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# %%
import json
import pickle as pk
import random
import threading
from datetime import datetime
import numpy as np
from gensim.models import KeyedVectors
from sentence_transformers import SentenceTransformer
from display import display_words
from pistas import curiosity, hint
from seguimiento import calculate_moving_average, calculate_tendency_slope
# %%
model = KeyedVectors(768)
model_st = SentenceTransformer(
"sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
)
# file_path = "word_embeddings_mpnet.pth"
# embeddings_dict = torch.load(file_path)
embeddings_dict = {}
config_file_path = "config/lang.json"
secret_file_path = "config/secret.json"
class DictWrapper:
def __init__(self, data_dict):
self.__dict__.update(data_dict)
with open(config_file_path, "r") as file:
# Load JSON from the file into a dictionary
Config_full = json.load(file)
with open(secret_file_path, "r") as file:
# Load JSON from the file into a dictionary
secret = json.load(file)
lang = 0
if lang == 0:
Config = DictWrapper(Config_full["SPA"]["Game"]) # type: ignore
secret_dict = secret["SPA"]
elif lang == 1:
Config = DictWrapper(Config_full["ENG"]["Game"]) # type: ignore
secret_dict = secret["ENG"]
else:
Config = DictWrapper(Config_full["SPA"]["Game"]) # type: ignore
secret_dict = secret["SPA"]
with open("ranking.txt", "w+") as file:
file.write("---------------------------")
# %%
pca = pk.load(open("pca_mpnet.pkl", "rb"))
print(Config.Difficulty_presentation_Full) # type: ignore
# difficulty = int(input("Dificultad: "))
difficulty = int(input(Config.Difficulty + ": ")) # type: ignore
# with open(file_path, "r") as file:
# secret_list = file.readlines()
# Write a function
# Optional: Remove newline characters from each element in the list
secret_list = secret_dict["basic"] if difficulty <= 2 else secret_dict["advanced"]
secret = secret_list.pop(random.randint(0, len(secret_list) - 1))
secret = secret.lower()
words = [Config.secret_word] # type: ignore
scores = [10]
# %%
# if word not in embeddings_dict.keys():
embeddings_dict[secret] = model_st.encode(secret, convert_to_tensor=True)
model.add_vector(secret, embeddings_dict[secret].tolist())
word_vect = [embeddings_dict[secret].tolist()]
# model.add_vector(secret, embedding.tolist())
thread = threading.Thread(
target=display_words, args=(words, pca.transform(word_vect), scores, -1)
)
# Start the thread
thread.start()
def preproc_vectors(words, word_vect, scores, repeated):
ascending_indices = np.argsort(scores)
# Reverse the order to get descending indices
descending_indices = list(ascending_indices[::-1])
ranking_data = []
k = len(words) - 1
if repeated != -1:
k = repeated
ranking_data.append(["#" + str(k), words[k], scores[k]])
ranking_data.append("---------------------------")
for i in descending_indices: # type: ignore
if i == 0:
continue
ranking_data.append(["#" + str(i), words[i], scores[i]])
with open("ranking.txt", "w+") as file:
for item in ranking_data:
file.write("%s\n" % item)
if len(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 = [words[i] for i in descending_indices]
displayvect_display = pca.transform([word_vect[i] for i in descending_indices])
scores_display = [scores[i] for i in descending_indices]
bold = descending_indices.index(k)
else:
words_display = words
displayvect_display = pca.transform(word_vect)
scores_display = scores
bold = k
return (
words_display,
displayvect_display,
scores_display,
bold,
)
# Example usage:
win = False
n = 0
recent_hint = 0
f_dev_avg = 0
last_hint = -1
if difficulty == 1:
n = 3
while win == False:
word = input(Config.New_word).lower() # type: ignore
if word == "give_up":
break
if word in words:
repeated = words.index(word)
else:
repeated = -1
words.append(word)
thread.join()
# if word not in embeddings_dict.keys():
embedding = model_st.encode(word, convert_to_tensor=True)
embeddings_dict[word] = embedding
# model.add_vector(word, embeddings_dict[word].tolist())
model.add_vector(word, embedding.tolist()) # type: ignore
# model.add_vector(word, embedding.tolist())
if repeated == -1:
word_vect.append(embeddings_dict[word].tolist())
score = round(model.similarity(secret, word) * 10, 2)
if repeated == -1:
scores.append(score) # type: ignore
#
# score = round(score * 10, 2)
# %%
if score <= 2.5:
feedback = Config.Feedback_0 + str(score) # type: ignore
elif score > 2.5 and score <= 4.0:
feedback = Config.Feedback_1 + str(score) # type: ignore
elif score > 4.0 and score <= 6.0:
feedback = Config.Feedback_2 + str(score) # type: ignore
elif score > 6.0 and score <= 7.5:
feedback = Config.Feedback_3 + str(score) # type: ignore
elif score > 7.5 and score <= 8.0:
feedback = Config.Feedback_4 + str(score) # type: ignore
elif score > 8.0 and score < 10.0:
feedback = Config.Feedback_5 + str(score) # type: ignore
else:
win = True
feedback = Config.Feedback_8 # type: ignore
words[0] = secret
words.pop(len(words) - 1)
word_vect.pop(len(word_vect) - 1)
scores.pop(len(scores) - 1)
# print(model.most_similar(secret, topn=20))
print(feedback)
if score > scores[len(scores) - 2] and win == False:
print(Config.Feedback_6) # type: ignore
elif score < scores[len(scores) - 2] and win == False:
print(Config.Feedback_7) # type: ignore
if difficulty != 4:
mov_avg = calculate_moving_average(scores[1:], 5)
# print (mov_avg)
if len(mov_avg) > 1 and win == False:
f_dev = calculate_tendency_slope(mov_avg)
# print(f_dev[len(f_dev) - 3 :])
f_dev_avg = calculate_moving_average(f_dev, 3)
# print(f_dev_avg[len(f_dev_avg) - 3 :])
# print(f_dev_avg)
if f_dev_avg[len(f_dev_avg) - 1] < 0 and recent_hint == 0:
i = random.randint(0, len(Config.hint_intro) - 1) # type: ignore
print("\n")
print(Config.hint_intro[i]) # type: ignore
hint_text, n, last_hint = hint(
secret,
n,
model_st,
last_hint,
lang,
DictWrapper(Config_full["SPA"]["Hint"])
if lang == 0
else DictWrapper(Config_full["ENG"]["Hint"]),
)
print(hint_text)
recent_hint = 3
if recent_hint != 0:
recent_hint -= 1
(
words_display,
displayvect_display,
scores_display,
bold_display,
) = preproc_vectors(words, word_vect, scores, repeated)
if win:
bold_display = 0
thread = threading.Thread(
target=display_words,
args=(words_display, displayvect_display, scores_display, bold_display),
)
# Start the thread
thread.start()
if win == False:
print(Config.Feedback_9 + secret) # type: ignore
print(Config.Feedback_10) # type: ignore
curiosity = curiosity(
secret,
DictWrapper(Config_full["SPA"]["Hint"])
if lang == 0
else DictWrapper(Config_full["ENG"]["Hint"]),
)
print(curiosity)
with open("ranking.txt", "r") as original_file:
file_content = original_file.readlines()
new_file_name = secret + "_" + str(datetime.now())
with open("data/" + new_file_name, "w") as new_file:
new_file.writelines(file_content[2:])
thread.join()
exit()
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