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# %%
import json
import pickle as pk
import random
import threading
from datetime import datetime
import time
import gradio as gr
import numpy as np
from display import display_words
from gensim.models import KeyedVectors
from pistas import curiosity, hint
from seguimiento import calculate_moving_average, calculate_tendency_slope
from sentence_transformers import SentenceTransformer
class Semantrix:
model = KeyedVectors(768)
model_st = SentenceTransformer(
"sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
)
config_file_path = "config/lang.json"
secret_file_path = "config/secret.json"
data_path = "data/"
class DictWrapper:
def __init__(self, data_dict):
self.__dict__.update(data_dict)
def __init__(self):
self.embeddings_dict = {}
with open(self.config_file_path, "r") as file:
self.Config_full = json.load(file)
with open(self.secret_file_path, "r") as file:
self.secret = json.load(file)
self.lang = 0
if self.lang == 0:
self.Config = self.DictWrapper(self.Config_full["SPA"]["Game"])
self.secret_dict = self.secret["SPA"]
elif self.lang == 1:
self.Config = self.DictWrapper(self.Config_full["ENG"]["Game"])
secret_dict = self.secret["ENG"]
else:
self.Config = self.DictWrapper(self.Config_full["SPA"]["Game"])
self.secret_dict = self.secret["SPA"]
with open(self.data_path + "ranking.txt", "w+") as file:
file.write("---------------------------")
# pca = pk.load(open("pca_mpnet.pkl", "rb"))
# print(Config.Difficulty_presentation_Full)
# difficulty = int(input(Config.Difficulty + ": "))
def prepare_game(self,difficulty):
# global secret, secret_list, words, scores, word_vect, thread, win, n, recent_hint, f_dev_avg, last_hint
self.secret_list = self.secret_dict["basic"] if difficulty <= 2 else self.secret_dict["advanced"]
self.secret = self.secret_list.pop(random.randint(0, len(self.secret_list) - 1))
self.secret = self.secret.lower()
self.secret = "amigo"
self.words = [self.Config.secret_word]
self.scores = [10]
if self.secret not in self.embeddings_dict.keys():
self.embeddings_dict[self.secret] = self.model_st.encode(self.secret, convert_to_tensor=True)
self.model.add_vector(self.secret, self.embeddings_dict[self.secret].tolist())
self.word_vect = [self.embeddings_dict[self.secret].tolist()]
# thread = threading.Thread(
# target=display_words, args=(words, pca.transform(word_vect), scores, -1)
# )
# thread.start()
self.win = False
self.n = 0
self.recent_hint = 0
self.f_dev_avg = 0
self.last_hint = -1
self.difficulty = difficulty
if self.difficulty == 1:
self.n = 3
def preproc_vectors(self,repeated):
ascending_indices = np.argsort(self.scores)
descending_indices = list(ascending_indices[::-1])
ranking_data = []
k = len(self.words) - 1
if repeated != -1:
k = repeated
ranking_data.append(["#" + str(k), self.words[k], self.scores[k]])
ranking_data.append("---------------------------")
for i in descending_indices:
if i == 0:
continue
ranking_data.append(["#" + str(i), self.words[i], self.scores[i]])
with open(self.data_path + "ranking.txt", "w+") as file:
for item in ranking_data:
file.write("%s\n" % item)
if len(self.words) > 11:
if k in descending_indices[:11]:
descending_indices = descending_indices[:11]
else:
descending_indices = descending_indices[:11]
descending_indices.append(k)
words_display = [self.words[i] for i in descending_indices]
# displayvect_display = pca.transform([word_vect[i] for i in descending_indices])
scores_display = [self.scores[i] for i in descending_indices]
bold = descending_indices.index(k)
else:
words_display = self.words
# displayvect_display = pca.transform(word_vect)
scores_display = self.scores
bold = k
return (
words_display,
# displayvect_display,
scores_display,
bold,
)
def play_game(self,word):
# global win, n, recent_hint, f_dev_avg, last_hint, words, word_vect, scores, thread
word = word.lower()
if word == "give_up":
text = (
"[win]"
+
self.Config.Feedback_9
+ self.secret
+ "\n\n"
+ self.Config.Feedback_10
)
return text
if word in self.words:
repeated = self.words.index(word)
else:
repeated = -1
self.words.append(word)
# thread.join()
if word not in self.embeddings_dict.keys():
embedding = self.model_st.encode(word, convert_to_tensor=True)
self.embeddings_dict[word] = embedding
self.model.add_vector(word, embedding.tolist())
if repeated == -1:
self.word_vect.append(self.embeddings_dict[word].tolist())
score = round(self.model.similarity(self.secret, word) * 10, 2)
if repeated == -1:
self.scores.append(score)
if score <= 2.5:
feedback = self.Config.Feedback_0 + str(score)
elif score > 2.5 and score <= 4.0:
feedback = self.Config.Feedback_1 + str(score)
elif score > 4.0 and score <= 6.0:
feedback = self.Config.Feedback_2 + str(score)
elif score > 6.0 and score <= 7.5:
feedback = self.Config.Feedback_3 + str(score)
elif score > 7.5 and score <= 8.0:
feedback = self.Config.Feedback_4 + str(score)
elif score > 8.0 and score < 10.0:
feedback = self.Config.Feedback_5 + str(score)
else:
self.win = True
feedback = "[win]" + self.Config.Feedback_8
self.words[0] = self.secret
self.words.pop(len(self.words) - 1)
self.word_vect.pop(len(self.word_vect) - 1)
self.scores.pop(len(self.scores) - 1)
if score > self.scores[len(self.scores) - 2] and self.win == False:
feedback += "\n" + self.Config.Feedback_6
elif score < self.scores[len(self.scores) - 2] and self.win == False:
feedback += "\n" + self.Config.Feedback_7
if self.difficulty != 4:
mov_avg = calculate_moving_average(self.scores[1:], 5)
if len(mov_avg) > 1 and self.win == False:
f_dev = calculate_tendency_slope(mov_avg)
f_dev_avg = calculate_moving_average(f_dev, 3)
if f_dev_avg[len(f_dev_avg) - 1] < 0 and self.recent_hint == 0:
i = random.randint(0, len(self.Config.hint_intro) - 1)
feedback += "\n\n[hint]" + self.Config.hint_intro[i]
hint_text, self.n, self.last_hint = hint(
self.secret,
self.n,
self.model_st,
self.last_hint,
self.lang,
(
self.DictWrapper(self.Config_full["SPA"]["Hint"])
if self.lang == 0
else self.DictWrapper(self.Config_full["ENG"]["Hint"])
),
)
feedback += "\n" + hint_text
self.recent_hint = 3
if self.recent_hint != 0:
self.recent_hint -= 1
(
words_display,
scores_display,
bold_display,
) = self.preproc_vectors(repeated)
feedback += "[rank]" + open(self.data_path + "ranking.txt", "r").read()
if self.win:
bold_display = 0
# thread = threading.Thread(
# target=display_words,
# args=(words_display, displayvect_display, scores_display, bold_display),
# )
# thread.start()
if self.win:
# feedback += "\nCongratulations! You guessed the secret word."
with open(self.data_path + "ranking.txt", "r") as original_file:
file_content = original_file.readlines()
new_file_name = self.secret + "_" + str(datetime.now())
with open(self.data_path + "plays/" + new_file_name, "w+") as new_file:
new_file.writelines(file_content[2:])
return feedback
def curiosity(self):
feedback = curiosity(self.secret, self.DictWrapper(self.Config_full["SPA"]["Hint"]))
return feedback
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