'''
Live version accessible at: 
https://coins-of-bengal.streamlit.app
'''

import streamlit as st
import pandas as pd
import plotly.express as px
import altair as alt
import os
import re
import warnings
from kaggle.api.kaggle_api_extended import KaggleApi
from PIL import Image

# Verbose Error Suppression
def configure_app():
    st.set_page_config(page_title="Coins of Bengal", layout="wide")
    st.set_option('client.showErrorDetails', False)
    warnings.filterwarnings("ignore")

# Landing Page
def landing_page():
    image_paths = [
        "data/Muslim Conquerors/9.2 Giasuddin Bahadur Ghazi re .webp",
        "data/Muslim Conquerors/29.5 Shamsuddin Firuz Shah re .webp",
        "data/Muslim Conquerors/1.1 Ikhtiyar Khilji re .webp",
        "data/Muslim Conquerors/2.1 Ali Mardan re .webp",
        "data/Muslim Conquerors/22.6 Ruknuddin Barbak Shah .webp"
    ]

    cols = st.columns(len(image_paths))
    for i, path in enumerate(image_paths):
        with cols[i]:
            try:
                # Optimize image loading with thumbnail and explicit format
                img = Image.open(path)
                img.thumbnail((500, 500))  # Resize for faster loading, adjust as needed
                st.image(img, use_container_width=True)
            except FileNotFoundError:
                st.error(f"Image not found at: {path}")
            except Exception as e:
                st.error(f"Error loading image {path}: {e}")

    st.markdown("<h2 style='text-align: center;'>Engineer Noorul Islam,<br>Proprietor of the actual museum and the private dataset</h2>", unsafe_allow_html=True)
    st.markdown("<h2 style='text-align: center;'>Dr. Md. Ataur Rahman,<br>Researcher and Archaeologist</h2>", unsafe_allow_html=True)
    st.markdown("<h2 style='text-align: center;'>Athoye Mahmud,<br>Developer and Data Science Undergrad, UIU</h2>", unsafe_allow_html=True)
    st.markdown("""
        <style>
            .footer {
                position: fixed;
                bottom: 0;
                left: 0;
                width: 100%;
                background: linear-gradient(90deg, #D4AF37, #FFD700); /* Metallic Gold */
                text-align: center; /* Default: Centered */
                padding: 5px;
                font-size: 14px;
                font-weight: 600;
                color: black;
                box-shadow: 0 -1px 3px rgba(0, 0, 0, 0.2);
            }

            .footer small {
                font-size: 14px;
                padding: 0;
                margin: 0;
                display: inline-block;
            }

            /* Responsive Design */
            @media screen and (max-width: 720px) {  /* Applies when screen width <= 720px */
                .footer {
                    text-align: left;
                    padding-left: 10px;
                }
            }
        </style>

        <div class="footer">
            <small>©️ Published January 2025. All rights reserved.</small>
        </div>
    """, unsafe_allow_html=True)
    st.markdown("---")

# Load and preprocess data (from Kaggle)
@st.cache_data
def load_data():
    try:
        # Initialize the Kaggle API and authenticate
        api = KaggleApi()
        api.authenticate()

        # Specify dataset and file path
        dataset_path = 'athoye/coins-of-bengal'
        file_name = 'coins.csv'
        download_path = 'data'  # Local directory to store the file

        # Download the file
        api.dataset_download_file(dataset_path, file_name, path=download_path, force=False, quiet=False)

        # Load the data into a pandas DataFrame
        csv_path = os.path.join(download_path, file_name)
        df = pd.read_csv(csv_path)

