import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import random

class Recommender:
    def __init__(self, retriever):
        self.curr_recommendations = {}
        self.recommended = {}
        self.retriever = retriever
        self.rand_seed = 5

    def _new(self, username, material):
      if username not in self.curr_recommendations:
          return True
      for row in self.curr_recommendations[username]:
        if row['id'] == material['id'] and row['type'] == material['type']:
          return False
      return True

    def _match_tags(self, username, material, interest):
        score = 0.7
        for tag in material['tags']:
            if cosine_similarity(np.array(self.retriever.encode([tag])),
                                 np.array(self.retriever.encode([interest]))) > score:
                if self._new(username, material):
                    #print('hi')
                    if username in self.curr_recommendations:
                        self.curr_recommendations[username].append(material)
                        self.recommended[username].append(False)
                    else: 
                        self.curr_recommendations[username] = [material]
                        self.recommended[username] = [False]

    def generate_recommendations(self, username, interests, new_material):
        for interest in interests:
            for i, material in new_material.iterrows():
                self._match_tags(username, material, interest)

    def make_recommendation(self, username, name):
        if len(self.curr_recommendations[username]) == 0:
            return ""
        to_consider = [idx for idx, value in enumerate(self.recommended[username]) if value == False]
        if len(to_consider) == 0:
            return ""
        index = random.choice(list(range(0, len(to_consider))))
        index = self.recommended[username][index]
        #while self.recommended[index] == True:
        #    index = random.choice(list(range(0, len(self.curr_recommendations))))
        recommendation = "Hey " + name + "! This " + self.curr_recommendations[username][index][
            'type'].lower() + " about " + ', '.join(
            self.curr_recommendations[username][index]['tags']).lower() + " was posted recently by " + \
                         self.curr_recommendations[username][index][
                             'author'].lower() + " on the catalogue. You may wanna check it out! It is titled " + \
                         self.curr_recommendations[username][index]['title'].lower() + ". Cheers, Janet"
        # self.curr_recommendations.remove(self.curr_recommendations[index])
        self.recommended[username][index] = True
        return recommendation