#!/usr/bin/env python

# coding: utf-8

# In[192]:

#pip install wx

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import pandas as pd

from rake_nltk import Rake

from sklearn.metrics.pairwise import cosine_similarity

from sklearn.feature_extraction.text import CountVectorizer

import tkinter

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pd.set_option('display.max_columns', 100)

ES = pd.read_csv('C:/Users/G50/Desktop/SRBCC/SRBCC/Inventario_English.spanish/inventario_spanish.csv', error_bad_lines=False, encoding="latin-1")

ES.head()

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ES = ES[['Titulo', 'autores', 'materia']]

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ES['materia'] = ES['materia'].astype(str)

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#inicializando la columna

ES['palabras_clave'] = ""

for index, row in ES.iterrows():

materia = row['materia']

# instanciando rake, que utiliza las stopwords en el idioma ingles y descartando

# puntuaciones

r = Rake(language="spanish")

# extrayendo a las palabras y pasandolas al texto

r.extract_keywords_from_text(materia)

key_words_dict_scores = r.get_word_degrees()

# asignando las palabras clave a la columna palabras_clave

row['palabras_clave'] = list(key_words_dict_scores.keys())

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ES = ES.drop("materia", axis=1)

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ES.set_index('Titulo', inplace = True)

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ES['palabras'] = ''

columns = ES.columns

for index, row in ES.iterrows():

words = ''

for col in columns:

if col != 'autores':

words = words + ' '.join(row[col])+ ''

else:

words = words + row[col]+ ' '

row['palabras'] = words

ES.drop(columns = [col for col in ES.columns if col!= 'palabras'], inplace = True)

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count = CountVectorizer()

count_matrix = count.fit_transform(ES['palabras'])

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indices = pd.Series(ES.index)

cosine_sim = cosine_similarity(count_matrix, count_matrix)

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def recomendaciones(title, cosine_sim = cosine_sim):

return recomendaciones_peliculas

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recomendaciones("mirada en dos tiempos")

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recomendaciones('pequeños contribuyentes fiscal 1')

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recomendaciones("capitan alatriste")

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count = CountVectorizer()

count_matrix = count.fit_transform(ES['palabras'])

indices = pd.Series(ES.index)

cosine_sim = cosine_similarity(count_matrix, count_matrix)

from tkinter import *

def hi():

global dave

dave = startEntry.get()

def recomendaciones(cosine_sim = cosine_sim):

label.config(text=recomendaciones_peliculas)

frame = Tk()

frame.title("Sistema de recomendacion")

frame.geometry("800x300")

frame.config(bg="#AEB6BF")

imagen=PhotoImage(file="book.png")

titulo = Label(frame, text="Sistema de recomendacion de la biblioteca de CU", font=(20))

titulo.place(x=300, y=100)

titulo.pack()

startLabel =Label(frame,text="Ingresa un libro de tu preferencia: ")

startLabel.place(x=250, y=250)

labelR=Label(frame, text="Recomendaciones:", bg="white", fg='black')

labelR.place(x=25, y=150)

label=Label(frame,text='...',bg='black',fg='white')

label.place(x=25,y=180)

startEntry=Entry(frame)

startLabel.pack()

startEntry.pack()

plotButton= Button(frame,text="Guardar", command=hi)

plotButton.config(fg = "black", bg = "white")

plotButton2= Button(frame,text="Obtener recomendaciones", command=recomendaciones)

plotButton2.config(fg = "white", bg = "black")

plotButton.pack()

plotButton2.pack()

frame.mainloop()

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