        # Standardize column names
        df.rename(columns=lambda x: x.strip(), inplace=True)
        df.rename(columns={
            "Coin No.": "Coin No.",
            "Ruler": "Ruler (or Issuer)",
            "Weight": "Weight (g)",
            "Dimensions": "Dimension (mm)",
        }, inplace=True)

        df["Weight (g)"] = pd.to_numeric(df["Weight (g)"], errors="coerce")
        df["Dimension (mm)"] = pd.to_numeric(df["Dimension (mm)"], errors="coerce")

        for col in ["Ruler (or Issuer)", "Reign", "Metal", "Mint", "Date of Issue"]:
            df[col].fillna("Unknown", inplace=True)
        for col in ["Weight (g)", "Dimension (mm)"]:
            df[col].fillna(0, inplace=True)

        # Function to convert Arabic calendar years to Gregorian
        def convert_to_gregorian(year):
            try:
                year = int(year)  # Ensure year is an integer
                if year < 1202:
                    return year + 622  # Approximate conversion
                return year
            except ValueError:
                return "Unknown"  # Handle cases where conversion is not possible

        # Apply the conversion to the "Date of Issue" column
        df['Date of Issue'] = df['Date of Issue'].apply(convert_to_gregorian)

        return df
    except Exception as e:
        st.error(f"Error loading data from Kaggle: {e}")
        st.info("Make sure you have a valid kaggle.json file in the correct location (~/.kaggle/kaggle.json).")
        return pd.DataFrame() # Return empty dataframe to prevent later errors

# Image matching (using Kaggle dataset)
@st.cache_data
def load_images():
    try:
        api = KaggleApi()
        api.authenticate()
        dataset_path = 'athoye/coins-of-bengal'
        download_path = 'data'
        api.dataset_download_files(dataset_path, path=download_path, unzip=True, force=False, quiet=False)
        images_folder = os.path.join(download_path, "Muslim Conquerors")
        return match_images(images_folder)
    except Exception as e:
         st.error(f"Error downloading or unzipping image dataset: {e}")
         return {}

def match_images(images_folder):
    images_dict = {}
    if not os.path.exists(images_folder):
        st.error(f"Error: Image folder not found at: {images_folder}")
        return images_dict

    pattern = re.compile(r"(\d+\.\d+)[_\s-]*.*\.webp$", re.IGNORECASE)
    for filename in os.listdir(images_folder):
        match = pattern.match(filename)
        if match:
            coin_no = match.group(1)
            if coin_no not in images_dict:
                images_dict[coin_no] = {"front": None, "back": None}
            lower_name = filename.lower()
            if " re " in lower_name or lower_name.endswith(" re.webp") or " re." in lower_name:
                images_dict[coin_no]["back"] = os.path.join(images_folder, filename)
            else:
                images_dict[coin_no]["front"] = os.path.join(images_folder, filename)
    return images_dict

def sidebar_filters(df):
    st.sidebar.header("🔍 Filter Coins")

    if df is not None and not df.empty:
        # Ruler filter with a dropdown
        selected_ruler = st.sidebar.selectbox("Select Ruler", ["All"] + sorted(df["Ruler (or Issuer)"].unique()))

        # Metal filter with multiselect
        selected_metals = st.sidebar.multiselect("Select Metal", options=["All"] + sorted(df["Metal"].unique()), default=["All"])

        # Improved Reign filter with a range slider
        reigns = sorted(df["Reign"].unique())
        if reigns:
            # Extract years from reign strings
            years = [int(y.split('–')[0]) for y in reigns if '–' in y]
            if years:
                min_year = min(years)
                max_year = max(years)
                selected_years = st.sidebar.slider(
                    "Select Reign Years",
                    min_value=min_year,
                    max_value=max_year,
                    value=(min_year, max_year)
                )

        # Weight filter with a slider
        weight_range = st.sidebar.slider(
            "Select Weight Range (g)",
            min_value=float(df["Weight (g)"].min()),
            max_value=float(df["Weight (g)"].max()),
            value=(0.0, float(df["Weight (g)"].max()))
        )

