feat: adjust login page
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parent
cf7091f83d
commit
7733fe7dd6
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@ -1,10 +1,15 @@
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from flask import jsonify, request, current_app
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from pydantic import ValidationError
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from models.login.login_response import UserResponseModel
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from schemas.basic_response_schema import ResponseSchema
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from schemas.google_login_schema import GoogleLoginSchema
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from schemas import LoginSchema
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from services import UserService, AuthService
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from exception import AuthException
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from mapper import UserMapper
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import logging
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logging = logging.getLogger(__name__)
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class AuthController:
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@ -20,7 +25,8 @@ class AuthController:
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return (
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jsonify(
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ResponseSchema(
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message="Register success", data=response
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message="Register success",
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data=UserMapper.user_entity_to_response(response),
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).model_dump()
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),
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200,
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@ -34,7 +40,7 @@ class AuthController:
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current_app.logger.error(
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f"Error during Google login: {str(e)}", exc_info=True
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)
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response = ResponseSchema(
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response = ResponseSchema(
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message="Internal server error", data=None, meta=None
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)
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return jsonify(response.model_dump()), 500
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@ -59,7 +65,7 @@ class AuthController:
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response = ResponseSchema(
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message="Login successful",
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data=user_info,
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data=UserMapper.user_entity_to_response(user_info),
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meta=None,
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)
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return jsonify(response.model_dump()), 200
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@ -4,10 +4,15 @@ from configs import Config, LoggerConfig
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from flask import Flask
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from blueprints import auth_blueprint, user_blueprint
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from database import init_db
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import logging
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def createApp() -> Flask:
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app = Flask(__name__)
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
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)
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app.config.from_object(Config)
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LoggerConfig.init_logger(app)
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@ -1,6 +1,6 @@
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from datetime import datetime
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from typing import Dict, Optional
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from models import UserEntity
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from models import UserEntity, UserResponseModel
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from schemas import RegisterSchema
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@ -39,3 +39,18 @@ class UserMapper:
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updated_at=datetime.now(),
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verification_token=None,
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)
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@staticmethod
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def user_entity_to_response(user: UserEntity) -> UserResponseModel:
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return UserResponseModel(
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_id=str(user._id) if user._id else None,
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google_id=user.google_id,
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email=user.email,
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name=user.name,
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birth_date=user.birth_date,
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pic_url=user.pic_url,
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phone=user.phone,
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locale=user.locale,
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# created_at=user.created_at,
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# updated_at=user.updated_at,
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)
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@ -1,4 +1,5 @@
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# app/models/__init__.py
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from .entities import UserEntity
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from .login import UserResponseModel
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__all__ = ["UserEntity", "UserDTO"]
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__all__ = ["UserEntity", "UserDTO", "UserResponseModel"]
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@ -0,0 +1 @@
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from .login_response import UserResponseModel
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@ -0,0 +1,20 @@
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from pydantic import BaseModel, EmailStr, Field
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from typing import Optional
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from datetime import datetime
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class UserResponseModel(BaseModel):
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id: Optional[str] = Field(alias="_id")
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google_id: Optional[str] = None
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email: EmailStr
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name: str
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birth_date: Optional[datetime] = None
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pic_url: Optional[str] = None
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phone: Optional[str] = None
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locale: str
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class Config:
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allow_population_by_field_name = True
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json_encoders = {
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datetime: lambda v: v.isoformat(),
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}
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@ -37,8 +37,10 @@ class AuthService:
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def login(self, data: LoginSchema):
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current_app.logger.info(f"request data: {data}")
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user_data = self.user_repository.get_user_by_email(data.email)
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current_app.logger.info(f"user_data: {user_data}")
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if user_data == None:
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return None
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@ -1,53 +0,0 @@
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import numpy as np
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class LSTM:
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def __init__(self, input_dim, hidden_dim):
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self.input_dim = input_dim
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self.hidden_dim = hidden_dim
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self.Wf = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
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self.Wi = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
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self.Wc = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
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self.Wo = np.random.randn(hidden_dim, input_dim + hidden_dim) * 0.01
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self.bf = np.zeros((hidden_dim, 1))
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self.bi = np.zeros((hidden_dim, 1))
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self.bc = np.zeros((hidden_dim, 1))
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self.bo = np.zeros((hidden_dim, 1))
