from flask import Flask
from flask import request, render_template
from markdown import markdown
import sys

app = Flask("recommend")

# appending a path
sys.path.append('../')
  
# importing customised module
import summa.edits
from summa.edits import scored_sentences, similarity_graph

import wikipage
from wikipage.page import get_wikipage

context = {
    'page_name' : "door",
    'page' : None,
    'graph' : None,
    'sentences' : None,
    'current_sentence' : None
}

#   TextRank
#   ------------------------------------------------------------------------


def get_similar_sentences(s0, sentences, graph):
    # return a list of all the sentences ordered by similarity to s0
    similar_sentences = sorted(sentences, key=lambda s: graph.edge_weight((s0.token, s.token)), reverse=True)
    return similar_sentences


#   main
#   ------------------------------------------------------------------------

def new_page(page_name):
    # from a pagename get
    # 1. get the page text
    # 2. graph and scored sentences

    # --- WIKI REQUEST ---

    # get text from wikipedia
    print('--- WIKI ---')
    context['page'] = get_wikipage(page_name)
    if not context['page']:
        sys.exit("--- STOP ---")
    text = context['page'].content

    # print text in terminal
    print('--- TXT ---')
    print(text)

    # --- APPLY TEXTRANK ---

    # apply textrank
    context['sentences'] = scored_sentences(text)
    context['graph'] = similarity_graph(text)
   

@app.route("/")
def index():

    if 'token' in request.args:
        token = request.args['token']
        context['current_sentence'] = [s for s in context['sentences'] if s.token == token][0]

    else:
        new_page(context['page_name'])
        sorted_sentences = sorted(context['sentences'], key=lambda s: s.score, reverse=True)
        best_sentence = sorted_sentences[0]

        print("--- START/BEST ---")
        print(best_sentence.text)

        context['current_sentence'] = best_sentence

    # sentences ordered by similarity to the best one
    similar_sentences = get_similar_sentences(context['current_sentence'] , context['sentences'], context['graph'])

    current_sentence=context['current_sentence']
    return render_template('index.html', current_sentence=current_sentence , similar_sentences=similar_sentences)


    # # --- APPLY TEXTRANK ---

    # # apply textrank
    # sentences = scored_sentences(text)
    # graph = similarity_graph(text)

    # # print ranked sentences in terminal
    # print('--- GRAPH ---')

    # # notes: 
    # # * the matrix/graph does not contain the sentences but their .token

    # sorted_sentences = sorted(sentences, key=lambda s: s.score, reverse=True)
    # best_sentence = sorted_sentences[0]

    # print("START/BEST: \n")
    # print(best_sentence.text + "\n")

    # # sentences ordered by similarity to the best one
    # similar_sentences = similar_sentences(best_sentence, sentences, graph)

    # for i in range(len(similar_sentences)):
    #     print(str(i) + ". " + similar_sentences[i].text)


    # # for s1 in sentences:
    # #     for s2 in sentences:

    # #         if s1 != s2:
    # #             if graph.has_node(s1.token) and graph.has_node(s2.token):

    # #                 weight = graph.edge_weight((s1.token, s2.token))

    # #                 print('---')
    # #                 print('1. ' + s1.text)
    # #                 print('2. ' + s2.text)
    # #                 print('similarity: ' + str(weight))