import base64
import json
from datetime import datetime, timedelta

import pandas as pd
import pytz
import streamlit as st

# File paths
predictions_csv = 'predictions.csv'
users_json = 'users.json'
matches_json = 'matches.json'
outcomes_json = 'match_outcomes.json'
image_path = 'ipl_image.png'


# Initialize CSV and JSON files if they don't exist
def initialize_files():
    # Initialize predictions CSV
    try:
        pd.read_csv(predictions_csv)
    except FileNotFoundError:
        df = pd.DataFrame(columns=['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points'])
        df.to_csv(predictions_csv, index=False)


# Load users from JSON
def get_users():
    try:
        with open(users_json, 'r') as file:
            users = json.load(file)
        return list(users.keys())
    except FileNotFoundError:
        return []


# Load matches from JSON
def load_matches():
    try:
        with open(matches_json, 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return []


def load_match_outcomes():
    try:
        with open(outcomes_json, 'r') as file:
            return json.load(file)
    except FileNotFoundError:
        return []


# Load the player list from the JSON file
def load_player_list():
    with open('players.json', 'r') as file:
        return json.load(file)


def get_base64_of_image(path):
    with open(path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode()


# Get today's date in IST to load today's match
def get_current_date_ist():
    tz_IST = pytz.timezone('Asia/Kolkata')
    datetime_ist = datetime.now(tz_IST)
    return datetime_ist.strftime('%Y-%m-%d')


# Function to get matches for today
def get_today_matches():
    today = get_current_date_ist()
    matches = load_matches()
    today_matches = [match for match in matches if match['date'] == today]
    return today_matches


# Function to check if prediction submission is allowed
def is_submission_allowed(match_id):
    matches = load_matches()  # This loads matches correctly with IST times

    for match in matches:
        if match["match_id"] == match_id:
            # Parse the match start time in IST
            tz_IST = pytz.timezone('Asia/Kolkata')
            match_datetime_str = f'{match["date"]} {match["time"]}'
            # The match time string is like "2024-03-21 7:30 PM"
            match_datetime = datetime.strptime(match_datetime_str, "%Y-%m-%d %I:%M %p")
            match_datetime = tz_IST.localize(match_datetime)  # Set the timezone to IST

            # Get the current time in IST
            current_datetime = datetime.now(tz_IST)

            if current_datetime > match_datetime + timedelta(minutes=30):
                return False
            else:
                return True
    return False  # If match_id not found, default to False


# Submit prediction function
def submit_prediction(
        user_name,
        match_id,
        predicted_winner,
        predicted_motm,
        bid_points,
        max_bid_points
        ):

    # Validation for user selection
    if user_name == "Select a user...":
        st.warning("Please select a valid user.")
        return

    # Check if prediction submission is allowed for the match
    if not is_submission_allowed(match_id):
        st.error("Prediction submission time has passed. Predictions can't be submitted after match start.")
        return

    if bid_points > max_bid_points:
        st.error(f"Your bid points exceed the 20% limit of your total points. Maximum allowed bid points: {max_bid_points}")
        return

    # Ensure predictions DataFrame is loaded or initialized correctly
    try:
        predictions = pd.read_csv(predictions_csv)
        # Check if all expected columns are present, if not, reinitialize the DataFrame
        expected_columns = ['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points']
        if not all(column in predictions.columns for column in expected_columns):
            raise ValueError("CSV file missing one or more columns; Reinitializing.")
    except (FileNotFoundError, ValueError) as e:
        predictions = pd.DataFrame(columns=expected_columns)

    # Check for duplicate prediction for the same match by the same user
    if user_name != "Select a user...":
        existing_predictions = predictions[(predictions['user_name'] == user_name) & (predictions['match_id'] == match_id)]
        if not existing_predictions.empty:
            st.error("You've already submitted a prediction for this match.")
            return

    # Append new prediction
    new_prediction = {
        'user_name': user_name,
        'match_id': match_id,
        'predicted_winner': predicted_winner,
        'predicted_motm': predicted_motm,
        'bid_points': bid_points
    }

    predictions = pd.concat([predictions, pd.DataFrame([new_prediction])], ignore_index=True)
    predictions.to_csv(predictions_csv, index=False)
    st.success("Prediction submitted successfully!")