        # Apply filters
        filtered_df = df.copy()

        if selected_ruler != "All":
            filtered_df = filtered_df[filtered_df["Ruler (or Issuer)"] == selected_ruler]

        if "All" not in selected_metals:
            filtered_df = filtered_df[filtered_df["Metal"].isin(selected_metals)]

        # Apply reign filter
        if reigns and years:
            filtered_df = filtered_df[
                filtered_df["Reign"].apply(
                    lambda x: selected_years[0] <= int(x.split('–')[0]) <= selected_years[1]
                )
            ]

        filtered_df = filtered_df[(filtered_df["Weight (g)"] >= weight_range[0]) & (filtered_df["Weight (g)"] <= weight_range[1])]
        return filtered_df
    return pd.DataFrame()  # Return empty dataframe if input df is empty

# Display data
def display_data(df):
    st.subheader("📜 Coin Database")
    if df is not None and not df.empty:  # Check for empty DataFrame
       st.dataframe(df, use_container_width=True)
    else:
        st.warning("No data available to display.")  # Display a warning
    st.markdown("---")

# Visualizations
def display_visualizations(df):
    st.subheader("📊 Coin Data Insights")

    if df is not None and not df.empty:

        # Filter out rows where "Weight (g)" is zero or NaN
        df_filtered = df[df["Weight (g)"].notna() & (df["Weight (g)"] != 0)]
        fig1 = px.histogram(df_filtered, x="Weight (g)", nbins=20, title="Distribution of Coin Weights", marginal="rug")

        # Make titles and axis labels bolder
        fig1.update_layout(
            title_font=dict(size=24),
            xaxis_title="Weight (g)",
            yaxis_title="Count",
            xaxis_title_font=dict(size=18),
            yaxis_title_font=dict(size=18)
        )
        st.plotly_chart(fig1, use_container_width=True)

        fig2 = px.scatter(
            df, x="Weight (g)", y="Dimension (mm)", color="Metal",
            size="Weight (g)", hover_data=["Ruler (or Issuer)"], title="Coin Weight vs. Dimension"
        )

        # Make titles and axis labels bolder
        fig2.update_layout(
            title_font=dict(size=24),
            xaxis_title="Weight (g)",
            yaxis_title="Dimension (mm)",
            xaxis_title_font=dict(size=18),
            yaxis_title_font=dict(size=18)
        )
        st.plotly_chart(fig2, use_container_width=True)

        alt_chart = alt.Chart(df).mark_bar().encode(
            x=alt.X("Metal:N", title="Metal Type"),
            y=alt.Y("count()", title="Count"),
            color="Metal"
        ).properties(title="Metal Type Distribution")

        # Make titles and axis labels bolder
        alt_chart = alt_chart.configure_title(
            fontSize=24,
            font="Arial",
            color="black"
        ).configure_axis(
            labelFontSize=18,
            titleFontSize=18,
            titleFont="Arial",
            titleColor="black"
        )
        st.altair_chart(alt_chart, use_container_width=True)

        # Count number of coins per ruler
        ruler_counts = df["Ruler (or Issuer)"].value_counts().reset_index()
        ruler_counts.columns = ["Ruler (or Issuer)", "Coin Count"]
        # Plot using Plotly
        fig3 = px.bar(
            ruler_counts,
            x="Coin Count",
            y="Ruler (or Issuer)",
            orientation="h",
            title="🏛 Number of Coins Issued per Ruler",
            labels={"Coin Count": "Number of Coins", "Ruler (or Issuer)": "Ruler"},
            text_auto=True
        )

        # Improve appearance
        fig3.update_layout(
            title_font=dict(size=24),
            xaxis_title="Number of Coins",
            yaxis_title="Ruler",
            xaxis_title_font=dict(size=18),
            yaxis_title_font=dict(size=18)
        )
        fig3.update_layout(yaxis=dict(categoryorder="total ascending"), xaxis=dict(showgrid=True))
        # Show plot in Streamlit
        st.plotly_chart(fig3)  # Use Streamlit to display the plot

    else:
        st.warning("No data to create visualizations")

