WeMeet Place

https://github.com/ymjo140/wemeet-project
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WeMeet Place is a location-based social platform that combines AI utility and gamification to...
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### Creating Masked Arrays with NumPy

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Demonstrates the creation of masked arrays using both the old numpy.core.ma package and the new maskedarray package. It shows how masked values are represented and how the mask is applied.

```python
>>> import numpy.core.ma as old_ma
>>> import maskedarray as new_ma
>>> x = old_ma.array([1,2,3,4,5], mask=[0,0,1,0,0])
>>> x
array(data =
 [     1      2 999999      4      5],
      mask =
 [False False True False False],
      fill_value=999999)
>>> y = new_ma.array([1,2,3,4,5], mask=[0,0,1,0,0])
>>> y
array(data = [1 2 -- 4 5],
      mask = [False False True False False],
      fill_value=999999)
```

--------------------------------

### Implementing _SLDateTime for pysqlite

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sqlalchemy/dialects/type_migration_guidelines.txt

This example shows how pysqlite handles bind/result processing for the DateTime type by creating a private _SLDateTime class that subclasses types.DateTime. This ensures consistent handling across all DateTime subclasses.

```python
from sqlalchemy import types

class _SLDateTime(types.DateTime):
    """Pysqlite's custom DateTime processing."""
    pass

# In pysqlite dialect's base.py:
# pysqlite_dialect.colspecs.update({
#    types.DateTime: _SLDateTime,
# })

```

--------------------------------

### GPL Notice for Interactive Mode

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/scipy-1.16.3.dist-info/LICENSE.txt

This is a standard notice displayed when a program under the GNU General Public License starts in interactive mode. It includes copyright information, warranty disclaimers, and redistribution terms. Users can typically access full license details via specific commands (e.g., 'show w', 'show c').

```plaintext
<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
```

--------------------------------

### Get Current User Profile API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Retrieves detailed information about the currently authenticated user, including their preferences, wallet balance, avatar details, and past review history.

```APIDOC
## GET /api/users/me

### Description
Fetches the profile details of the authenticated user, including preferences, location, wallet balance, avatar status, and review history.

### Method
GET

### Endpoint
/api/users/me

### Parameters
#### Headers
- **Authorization** (string) - Required - Bearer token for authentication (e.g., `Bearer YOUR_JWT_TOKEN`).

### Response
#### Success Response (200)
- **id** (integer) - User's unique identifier.
- **name** (string) - User's name.
- **email** (string) - User's email address.
- **preferences** (object) - User's taste preferences.
  - **foods** (array of strings) - Preferred food types.
  - **disliked_foods** (array of strings) - Disliked food types.
  - **vibes** (array of strings) - Preferred atmosphere types.
  - **avg_spend** (integer) - Average spending per visit.
  - **tag_weights** (object) - Weights assigned to different tags (e.g., "맛집": 2.5).
- **location** (object) - User's current or default location.
  - **lat** (float) - Latitude.
  - **lng** (float) - Longitude.
- **wallet_balance** (integer) - Current balance in the user's wallet.
- **avatar** (object) - User's avatar details.
  - **equipped** (object) - Currently equipped avatar items.
  - **inventory** (array of strings) - Items available in the avatar inventory.
  - **level** (integer) - User's avatar level.
- **favorites** (array of objects) - List of user's favorite venues.
- **reviews** (array of objects) - List of reviews submitted by the user.

#### Response Example
```json
{
    "id": 1,
    "name": "John Doe",
    "email": "user@example.com",
    "preferences": {
        "foods": ["한식", "일식"],
        "vibes": ["조용한", "분위기"],
        "avg_spend": 20000,
        "tag_weights": {"맛집": 2.5, "가성비": 1.0}
    },
    "location": {"lat": 37.566, "lng": 126.978},
    "wallet_balance": 5000,
    "avatar": {
        "equipped": {"body": "body_basic", "hair": "hair_01"},
        "inventory": ["body_basic", "hair_01", "top_tshirt"],
        "level": 1
    },
    "favorites": [{"id": 123, "name": "맛집 이름"}],
    "reviews": []
}
```
```