def get_user_total_points(user_name):
    with open(users_json, 'r') as file:
        users = json.load(file)
    return users.get(user_name, 0)


# Define the new function
def calculate_max_bid_points(user_name):
    total_points = get_user_total_points(user_name)
    max_bid_points = int(total_points * 0.20)  # 20% of total points
    return max_bid_points


# Streamlit UI
encoded_image = get_base64_of_image(image_path)
custom_css = f"""
<style>
.header {{
    font-size: 50px;
    color: #FFD700; /* Gold */
    text-shadow: -1px -1px 0 #000, 1px -1px 0 #000, -1px 1px 0 #000, 1px 1px 0 #000; /* Black text shadow */
    text-align: center;
    padding: 10px;
    background-image: url('data:image/png;base64,{encoded_image}');
    background-size: cover;
}}
</style>
"""
# Apply custom CSS
st.markdown(custom_css, unsafe_allow_html=True)
# Use the custom class in a div with your title
st.markdown('<div class="header">DIS IPL Match Predictions</div>', unsafe_allow_html=True)

st.write("🏆 Predict, Compete, and Win 🏏 - Where Every Guess Counts! 🏆")

user_guide_content = """
### 📘 User Guide

#### Submitting Predictions
- **Match Selection**: Choose the match you want to predict from today's available matches.
- **Team and Player Prediction**: Select the team you predict will win and the "Man of the Match".
- **Bid Points**: Enter the number of points you wish to bid on your prediction. Remember, the maximum you can bid is capped at 20% of your total points.

#### Scoring System
- **Winning Team Prediction**: Correct predictions earn you 1000 points, while incorrect predictions deduct 200 points.
- **Man of the Match Prediction**: Correctly predicting the "Man of the Match" awards you 200 points. No penalty for incorrect guesses.
- **Bonus Points**: An additional 200 points bonus is awarded for getting both the team and "Man of the Match" predictions right.

#### Bid Point Constraints
- You cannot bid more than 20% of your current total points.
- Bid points will be doubled if your prediction is correct, and deducted if incorrect.

#### Rules for Submission
- Predictions must be submitted before the match starts.
- Only one prediction per match is allowed.
- Review your prediction carefully before submission, as it cannot be changed once submitted.
"""

# User Guide as an expander
with st.expander("User Guide 📘"):
    st.markdown(user_guide_content)

# Prediction form
with st.expander("Submit Prediction 📝"):

    # User selection
    user_name = st.selectbox("Select User", ["Select a user..."] + get_users())

    # Initialize max_bid_points to None
    max_bid_points = None

    if user_name != "Select a user...":
        max_bid_points = calculate_max_bid_points(user_name)
        # Display the max bid points
        st.write(f"Maximum bid points you can submit: {max_bid_points}")

    # Match selection
    matches = get_today_matches()
    if matches:
        match_choice = st.selectbox("Select Today's Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}")
        match_id = match_choice['match_id']
        teams = match_choice['teams']
    else:
        st.write("No matches are scheduled for today.")
        st.stop()

    # Predictions
    predicted_winner = st.selectbox("Predicted Winner", teams)

    # Load the player list and populate the dropdown based on the selected team
    predicted_motm = ""
    player_list = load_player_list()
    if predicted_winner in player_list:
        players = player_list[predicted_winner]
        predicted_motm = st.selectbox("Predicted Man of the Match", players)
    # predicted_motm = st.text_input("Predicted Man of the Match")

    bid_points = st.number_input("Bid Points", min_value=1, value=100, format="%d")

    # Submit button
    if st.button("Submit Prediction"):
        if user_name != "Select a user...":
            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points)


# Show predictions
with st.expander("Predictions 🔍"):

    # Display predictions
    if st.button("Show Predictions"):
        try:
            df = pd.read_csv(predictions_csv)
            st.dataframe(df, hide_index=True)
        except FileNotFoundError:
            st.write("No predictions have been submitted yet.")