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self.h = np.zeros((hidden_dim, 1))
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self.c = np.zeros((hidden_dim, 1))
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def sigmoid(self, x):
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return 1 / (1 + np.exp(-x))
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def tanh(self, x):
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return np.tanh(x)
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def forward(self, x_t):
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combined = np.vstack((self.h, x_t))
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f_t = self.sigmoid(np.dot(self.Wf, combined) + self.bf)
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i_t = self.sigmoid(np.dot(self.Wi, combined) + self.bi)
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C_tilde_t = self.tanh(np.dot(self.Wc, combined) + self.bc)
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self.c = f_t * self.c + i_t * C_tilde_t
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o_t = self.sigmoid(np.dot(self.Wo, combined) + self.bo)
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self.h = o_t * self.tanh(self.c)
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return self.h
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def backward(self, x_t, h_t, y_t, learning_rate):
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# Your backward pass implementation here
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pass
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def train(self, X, y, num_epochs, learning_rate):
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for epoch in range(num_epochs):
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for i in range(len(X)):
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x_t = X[i]
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y_t = y[i]
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# Forward pass
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h_t = self.forward(x_t)
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# Calculate loss and perform backward pass
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loss = np.mean((h_t - y_t) ** 2) # Example loss
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self.backward(x_t, h_t, y_t, learning_rate)
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if i % 100 == 0: # Print loss every 100 samples
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print(f"Epoch {epoch}, Sample {i}, Loss: {loss}")
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@ -0,0 +1,58 @@
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from keras.models import load_model
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import pickle
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class LSTMService:
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def predict(self, input_data, maxlen=50):
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with open("QC/tokenizers.pkl", "rb") as f:
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tokenizers = pickle.load(f)
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model = load_model("QC/lstm_qg.keras")
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tok_token = tokenizers["token"]
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tok_ner = tokenizers["ner"]
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tok_srl = tokenizers["srl"]
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tok_q = tokenizers["question"]
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tok_a = tokenizers["answer"]
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tok_type = tokenizers["type"]
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# Prepare input
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tokens = input_data["tokens"]
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ner = input_data["ner"]
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srl = input_data["srl"]
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x_tok = pad_sequences(
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[tok_token.texts_to_sequences([tokens])[0]], maxlen=maxlen, padding="post"
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)
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x_ner = pad_sequences(
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[tok_ner.texts_to_sequences([ner])[0]], maxlen=maxlen, padding="post"
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)
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x_srl = pad_sequences(
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[tok_srl.texts_to_sequences([srl])[0]], maxlen=maxlen, padding="post"
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)
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# Predict
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pred_q, pred_a, pred_type = model.predict([x_tok, x_ner, x_srl])
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pred_q_ids = np.argmax(pred_q[0], axis=-1)
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pred_a_ids = np.argmax(pred_a[0], axis=-1)
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pred_type_id = np.argmax(pred_type[0])
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# Decode
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index2word_q = {v: k for k, v in tok_q.word_index.items()}
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index2word_a = {v: k for k, v in tok_a.word_index.items()}
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index2word_q[0] = "<PAD>"
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index2word_a[0] = "<PAD>"
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decoded_q = [index2word_q[i] for i in pred_q_ids if i != 0]
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decoded_a = [index2word_a[i] for i in pred_a_ids if i != 0]
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index2type = {v - 1: k for k, v in tok_type.word_index.items()}
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decoded_type = index2type.get(pred_type_id, "unknown")
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return {
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"question": " ".join(decoded_q),
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"answer": " ".join(decoded_a),
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"type": decoded_type,
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}
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@ -1,7 +1,9 @@
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annotated-types==0.7.0
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Authlib==1.5.1
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bcrypt==4.3.0
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bidict==0.23.1
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blinker==1.9.0
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cachetools==5.5.2
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certifi==2025.1.31
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cffi==1.17.1
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charset-normalizer==3.4.1
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@ -16,23 +18,40 @@ Flask-Bcrypt==1.0.1
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Flask-JWT-Extended==4.7.1
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Flask-Login==0.6.3
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Flask-PyMongo==3.0.1
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Flask-SocketIO==5.5.1
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flask-swagger-ui==4.11.1
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google-auth==2.38.0
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google-auth-httplib2==0.2.0
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google-auth-oauthlib==1.2.1
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h11==0.14.0
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httplib2==0.22.0
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idna==3.10
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iniconfig==2.0.0
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itsdangerous==2.2.0
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Jinja2==3.1.6
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MarkupSafe==3.0.2
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numpy==2.1.2
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oauthlib==3.2.2
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packaging==24.2
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pluggy==1.5.0
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pyasn1==0.6.1
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pyasn1_modules==0.4.1
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pycparser==2.22
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pydantic==2.10.6
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pydantic_core==2.27.2
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PyJWT==2.10.1
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pymongo==4.11.2
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pyparsing==3.2.1
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pytest==8.3.4
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python-dotenv==1.0.1
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python-engineio==4.11.2
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python-socketio==5.12.1
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requests==2.32.3
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requests-oauthlib==2.0.0
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rsa==4.9
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simple-websocket==1.1.0
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tomli==2.2.1
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typing_extensions==4.12.2
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urllib3==2.3.0
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Werkzeug==3.1.3
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wsproto==1.2.0
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