#-------------------------------------libraries ----------------------------------

import os
import pandas as pd
import streamlit as st
import plotly.graph_objs as go
import numpy as np
import plotly.express as px 
import logging
# Set up logging basic configuration
logging.basicConfig(level=logging.INFO)
# Example of logging
logging.info("Streamlit app has started")


#-------------------------------------back ----------------------------------

# etherscan
## Load the data from the CSV files
dataframes = []
for filename in os.listdir('output'):
    if filename.endswith('.csv'):
        df_temp = pd.read_csv(os.path.join('output', filename), sep=';')
        dataframes.append(df_temp)
df_etherscan = pd.concat(dataframes)
del df_temp

# CMC
## Load cmc data
df_temp = pd.read_csv("output/top_100_update.csv", sep=',')
df_cmc = df_temp[df_temp["last_updated"] == df_temp["last_updated"].max()]
del df_temp
#-------------------------------------streamlit ----------------------------------

# Set the title and other page configurations
st.title('Crypto Analysis')
# Create two columns for the two plots
col1, col2 = st.columns(2)

with st.container():

    with col1:
        # etherscan
        selected_token = st.selectbox('Select Token', df_etherscan['tokenSymbol'].unique(), index=0)
        # Filter the data based on the selected token
        filtered_df = df_etherscan[df_etherscan['tokenSymbol'] == selected_token]
        # Plot the token value over time
        st.plotly_chart(
            go.Figure(
                data=[
                    go.Scatter(
                        x=filtered_df['timeStamp'],
                        y=filtered_df['value'],
                        mode='lines',
                        name='Value over time'
                    )
                ],
                layout=go.Layout(
                    title='Token Value Over Time',
                    yaxis=dict(
                        title=f'Value ({selected_token})',
                    ),
                    showlegend=True,
                    legend=go.layout.Legend(x=0, y=1.0),
                    margin=go.layout.Margin(l=40, r=0, t=40, b=30),
                    width=500,
                    height=500

                )
            )
        )

    with col2:
        # cmc
        selected_var = st.selectbox('Select Token', ["percent_change_24h","percent_change_7d","percent_change_90d"], index=0)
        # Sort the DataFrame by the 'percent_change_24h' column in ascending order
        df_sorted = df_cmc.sort_values(by=selected_var, ascending=False)
        # Select the top 10 and worst 10 rows
        top_10 = df_sorted.head(10)
        worst_10 = df_sorted.tail(10)
        # Combine the top and worst dataframes for plotting
        combined_df = pd.concat([top_10, worst_10], axis=0)
        max_abs_val = max(abs(combined_df[selected_var].min()), abs(combined_df[selected_var].max()))

        # Create a bar plot for the top 10 with a green color scale
        fig = go.Figure(data=[
            go.Bar(
                x=top_10["symbol"],
                y=top_10[selected_var],
                marker_color='rgb(0,100,0)',  # Green color for top 10
                hovertext= "Name : "+top_10["name"].astype(str)+ '<br>' +
                        selected_var + " : " + top_10["percent_tokens_circulation"].astype(str) + '<br>' +
                        'Market Cap: ' + top_10["market_cap"].astype(str) + '<br>' +
                        'Fully Diluted Market Cap: ' + top_10["fully_diluted_market_cap"].astype(str) + '<br>' +
                        'Last Updated: ' + top_10["last_updated"].astype(str),
                name="top_10"
            )
        ])

        # Add the worst 10 to the same plot with a red color scale
        fig.add_traces(go.Bar(
                x=worst_10["symbol"],
                y=worst_10[selected_var],
                marker_color='rgb(255,0,0)',  # Red color for worst 10
                hovertext="Name:"+worst_10["name"].astype(str)+ '<br>' +
                        selected_var + " : " + worst_10["percent_tokens_circulation"].astype(str) + '<br>' +
                        'Market Cap: ' + worst_10["market_cap"].astype(str) + '<br>' +
                        'Fully Diluted Market Cap: ' + worst_10["fully_diluted_market_cap"].astype(str) + '<br>' +
                        'Last Updated: ' + worst_10["last_updated"].astype(str),
                name="worst_10"
            )
        )

        # Customize aspect
        fig.update_traces(marker_line_color='rgb(8,48,107)', marker_line_width=1.5, opacity=0.8)
        fig.update_layout(title_text=f'Top 10 and Worst 10 by {selected_var.split("_")[-1]} Percentage Change')
        fig.update_xaxes(categoryorder='total ascending')
        fig.update_layout(
            autosize=False,
            width=500,
            height=500,
            margin=dict(
                l=50,
                r=50,
                b=100,
                t=100,
                pad=4
            ),
            #paper_bgcolor="LightSteelBlue",
        )
        st.plotly_chart(fig)




#-------------------------------------end ----------------------------------