# Show leaderboard
with st.expander("Leaderboard 🏆"):

    # Display leaderboard
    if st.button("Show Leaderboard"):
        try:
            with open(users_json, 'r') as f:
                users = json.load(f)
            leaderboard = sorted(users.items(), key=lambda x: x[1], reverse=True)
            df_leaderboard = pd.DataFrame(leaderboard, columns=['User', 'Points'])

            # Add a 'Rank' column starting from 1
            df_leaderboard['Rank'] = range(1, len(df_leaderboard) + 1)

            # Reorder DataFrame columns so 'Rank' is first
            df_leaderboard = df_leaderboard[['Rank', 'User', 'Points']]

            # Reset index to remove the default index column
            df_leaderboard.reset_index(drop=True, inplace=True)

            st.dataframe(df_leaderboard, hide_index=True)
        except FileNotFoundError:
            st.write("Leaderboard data not available.")


############################# Admin Panel ##################################
ADMIN_PASSPHRASE = "admin123"


def load_predictions():
    # loading predictions from 'predictions.csv'
    try:
        return pd.read_csv(predictions_csv)
    except FileNotFoundError:
        return pd.DataFrame(columns=['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points'])


def load_users():
    with open(users_json, 'r') as file:
        return json.load(file)


def save_users(users):
    with open(users_json, 'w') as file:
        json.dump(users, file, indent=4)


def save_match_outcomes(outcomes):
    with open(outcomes_json, 'w') as file:
        json.dump(outcomes, file, indent=4)


def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
    outcomes = load_match_outcomes()  # Load existing match outcomes
    predictions = load_predictions()  # Load existing predictions
    users = load_users()  # Load existing user points

    # Update match outcomes
    match_outcome = next((outcome for outcome in outcomes if outcome['match_id'] == match_id), None)
    if match_outcome:
        match_outcome['winning_team'] = winning_team
        match_outcome['man_of_the_match'] = man_of_the_match
    else:
        outcomes.append({
            "match_id": match_id,
            "winning_team": winning_team,
            "man_of_the_match": man_of_the_match
        })

    # Update user points based on prediction accuracy
    match_predictions = predictions[predictions['match_id'] == match_id]
    for _, prediction in match_predictions.iterrows():
        user_name = prediction['user_name']
        # Initialize user points if not present
        users[user_name] = users.get(user_name, 0)

        # Points for correct or incorrect team prediction
        if prediction['predicted_winner'] == winning_team:
            users[user_name] += 1000  # Correct team prediction
            # Check for double or nothing bid points
            users[user_name] += prediction['bid_points'] * 2
        else:
            users[user_name] -= 200  # Deduct points for incorrect team prediction
            users[user_name] -= prediction['bid_points']  # Lose bid points for incorrect prediction

        # Points for correct man of the match prediction
        if prediction['predicted_motm'] == man_of_the_match:
            users[user_name] += 200  # Correct man of the match prediction
            # Bonus for getting both right
            if prediction['predicted_winner'] == winning_team:
                users[user_name] += 200

    # Save updated outcomes and user points
    save_match_outcomes(outcomes)
    save_users(users)


with st.sidebar:
    expander = st.expander("Admin Panel", expanded=False)
    admin_pass = expander.text_input("Enter admin passphrase:", type="password", key="admin_pass")

    if admin_pass == ADMIN_PASSPHRASE:
        expander.success("Authenticated")
        matches = get_today_matches()  # This function fetches today's matches

        # If matches are available, let the admin select one
        if matches:
            match_selection = expander.selectbox("Select Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}", key="match_selection")
            selected_match_id = match_selection['match_id']
            teams = match_selection['teams']

            # Let admin select the winning team
            winning_team = expander.selectbox("Winning Team", teams, key="winning_team")

            # Fetch and display players for the selected winning team
            player_list = load_player_list()
            if winning_team in player_list:
                players = player_list[winning_team]
                man_of_the_match = expander.selectbox("Man of the Match", players, key="man_of_the_match")
            else:
                players = []
                man_of_the_match = expander.text_input("Man of the Match (Type if not listed)", key="man_of_the_match_fallback")

            if expander.button("Submit Match Outcome", key="submit_outcome"):
                update_leaderboard_and_outcomes(selected_match_id, winning_team, man_of_the_match)
                expander.success("Match outcome submitted and leaderboard updated!")
        else:
            expander.write("No matches are available for today.")
    else:
        if admin_pass:  # Show error only if something was typed
            expander.error("Not authenticated")