import requests
from lxml import etree
from bs4 import BeautifulSoup
import re
from io import BytesIO
import pandas as pd
from pathlib import Path
import os
import sys
import click
from data.main import connect, map_tld, paths
from random import randint
from time import sleep
from tqdm import tqdm


@click.command('mbfc:parse-index')
def parse_index():
    parser = etree.HTMLParser()
    publishers = []
    for page in range(1, 54):
        url = f"https://mediabiasfactcheck.com/filtered-search/?pg={page}"
        print(f"downloading {url}", file=sys.stderr)
        response = requests.get(url)
        html = response.content
        tree = etree.parse(BytesIO(html), parser)
        rows = tree.xpath('//table[@class="mbfc-table"]/tbody/tr')
        print(f"parsing {len(rows)} rows", file=sys.stderr)
        for row in rows:
            publisher = {}
            link, bias, reporting, country, credibility, media_type, traffic, popularity = tuple(col for col in row.iterchildren())
            link = link.xpath('./a')[0]
            publisher['name'] = link.text
            publisher['detail_url'] = link.get('href')
            publisher['bias'] = bias.text
            publisher['reporting'] = reporting.text
            publisher['country'] = country.text
            publisher['credibility'] = credibility.text
            publisher['media_type'] = media_type.text
            publisher['traffic'] = traffic.text
            publisher['popularity'] = popularity.xpath('./span')[0].text
            publishers.append(publisher)
    df = pd.DataFrame(publishers)
    save_to = paths('data') / 'mbfc_bias.csv'
    df.to_csv(save_to, sep='|', index=False)
    print(f"saved {len(df)}: {save_to}", file=sys.stderr)

@click.command("mbfc:schema")
def schema():
    with connect() as db:
        db.sql("""create schema mbfc""")
        db.sql("""create or replace table mbfc.scrape (
            url text
            ,scraped_at datetime default now()
            )
        """)

@click.command("mbfc:scrape")
def scrape():

    df = pd.read_csv(paths('data') / 'mbfc_bias.csv', sep="|")

    with connect() as db:
        stats  = db.query("""
            select
                count(1) filter(where s.url is not null) as elapsed
                ,count(1) filter(where s.url is null) as remaining
            from df
            left join mbfc.scrape s
            on df.detail_url = s.url
        """).fetchall()
        df = db.query("""
            select
                detail_url as url
            from df
            where df.detail_url not in (
                select
                    url
                from mbfc.scrape
            )
        """).df()
    print(f"{stats[0][0]} elapsed. {stats[0][1]} remaining.")

    for url in df.url:
        delay = randint(1,3)
        save_as = paths('data') / 'mbfc' / (url.strip('/').split('/')[-1] + '.html')
        print(f"downloading (delay: {delay}): {url}", file=sys.stderr)
        sleep(delay)
        try:
            response = requests.get(url)
        except Exception as e:
            print(f"request failed: {url}", file=sys.stderr)
            continue
        with open(save_as, 'w') as f:
            f.write(response.text)
        with connect() as db:
            db.execute("""insert into mbfc.scrape (url) values (?)""", [url])
        print(f"saved: {save_as}", file=sys.stderr)

def load():

    publishers = []
    for i, page in enumerate(tqdm((paths('data') / 'mbfc').iterdir())):
        publisher = {}
        publisher['origin_url'] = f"https://mediabiasfactcheck.com/{page.stem}"
        with page.open() as p:
            tree = BeautifulSoup(p, 'html.parser')
        for e in tree(string=re.compile(r'source:', re.IGNORECASE)):
            e = e.parent
            while e.name != 'p':
                e = e.parent
            l = e.find('a')
            if l:
                publisher['tld'] = l.get('href')
                break
            else:
                breakpoint()
        publishers.append(publisher)
    df = pd.DataFrame(publishers)
    df.to_csv(paths('data') / 'mbfc_publisher_url.csv', index=False, sep="|")

@click.command('mbfc:create-tables')
def create_tables():

    pubs = pd.read_csv(paths('data') / 'mbfc_publishers.csv', sep='|')
    urls = pd.read_csv(paths('data') / 'mbfc_publisher_url.csv', sep="|")
    df = pubs.merge(urls, on='mbfc_url')
    df['tld'] = df.tld.apply(map_tld)
    df['ordinal'] = df.bias.apply(bias_label_to_int)

    with connect() as db:
        db.sql("""
            CREATE OR REPLACE TABLE mbfc.publishers AS
            SELECT
                row_number() over() as id
                ,p.tld
                ,mode(p.name) as name
                ,mode(p.bias) as bias
                ,mode(p.ordinal) as ordinal
                ,mode(p.reporting) as reporting
                ,mode(p.country) as country
                ,mode(p.credibility) as credibility
                ,mode(p.media_type) as media_type
                ,mode(p.traffic) as traffic
                ,mode(p.popularity) as popularity
            FROM df p
            GROUP BY
                p.tld
        """)

    with connect() as db:
        raw_stories = db.sql("""
            SELECT
                *
            FROM stories s
        """).df()

    stories['tld'] = stories.url.apply(map_tld)
    
    with connect() as db:
        db.sql("""
            CREATE OR REPLACE TABLE mbfc.publisher_stories AS
            SELECT
               s.id as story_id
               ,p.id as publisher_id
            FROM raw_stories s
            JOIN mbfc.publishers p
            ON p.tld = s.tld
        """)