import streamlit as st
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from datetime import datetime
from base64 import b64encode
import time

FOOD_LIST = {4: "🥦", 6: "🍇", 8: "🥕", 10: "🍅", 12: "🍓", 20: "🥑", 50: "🍍", 100: "🥥"}

def roll_dice(num_rolls, dice_type):
    rolls = np.random.randint(1, dice_type + 1, size=num_rolls)
    return rolls

def plot_tokens(health_tokens, coin_tokens):
    fig = go.Figure()
    fig.add_trace(go.Sankey(
        node = {
            "label": ["Health", "Coins"] + [FOOD_LIST[i] for i in DICE_TYPES],
            "pad": 15
        },
        link = {
            "source": [0, 1] + list(range(2, len(DICE_TYPES) + 2)),
            "target": [2] * len(DICE_TYPES) + [3 + i for i in range(len(DICE_TYPES))],
            "value": health_tokens + coin_tokens
        },
    ))
    return fig

st.set_page_config(page_title="🍎🥕🥦🍓Sankey Snacks🌽🥒🍇🥤", page_icon=":game_die:")
st.title("🍎🥕🥦🍓Sankey Snacks🌽🥒🍇🥤")

username = st.sidebar.text_input("👤 Enter your username:")
num_rolls = st.sidebar.slider("🔢 Choose the number of rolls:", 1, 100, 3)

DICE_TYPES = [4, 6, 8, 10, 12, 20, 50, 100]
history = {"health_tokens": [0], "coin_tokens": [0]}
fig_element = st.empty()

for i in range(10):
    for dice_type in DICE_TYPES:
        rolls = roll_dice(num_rolls, dice_type)
        highest_rolls = sum(roll == dice_type for roll in rolls)
        coin_tokens_added = 0

        dice_results = [f"{FOOD_LIST[dice_type]} {roll}" for roll in rolls]
        st.write(f"🎰 Results for {dice_type}-sided slot machine: {' | '.join(dice_results)}")

        for roll in rolls:
            if roll == dice_type:
                st.write(f"🎉 Congratulations! You got the {FOOD_LIST[dice_type]} jackpot! 💰 Adding 3 coins.")
                coin_tokens_added += 3
            if roll == max(rolls):
                st.write(f"🎉 Congratulations! You got the {FOOD_LIST[dice_type]} maximum value! 💖 Adding 10 health tokens.")
                if dice_type == 100:
                    history["health_tokens"].append(history["health_tokens"][-1] + 10)

        history[f"{dice_type}-sided slot machine jackpots"] = highest_rolls
        history["roll_history"] = {**history.get("roll_history", {}), dice_type: rolls}
        history["coin_tokens"].append(history["coin_tokens"][-1] + coin_tokens_added)

    fig = plot_tokens(history["health_tokens"], history["coin_tokens"])
    fig_element.plotly_chart(fig)
    time.sleep(1)

df = pd.concat([pd.DataFrame(history["roll_history"]), pd.DataFrame(history["health_tokens"], columns=["Health Tokens"]), pd.DataFrame(history["coin_tokens"], columns=["Coin Tokens

#df = pd.concat([pd.DataFrame(history["roll_history"]), pd.DataFrame(history["health_tokens"], columns=["Health Tokens"]), pd.DataFrame(history["coin_tokens"], columns=["Coin Tokens"])], axis=1)

timestamp = datetime.now().strftime("%m-%d-%Y-%H-%M-%S")
filename = f"{username}_{timestamp}.csv"
df.to_csv(filename, index=False)
st.markdown(f'<a href="data:file/csv;base64,{b64encode(open(filename, "rb").read()).decode()}" download="{filename}">Download CSV File</a>', unsafe_allow_html=True)

st.markdown("""

📣 Introducing 🍎🥕🥦🍓Sankey Snacks🌽🥒🍇🥤 - the game that makes healthy eating fun and interactive! 
👉 Are you tired of the same old boring STEM activities? Sankey Snacks is here to add some excitement to your learning experience! 🤩
👉 Our game uses Sankey diagrams to show you the flow of nutrients in the food you eat. 🌡️
👉 From crispy 🍎 to crunchy 🥕, Sankey Snacks makes it easy to see how the food you eat affects your body. And don't worry, we didn't forget about the occasional 🍔 or 🍟 treat! 🤤
👉 Download Sankey Snacks today and start exploring the world of healthy eating in a fun and interactive way! 🎮
👉 It's time to ditch the boring old textbooks and embrace the world of Sankey diagrams and healthy snacks! 🥦🥤
👉 Get ready to snack and learn with Sankey Snacks! 🍇🌽🍓🍒🥒🍊🥕🥤

""")