--------------------------------

### Load LFW Dataset with Color Channels using scikit-learn

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/lfw.rst

This example illustrates how to fetch LFW data with RGB color channels included by setting the `color` parameter to `True`. This modifies the shape of the resulting arrays to include the color dimension, which is essential for certain color-based image processing tasks.

```python
from sklearn.datasets import fetch_lfw_pairs
# For fetch_lfw_pairs
lfw_pairs_color = fetch_lfw_pairs(color=True)
# The shape will be (2200, 2, 62, 47, 3)

from sklearn.datasets import fetch_lfw_people
lfw_people_color = fetch_lfw_people(color=True)
# The shape will be (1288, 50, 37, 3)
```

--------------------------------

### List Communities API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Retrieves a list of all available communities via a GET request to the /api/communities endpoint. The response includes details about each community and its current members.

```python
# GET /api/communities
communities = requests.get("http://localhost:8000/api/communities")

# Response: List of all communities with member details
```

--------------------------------

### Comparing Masked Arrays

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Illustrates the comparison between a masked array created with the older numpy.core.ma package and one created with the new maskedarray package. It shows how the comparison results in a masked array, preserving the mask information.

```python
>>> x==y
array(data =
 [True True True True True],
      mask =
 [False False True False False],
      fill_value=?)
```

--------------------------------

### Masked Array Division: With Prefilling

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Shows an optimized approach for dividing masked arrays by pre-filling the arrays while considering the domain mask. This method helps avoid division by zero and floating-point exceptions, ensuring more predictable results.

```python
  d1 = x._data.copy() # d1 = array([1., 2., 3., 4.])
  d2 = y._data.copy() # array([-1.,  0.,  1.,  2.])
  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
  numpy.putmask(d2, dm, 1) # d2 = array([-1.,  1.,  1.,  2.])
  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m = array([True,False,False,True])
  result = (d1/d2).view(ma.MaskedArray) # masked_array([-1. 0., 3., 2.])
  result._mask = numpy.logical_or(m, dm)
```

--------------------------------

### Retrieve Calendar Events API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Retrieves all calendar events for the authenticated user via a GET request to the /api/events endpoint. This endpoint returns a list of scheduled events.

```python
# GET /api/events
all_events = requests.get("http://localhost:8000/api/events")
```

--------------------------------

### RCV1 Dataset Sample IDs

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/rcv1.rst

This code example shows how to access the first three sample IDs from the RCV1 dataset loaded with scikit-learn. The `sample_id` attribute provides unique identifiers for each data sample.

```python
rcv1.sample_id[:3]
```

--------------------------------

### Get Current User Profile API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Retrieves detailed information about the currently authenticated user. This includes preferences, wallet balance, avatar equipment, favorite places, and review history. Requires a JWT token for authorization.

```python
import requests

headers = {"Authorization": "Bearer YOUR_JWT_TOKEN"}
response = requests.get("http://localhost:8000/api/users/me", headers=headers)

# Response
# {
#     "id": 1,
#     "name": "John Doe",
#     "email": "user@example.com",
#     "preferences": {
#         "foods": ["한식", "일식"],
#         "vibes": ["조용한", "분위기"],
#         "avg_spend": 20000,
#         "tag_weights": {"맛집": 2.5, "가성비": 1.0}
#     },
#     "location": {"lat": 37.566, "lng": 126.978},
#     "wallet_balance": 5000,
#     "avatar": {
#         "equipped": {"body": "body_basic", "hair": "hair_01"},
#         "inventory": ["body_basic", "hair_01", "top_tshirt"],
#         "level": 1
#     },
#     "favorites": [{"id": 123, "name": "맛집 이름"}],
#     "reviews": []
# }
```

--------------------------------

### Masked Array Division: Default Behavior

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Demonstrates the default behavior of dividing two masked arrays in Python using numpy and maskedarray. It illustrates the steps involved, including filling, masking, and the potential for division by zero resulting in 'inf'.

```python
  import numpy
  import maskedarray as ma
  x = ma.array([1,2,3,4],mask=[1,0,0,0], dtype=numpy.float64)
  y = ma.array([-1,0,1,2], mask=[0,0,0,1], dtype=numpy.float64)

  d1 = x.filled(0) # d1 = array([0., 2., 3., 4.])
  d2 = y.filled(1) # array([-1.,  0.,  1.,  1.])
  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m = array([True,False,False,True])
  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
  result = (d1/d2).view(ma.MaskedArray) # masked_array([-0. inf, 3., 4.])
  result._mask = numpy.logical_or(m, dm)
```

