi = np.mean(multi_results['reviews_per_day'])

    # Calculate efficiency gain
    std_retention = np.mean(fsrs_review_cnt[-10:]) / np.mean(fsrs_review_cnt[:10]) if len(fsrs_review_cnt) > 10 else 1
    multi_retention = multi_results['final_retention']

    efficiency_gain = (multi_retention / float(target_retention)) / (avg_reviews_multi / avg_reviews_std)

    # Generate recommendations
    top_modalities = [mod.name for mod, _ in sorted_modalities[:3]]

    # Dynamic time period calculations based on learning_days
    total_period = learning_days

    # Scale intervals based on learning period length
    if total_period <= 30:  # Short learning period
        initial_interval = (1, 1)
        second_interval = (1, 2)
        third_interval = (2, 3)
        long_term_start = "Week 2+"
    elif total_period <= 90:  # Medium learning period
        initial_interval = (1, 2)
        second_interval = (2, 4)
        third_interval = (4, 7)
        long_term_start = "Week 4+"
    else:  # Long learning period
        initial_interval = (1, 3)
        second_interval = (3, 6)
        third_interval = (6, 10)
        long_term_start = "Month 2+"

    # Calculate period durations (as percentage of total learning period)
    initial_period = max(1, int(total_period * 0.1))  # 10% of learning period
    second_period = max(1, int(total_period * 0.15))  # 15% of learning period
    third_period = max(1, int(total_period * 0.25))  # 25% of learning period

    # Format period text based on learning days
    if total_period < 14:
        period_unit = "days"
        initial_text = f"Days 1-{initial_period}"
        second_text = f"Days {initial_period + 1}-{initial_period + second_period}"
        third_text = f"Days {initial_period + second_period + 1}-{initial_period + second_period + third_period}"
    elif total_period < 60:
        period_unit = "weeks"
        initial_text = f"Week 1"
        second_text = f"Week 2"
        third_text = f"Weeks 3-4"
    else:
        period_unit = "months"
        initial_text = f"Month 1"
        second_text = f"Month 2"
        third_text = f"Month 3"

    recommendation = f"""
# Learning Optimization Recommendations

## Target Retention Analysis
- Target retention rate: {float(target_retention):.1%}
- Achieved retention with multimodal approach: {multi_retention:.1%}
- Estimated learning efficiency gain: {efficiency_gain:.2f}x

## Optimal Modality Recommendations
Based on the simulation, the most effective learning modalities for you are:
1. **{top_modalities[0]}** (Primary) - Use for initial learning and difficult content
2. **{top_modalities[1]}** (Secondary) - Use for reinforcement and review
3. **{top_modalities[2]}** (Supplementary) - Use for variety and to prevent fatigue

## Review Schedule Optimization
- Optimal workload per day: {int(min(20, avg_reviews_std / 3))}
- Recommended review sessions: {2 if avg_reviews_std > 30 else 1} per day

## Spaced Repetition Strategy
- **{initial_text}:** Focus on using {top_modalities[0]} modality with shorter intervals ({initial_interval[0]}-{initial_interval[1]} {period_unit})
- **{second_text}:** Introduce {top_modalities[1]} modality and extend intervals ({second_interval[0]}-{second_interval[1]} {period_unit})
- **{third_text}:** Begin mixing in {top_modalities[2]} for variety and extend intervals ({third_interval[0]}-{third_interval[1]} {period_unit})
- **{long_term_start}:** Prioritize tough content in {top_modalities[0]} format, and maintain variety with other modalities

## Estimated Results
Following this personalized approach should help you:
- Reduce total review time by approximately {min(75, int(100 * (1 - 1 / efficiency_gain)))}%
- Reach your target retention rate of {float(target_retention):.1%} or higher
- Maintain knowledge for longer periods with less review
"""

    return recommendation


# Create the Gradio interface
title = """
# CS6460-Ed Tech: Comprehensive Multimodal Spaced Repetition Learning Dashboard

This dashboard combines two powerful learning optimization approaches:
1. **Free Spaced Repetition Scheduler (FSRS)** - An advanced algorithm for optimal review timing
2. **Multimodal Learning System** - A system that adapts content presentation to your learning preferences

## Parameter Settings

- **Preset Parameters**: These are pre-calibrated values based on research data that define the underlying models
  - FSRS Model Parameters: Define the mathematical model for spaced repetition intervals
  - Multimodal Weights: Define the effectiveness of different learning modalities
  - **Those preset parameters are for user personal learning patterns, since all of them are customized parameters, I leave default values here for now.**
  
- **Customizable Settings**: These are parameters you can adjust based on your specific learning scenario
  - Learning Period & Time: How long and how much time per day you plan to study
  - Target Retention: The memory retention rate you aim to achieve
  - Knowledge Load: How much material you need to learn
  
## How to Use This Dashboard

1. **Configure Settings** (Parameter Settings tab):
   - Adjust the preset parameters if you have specific data about your learning preferences
   - Set your customizable settings based on your actual study plan and goals
   - Click "Run Simulation" to process your configuration

