import urllib.request
import re
from bs4 import BeautifulSoup
import plotly.express as px
import pandas as pd
import numpy as np
import pickle
import os
from natsort import natsorted, natsort_keygen, ns

url = "https://appdb.winehq.org/objectManager.php?bIsQueue=false&bIsRejected=false&sClass=application&sTitle=Browse+Applications&iItemsPerPage=200&sOrderBy=appName&bAscending=true&sOrderBy=appId&bAscending=true&iPage="
url_version = "https://appdb.winehq.org/objectManager.php?sClass=application&iId="

#
# get pages
#

req = urllib.request.Request(
    url + "1", 
    data=None, 
    headers={
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
    }
)

f = urllib.request.urlopen(req)
html = f.read().decode('utf-8')

pages = int(re.search(r'of <b>(\d*)<\/b>', html, re.IGNORECASE).group(1))
pages

#
# get applications
#

applications = None

for i in range(pages):
    req = urllib.request.Request(
        url + str(i+1), 
        data=None, 
        headers={
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
        }
    )
    f = urllib.request.urlopen(req)
    html = f.read().decode('utf-8')
    soup = BeautifulSoup(html, "html.parser")
    table = soup.find("table", {"class": "whq-table"})

    if isinstance(applications, pd.DataFrame):
        applications = pd.concat([applications, pd.read_html(table.prettify())[0]])
    else:
        applications = pd.read_html(table.prettify())[0]

pickle.dump(applications, open("wine_applications.pickle", "wb"))
applications

if os.path.isfile("wine_applications.pickle"):
    applications = pickle.load(open("wine_applications.pickle", "rb"))

applications_to_process = applications.iloc[:, 1].values.tolist()
versions = None

#
# get versions
#

if os.path.isfile("wine_versions.pickle"):
    applications_to_process, versions = pickle.load(open("wine_versions.pickle", "rb"))

while len(applications_to_process) > 0:
    req = urllib.request.Request(
        url_version + str(applications_to_process[0]), 
        data=None, 
        headers={
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
        }
    )
    f = urllib.request.urlopen(req)
    html = f.read().decode('utf-8')
    soup = BeautifulSoup(html, "html.parser")
    table = soup.find("table", {"class": "whq-table"})

    if table != None:
        df = pd.read_html(table.prettify(), extract_links="body")[0]
        df["AppId"] = applications_to_process[0]

        if isinstance(versions, pd.DataFrame):
            versions = pd.concat([versions, df])
        else:
            versions = df

    applications_to_process.remove(applications_to_process[0])
    
    pickle.dump((applications_to_process, versions), open("wine_versions.pickle", "wb"))

    print("remain: " + str(len(applications_to_process)))

versions_to_process = []
for i in range(versions.shape[0]):
    versions_to_process.append((versions["Version"].tolist()[i][0], versions["Version"].tolist()[i][1], versions["AppId"].tolist()[i]))
tests = None

#
# get tests
#

if os.path.isfile("wine_tests.pickle"):
    versions_to_process, tests = pickle.load(open("wine_tests.pickle", "rb"))

while len(versions_to_process) > 0:
    req = urllib.request.Request(
        versions_to_process[0][1] + "&bShowAll=true", 
        data=None, 
        headers={
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
        }
    )
    f = urllib.request.urlopen(req)
    html = f.read().decode('utf-8')
    soup = BeautifulSoup(html, "html.parser")
    table = soup.find("div", id="collapse-tests").find("table", {"class": "whq-table"})

    if table != None:
        df = pd.read_html(table.prettify(), extract_links="body")[0]
        df["Ver"] = versions_to_process[0][0]
        df["AppId"] = versions_to_process[0][2]

        if isinstance(tests, pd.DataFrame):
            tests = pd.concat([tests, df])
        else:
            tests = df
            
    versions_to_process.remove(versions_to_process[0])
    
    pickle.dump((versions_to_process, tests), open("wine_tests.pickle", "wb"))

    print("remain: " + str(len(versions_to_process)))

if os.path.isfile("wine_applications.pickle"):
    applications = pickle.load(open("wine_applications.pickle", "rb"))
if os.path.isfile("wine_versions.pickle"):
    applications_to_process, versions = pickle.load(open("wine_versions.pickle", "rb"))
if os.path.isfile("wine_tests.pickle"):
    versions_to_process, tests = pickle.load(open("wine_tests.pickle", "rb"))

version_no = natsorted([x for x, y in tests["Wine version"].drop_duplicates().tolist() if not "staging" in x and not "rc" in x], alg=ns.IGNORECASE)
version_no

df = pd.DataFrame({"version": version_no})
df

tests_edit = tests.copy()
tests_edit["Wine version"] = [x for x, y in tests_edit["Wine version"]]
tests_edit["Test date"] = [x for x, y in tests_edit["Test date"]]
tests_edit["Rating"] = [x for x, y in tests_edit["Rating"]]
tests_edit

df1 = pd.merge(df, tests_edit, how="left", left_on="version", right_on="Wine version")
df1 = df1[["version", "Test date", "Rating"]]
df1

df2 = pd.pivot_table(df1, index="version", columns="Rating", values="Rating", aggfunc="count").reset_index()
df2 = df2.fillna(0)
df2["Sum"] = df2["Bronze"] + df2["Garbage"] + df2["Gold"] + df2["Platinum"] + df2["Silver"]
df2["Bronze %"] = df2["Bronze"] / df2["Sum"]
df2["Garbage %"] = df2["Garbage"] / df2["Sum"]
df2["Gold %"] = df2["Gold"] / df2["Sum"]
df2["Platinum %"] = df2["Platinum"] / df2["Sum"]
df2["Silver %"] = df2["Silver"] / df2["Sum"]
df2 = df2.replace([np.inf, -np.inf], 0)
df2

df3 = df2.copy()
df3 = df3[["version", "Bronze %", "Garbage %", "Gold %", "Platinum %", "Silver %"]]
df3 = pd.melt(df3, id_vars="version", value_vars=list(df3.columns[1:]))
df3['Rating'] = df3['Rating'].str.replace(' %','')
df3['order'] = df3['Rating'].replace({'Garbage':0, 'Bronze':1, 'Silver':2, 'Gold':3, 'Platinum':4})
df3

fig = px.bar(df3.sort_values(["version", "order"], key=natsort_keygen()), width=800, height=400, x="version", y="value", color="Rating", color_discrete_map={"Garbage": 'rgb(255, 0, 0)', "Bronze": 'rgb(255, 128, 2)', "Silver": 'rgb(255, 255, 0)', "Gold": 'rgb(128, 192, 0)', "Platinum": 'rgb(0, 128, 0)'})
fig.update_layout(bargap=0)
fig.update_layout({
    'plot_bgcolor': 'rgba(0, 0, 0, 0)',
    'paper_bgcolor': 'rgba(255, 255, 255, 255)',
    'title': dict(text = 'WineHQ ratings of applications for different wine versions', y=0.955),
    'margin': dict( l = 10, r = 10, b = 10, t = 30)
})
fig.update_traces(marker_line_width=0)
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_xaxes(title='Version')
fig.write_image("wine.svg")
fig.show()