# Display coins with images
def display_coins_with_images(df, images_dict):
    st.subheader("🖼️ Coin Details with Images")
    if df is not None and not df.empty:
        for idx, row in df.iterrows():
            coin_no = str(row["Coin No."])
            st.markdown(f"### Coin No. {coin_no}")
            st.markdown(f"<h2 style='font-size:24px; font-weight:bold;'>Ruler: {row['Ruler (or Issuer)']}</h2>", unsafe_allow_html=True)
            st.write(f"**Reign:** {row['Reign']}")
            st.write(f"**Metal:** {row['Metal']}")
            st.write(f"**Weight (g):** {row['Weight (g)']}")
            st.write(f"**Dimension (mm):** {row['Dimension (mm)']}")
            st.write(f"**Mint:** {row['Mint']}")
            st.write(f"**Date of Issue:** {row['Date of Issue']}")

            front_path = images_dict.get(coin_no, {}).get("front")
            back_path = images_dict.get(coin_no, {}).get("back")

            col1, col2 = st.columns(2)

            # Image loading and display with error handling and optimization
            with col1:
                if front_path:
                    try:
                        img = Image.open(front_path)
                        img.thumbnail((500, 500)) # Resize for faster loading
                        st.image(img, caption=f"{coin_no} (Front)", use_container_width=True)
                    except Exception as e:
                        st.error(f"Error loading front image: {e}")
                else:
                    st.warning("Front image not found.")

            with col2:
                if back_path:
                    try:
                        img = Image.open(back_path)
                        img.thumbnail((400, 400))  # Resize for faster loading
                        st.image(img, caption=f"{coin_no} (Back)", use_container_width=True)
                    except Exception as e:
                        st.error(f"Error loading back image: {e}")
                else:
                    st.warning("Back image not found.")

            st.markdown("---")
    else:
        st.warning("No coins to display.")

def test_kaggle_connection():
    st.title("🔌 Kaggle Connection Test")

    try:
        # Initialize the Kaggle API
        api = KaggleApi()
        api.authenticate()

        # Test connection by listing competitions
        competitions = api.competitions_list()

        st.success("✅ Successfully connected to Kaggle!")

        # Display some data to verify
        st.write("### Recent Kaggle Competitions:")
        for comp in competitions[:5]:  # Show first 5 competitions
            st.write(f"- {comp.title}")

    except Exception as e:
        st.error(f"❌ Failed to connect to Kaggle: {str(e)}")
        st.info("Make sure you have kaggle.json in ~/.kaggle/ directory")

# Main function
def main():
    configure_app()
    st.title("🪙 Coins of Bengal, A Digital Coin Museum", anchor="center")

    # Navigation
    pages = {
        "Home": landing_page,
        "Data": display_data,
        "Visualizations": display_visualizations,
        "Coin Catalog": display_coins_with_images,
        "Kaggle Connection Test": test_kaggle_connection
    }

    st.sidebar.title("🧭 Navigation")
    selection = st.sidebar.radio("⤵️ Go to", list(pages.keys()))

    # Load data and images (using Kaggle)
    coins_df = load_data()
    images_dict = load_images()
    filtered_df = sidebar_filters(coins_df)

    # Display selected page, handling potential None/empty DataFrame
    if selection == "Home":
        pages[selection]()  # Landing page doesn't need data
    elif selection in ["Data", "Visualizations", "Coin Catalog"]:
        if not filtered_df.empty:  # Only pass data if it's not empty
          pages[selection](filtered_df, images_dict)
        else:
          st.warning("No data available based on current filter selection.")
          if selection == "Coin Catalog": #Need to account for empty dict too
              st.warning("Ensure image directory has been downloaded.")
    elif selection == "Kaggle Connection Test":
        test_kaggle_connection()

if __name__ == "__main__":
    main()