import pandas as pd
import streamlit as st

from app_utils import filter_dataframe, calculate_height_to_display
from contants import INFO_CATALOG, CITATION_CATALOG, HOWTO_CATALOG,INFO_BENCHMARK, CITATION_BENCHMARK, INFO_SURVEY, CITATION_SURVEY
from utils import BASE_SUMMARY_METRICS
from utils import  load_data_catalog, load_data_taxonomy, load_bench_catalog, load_bench_taxonomy
from utils import datasets_count_and_size, datasets_count_and_size_standard, metadata_coverage, catalog_summary_statistics

import matplotlib.pyplot as plt
import seaborn as sns


st.set_page_config(layout="wide")


# Load PL ASR data survey data
# Cache the dataframe so it's only loaded once
df_data_cat = load_data_catalog()
df_data_tax = load_data_taxonomy()

# Filter out non available datasets
df_data_cat_available = df_data_cat[df_data_cat['Available online'] == 'yes']
# Available and free
df_data_cat_available_free = df_data_cat[(df_data_cat['Available online'] == 'yes') & (df_data_cat['Price - non-commercial usage'] == 'free')]

# Available and paid    
df_data_cat_available_paid = df_data_cat[(df_data_cat['Available online'] == 'yes') & (df_data_cat['Price - non-commercial usage'] != 'free')]


# Load PL ASR benchmarks survey data
df_bench_cat = load_bench_catalog()
df_bench_tax = load_bench_taxonomy()

data_cat, data_taxonomy, data_survey, bench_cat, bench_taxonomy, bench_survey = st.tabs(["PL ASR speech data **catalog**", "PL ASR speech data **survey**", "ASR speech data **taxonomy**", "PL ASR benchmarks catalog", "ASR benchmarks taxonomy", "PL ASR benchmarks survey"])


with data_cat:
    st.title("Polish ASR Speech Datasets Catalog")

    st.markdown(INFO_CATALOG, unsafe_allow_html=True)

    st.header("How to use?")
    st.markdown(HOWTO_CATALOG, unsafe_allow_html=True)
    
    st.header("How to cite?")
    st.markdown(CITATION_CATALOG, unsafe_allow_html=True)

    # Display catalog contents
    st.header("Browse the catalog content")
    st.dataframe(filter_dataframe(df_data_cat, "datasets"), hide_index=True, use_container_width=True)

    # Display taxonomy contents

with data_survey:
    # Display summary statistics
    st.title("Polish ASR Speech Datasets Survey")

    st.header("Polish ASR speech datasets summary statistics")
    df_summary_metrics = catalog_summary_statistics(df_data_cat)

    df_basic_stats = df_summary_metrics.loc[BASE_SUMMARY_METRICS[0:5]]
    st.dataframe(df_basic_stats, use_container_width=False)

    st.header("Speech data available across Polish ASR speech datasets")
    df_stats_audio_available = df_summary_metrics.loc[BASE_SUMMARY_METRICS[5:10]]
    st.dataframe(df_stats_audio_available, use_container_width=False)

    st.header("Transcribed data available across Polish ASR speech datasets")
    df_stats_transcribed_available = df_summary_metrics.loc[BASE_SUMMARY_METRICS[10:15]]
    st.dataframe(df_stats_transcribed_available, use_container_width=False)


    # Display distribution of datasets created per year
    st.header("Polish ASR speech datasets created in 1997-2023")
    col_groupby = ['Creation year']
    df_datasets_per_speech_type = datasets_count_and_size(df_data_cat, col_groupby, col_sort=col_groupby, col_percent=None, col_sum=['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])

    st.dataframe(df_datasets_per_speech_type, use_container_width=False)

    st.header("Institutions contributing Polish ASR speech dataset")
    col_groupby = ['Publisher']
    df_datasets_per_publisher = datasets_count_and_size(df_data_cat, col_groupby, col_sort='Count Dataset ID', col_percent=None, col_sum=['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])
    st.dataframe(df_datasets_per_publisher, use_container_width=False)

    st.header("Repositories hosting Polish ASR speech datasets")
    col_groupby = ['Repository']
    df_datasets_per_repo = datasets_count_and_size(df_data_cat, col_groupby, col_sort='Count Dataset ID', col_percent=None, col_sum=['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])
    st.dataframe(df_datasets_per_repo, use_container_width=False)

    st.header("Public domain Polish ASR speech datasets")
    col_groupby = ['License', "Dataset ID"]
    df_datasets_public = datasets_count_and_size(df_data_cat_available_free, col_groupby, col_sort='License', col_percent=None, col_sum=['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = [])
    st.dataframe(df_datasets_public, use_container_width=False)

    st.header("Commercialy available Polish ASR speech datasets")
    col_groupby = ['License', "Dataset ID"]
    df_datasets_paid = datasets_count_and_size(df_data_cat_available_paid, col_groupby, col_sort='License', col_percent=None, col_sum=['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = [])
    st.dataframe(df_datasets_paid, use_container_width=False)

    st.header("Coverage of metadata across Polish ASR speech datasets")
    df_meta_all_flat, df_meta_all_pivot = metadata_coverage(df_data_cat, df_data_cat_available_free, df_data_cat_available_paid)
    st.dataframe(df_meta_all_pivot, use_container_width=False)

    # Display distribution of datasets for various speech types
    st.header("Datasets per speech type")
    col_groupby = ['Speech type']
    df_datasets_per_speech_type = datasets_count_and_size(df_data_cat, col_groupby, col_sort=col_groupby, col_percent = ['Size audio transcribed [hours]'], col_sum = ['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])
    st.dataframe(df_datasets_per_speech_type, use_container_width=False)

    # Display distribution of datasets for various speech types
    st.header("Distribution of available speech data per audio device - Public domain datasets")
    col_groupby = ['Audio device']
    df_datasets_per_device = datasets_count_and_size(df_data_cat_available_free, col_groupby, col_sort=col_groupby, col_percent = ['Size audio transcribed [hours]'], col_sum = ['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])
    st.dataframe(df_datasets_per_device, use_container_width=False)

    # Display distribution of datasets for various speech types
    st.header("Distribution of available speech data per audio device - Commercial datasets")
    col_groupby = ['Audio device']
    df_datasets_per_device = datasets_count_and_size(df_data_cat_available_paid, col_groupby, col_sort=col_groupby, col_percent = ['Size audio transcribed [hours]'], col_sum = ['Size audio transcribed [hours]','Audio recordings', 'Speakers'], col_count = ['Dataset ID'])
    st.dataframe(df_datasets_per_device, use_container_width=False)

with bench_cat:
    st.write("Benchmarks catalog")
    # TODO - load and display benchmarks catalog
    st.title("Polish ASR Benchmarks Catalog")


    # Display catalog contents
    st.dataframe(filter_dataframe(df_bench_cat, "benchmarks"), hide_index=True, use_container_width=True)

    # Display taxonomy contents