2. **Review Analysis** (Analysis tab):
   - Compare standard vs. multimodal review patterns
   - Examine retention rates over time
   - Understand which modalities are most effective for your learning style
   - See how modality usage evolves as the system adapts to your preferences

3. **Apply Recommendations** (Recommendations tab):
   - Review the personalized learning strategy based on simulation results
   - Follow the suggested spaced repetition schedule and modality mix
   - Apply the recommendations to your actual study plan
  
  
Adjust the parameters below to see how different settings affect your learning efficiency, 
and get personalized recommendations for optimizing your study approach.
"""

with gr.Blocks() as demo:
    gr.Markdown(title)

    with gr.Tab("Parameter Settings"):
        with gr.Row():
            with gr.Column():
                gr.Markdown("### Spaced Repetition (FSRS) Settings")
                fsrs_weights = gr.Textbox(
                    label="Model Super-Parameter",
                    value="0.4, 0.6, 2.4, 5.8, 4.93, 0.94, 0.86, 0.01, 1.49, 0.14, 0.94, 2.18, 0.05, 0.34"
                )
                retrievability = gr.Textbox(label="Retrievability", value="0.9")
                stability = gr.Textbox(label="Stability", value="0.95")
                difficulty = gr.Textbox(label="Difficulty", value="1.06")

            with gr.Column():
                gr.Markdown("### Multimodal Learning Settings")
                text_weight = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Text Effectiveness")
                image_weight = gr.Slider(minimum=0.5, maximum=2.0, value=1.2, step=0.1, label="Image Effectiveness")
                audio_weight = gr.Slider(minimum=0.5, maximum=2.0, value=0.9, step=0.1, label="Audio Effectiveness")
                video_weight = gr.Slider(minimum=0.5, maximum=2.0, value=1.3, step=0.1, label="Video Effectiveness")
                interactive_weight = gr.Slider(minimum=0.5, maximum=2.0, value=1.4, step=0.1,
                                               label="Interactive Effectiveness")
                mixed_weight = gr.Slider(minimum=0.5, maximum=2.0, value=1.1, step=0.1, label="Mixed Effectiveness")

        with gr.Row():
            with gr.Column():
                gr.Markdown("### Shared Learning Parameters (Set Customized Parameters Here)")
                target_retention = gr.Slider(
                    label="Target Recall Rate",
                    minimum=0.7,
                    maximum=0.99,
                    value=0.9,
                    step=0.01
                )
                learning_time = gr.Slider(
                    label="Learning Time Per Day (minutes)",
                    minimum=5,
                    maximum=120,
                    value=30,
                    step=5
                )
                learning_days = gr.Slider(
                    label="Learning Period (days)",
                    minimum=30,
                    maximum=365,
                    value=100,
                    step=5
                )
                deck_size = gr.Slider(
                    label="Knowledge Load",
                    minimum=100,
                    maximum=10000,
                    value=1000,
                    step=100
                )

            with gr.Column():
                max_ivl = gr.Slider(
                    label="Maximum Interval (days)",
                    minimum=1,
                    maximum=365,
                    value=36,
                    step=1
                )
                recall_cost = gr.Slider(
                    label="Review Cost (seconds)",
                    minimum=1,
                    maximum=60,
                    value=10,
                    step=1
                )
                forget_cost = gr.Slider(
                    label="Relearn Cost (seconds)",
                    minimum=1,
                    maximum=120,
                    value=30,
                    step=1
                )
                learn_cost = gr.Slider(
                    label="Learn Cost (seconds)",
                    minimum=1,
                    maximum=60,
                    value=10,
                    step=1
                )
                learning_rate = gr.Slider(
                    label="Learning Rate",
                    minimum=0.01,
                    maximum=0.2,
                    value=0.05,
                    step=0.01
                )

        run_btn = gr.Button("Run Simulation", variant="primary")

    with gr.Tab("Analysis"):
        with gr.Row():
            plot1 = gr.Plot(label="Review Counts: Standard vs Multimodal")
            plot2 = gr.Plot(label="Retention Rate & Memorized Items")
        with gr.Row():
            plot3 = gr.Plot(label="Modality Effectiveness Over Time")
            plot4 = gr.Plot(label="Modality Distribution Over Time")

    with gr.Tab("Recommendations"):
        recommendations = gr.Markdown(label="Personalized Learning Recommendations")

    # Connect the button to the function
    run_btn.click(
        fn=run_combined_simulation,
        inputs=[
            fsrs_weights, retrievability, stability, difficulty,
            text_weight, image_weight, audio_weight, video_weight, interactive_weight, mixed_weight,
            target_retention, learning_time, learning_days, deck_size, max_ivl,
            recall_cost, forget_cost, learn_cost, learning_rate
        ],
        outputs=[plot1, plot2, plot3, plot4, recommendations]
    )

if __name__ == "__main__":
    demo.launch(show_error=True,share=True)