def newChat():
new_id = max(collection.distinct("idChat")) + 1
name = str(request.forms.get("name", f"chat{new_id}"))
new_chat = {"id_Chat": new_id, "name": name}
collection.insert_one(new_chat)
print("Chat created")
database, collection = connectCollection('chats', 'chateo')
def chatsWant(x, y):
x = int(x)
y = int(y)
database, collection = connectCollection('chats', 'chateo')
chats_want = []
for a in range(x, y):
chats_want.append(
dumps(collection.find({"idChat": a}, {
"userName": 1,
"text": 1
})))
return chats_want
def createUser():
database, collection = connectCollection('chats', 'users')
name = str(request.forms.get("name"))
ids = collection.distinct("idUser")[-1] + 1
user_new = {"idUser": ids, "userName": name}
takenNames = list(collection.aggregate([{'$project': {'userName': 1}}]))
if user_new['userName'] in [e['userName'] for e in takenNames]:
return {"Error!": "Username already in use"}
else:
user_id = collection.insert_one(user_new)
return {
'NEW USER CREATED!'
'userName': name,
'UserId': int(user_new['idUser'])
}
def recommending_user(userName):
database, collection =connectCollection('chats','chateo')
query = list(collection.find({},{'userName':1,"text":1,'_id':0}))
diccionario = getting_every_sentence(query)
recommendation_dict=dict()
count_vectorizer=CountVectorizer(stop_words='english')
sparse_matrix = count_vectorizer.fit_transform(diccionario.values())
doc_term_matrix = sparse_matrix.todense()
df = pd.DataFrame(doc_term_matrix, columns=count_vectorizer.get_feature_names(), index=diccionario.keys())
similarity_matrix = distance(df, df)
sim_df = pd.DataFrame(similarity_matrix, columns=diccionario.keys(), index=diccionario.keys())
np.fill_diagonal(sim_df.values, 0)
final_matrix=sim_df.idxmax()
recommended = list(sim_df.sort_values(by=userName, ascending = False).index[0:3])
return json.dumps(recommended)
def getChats():
database, collection = connectCollection('chats', 'chateo')
all_chats = dumps(collection.find().distinct("text")[0:])
return all_chats
def getUsers():
database, collection = connectCollection('chats', 'users')
return dumps(collection.find({}).distinct("userName"))
def getAllUsers():
database, collection = connectCollection('chats', 'chateo')
"""Returns all users"""
return dumps(collection.find({}, {'userName': 1, "text": 1, '_id': 0}))
import dns
from bson.json_util import dumps, loads
import re
from sklearn.metrics.pairwise import cosine_similarity as distance
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from bottle import get, run
import numpy as np
from connect import connectCollection
import json
load_dotenv()
url=os.getenv('URLMONGO')
client = MongoClient(url)
database, collection =connectCollection('chats','chateo')
def getting_every_sentence(lista):
users_dict=dict()
for dicc in lista:
if dicc['userName'] not in users_dict:
users_dict[dicc['userName']]=dicc['text']
else:
users_dict[dicc['userName']]+=' ' +dicc['text']
for e in users_dict:
users_dict[e]=re.sub(r"[^a-zA-Z0-9]+", ' ', users_dict[e])
return users_dict
@get('/recommendation/user=<userName>')
def recommending_user(userName):