--------------------------------

### User Registration API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Creates a new user account with email, password, and name. Returns a success message upon successful creation.

```APIDOC
## POST /api/register

### Description
Creates a new user account with the provided email, password, and name.

### Method
POST

### Endpoint
/api/register

### Parameters
#### Request Body
- **email** (string) - Required - The user's email address.
- **password** (string) - Required - The user's password.
- **name** (string) - Required - The user's name.

### Request Example
```json
{
    "email": "user@example.com",
    "password": "securepassword123",
    "name": "John Doe"
}
```

### Response
#### Success Response (200)
- **message** (string) - Indicates that the user was created successfully.

#### Response Example
```json
{
    "message": "User created"
}
```
```

--------------------------------

### User Registration API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Handles the creation of new user accounts. Requires email, password, and name. Returns a success message upon successful user creation.

```python
import requests

response = requests.post(
    "http://localhost:8000/api/register",
    json={
        "email": "user@example.com",
        "password": "securepassword123",
        "name": "John Doe"
    }
)

# Response
# {"message": "User created"}
```

--------------------------------

### Configuring Matplotlib for Masked Arrays

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

This snippet shows how to configure Matplotlib to use an external maskedarray module instead of numpy.ma. This is done by modifying the matplotlibrc file or using a command-line option.

```text
  #maskedarray : False       # True to use external maskedarray module
                             # instead of numpy.ma; this is a temporary #
                             # setting for testing maskedarray.

  # Uncomment and set to True to select maskedarray everywhere.
  # Alternatively, you can test a script with maskedarray by using a
  # command-line option, e.g.::
  #
  #   python simple_plot.py --maskedarray
```

--------------------------------

### Extracting Mask from Masked Arrays

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Shows how to retrieve the mask associated with a masked array using functions from both the old numpy.core.ma and the new maskedarray packages. It highlights the differences in how the mask is represented.

```python
>>> old_ma.getmask(x) == new_ma.getmask(x)
array([True, True, True, True, True])
>>> old_ma.getmask(y) == new_ma.getmask(y)
array([True, True, False, True, True])
>>> old_ma.getmask(y)
False
```

--------------------------------

### AI Recommendation Algorithm

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Information about the AI recommendation engine that learns from user behavior to provide personalized venue suggestions.

```APIDOC
## AI Recommendation Algorithm

### Description
The core recommendation engine that learns from user behavior and provides personalized venue suggestions using collaborative filtering and preference learning.

### Details
This section describes the underlying AI model responsible for suggesting venues. It utilizes techniques such as collaborative filtering to identify patterns in user preferences and behaviors. The algorithm aims to provide highly personalized recommendations by understanding individual tastes and the collective behavior of similar users.

### Further Information
Specific API endpoints for interacting with the recommendation algorithm are not detailed here but would typically involve requesting recommendations based on user profiles, past activities, or contextual information (e.g., time of day, location).

```

--------------------------------

### Kakao OAuth Login API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Handles user authentication via Kakao. It exchanges the provided authorization code for a Kakao access token and then either creates a new user account or retrieves an existing one.

```APIDOC
## POST /api/auth/kakao

### Description
Facilitates social login using Kakao. Exchanges a Kakao authorization code to obtain user tokens and links them to a WeMeet user account, creating one if it doesn't exist.

### Method
POST

### Endpoint
/api/auth/kakao

### Parameters
#### Request Body
- **code** (string) - Required - The authorization code obtained from Kakao after user consent.

### Request Example
```json
{
    "code": "kakao_authorization_code_from_redirect"
}
```

### Response
#### Success Response (200)
- **access_token** (string) - JWT token for authentication within WeMeet.
- **token_type** (string) - Type of token, typically 'bearer'.
- **user_id** (integer) - The unique identifier for the user in WeMeet.
- **name** (string) - The user's name.

#### Response Example
```json
{
    "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
    "token_type": "bearer",
    "user_id": 5,
    "name": "카카오유저"
}
```
```

--------------------------------

### RCV1 Dataset Target Names

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/rcv1.rst

This snippet demonstrates how to get the first three target names (topics) from the RCV1 dataset loaded via scikit-learn. The `target_names` attribute is a list of strings representing the categories in the dataset.

```python
rcv1.target_names[:3].tolist()  # doctest: +SKIP
```

--------------------------------

### Fetch LFW Pairs Data for Face Verification with scikit-learn

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/lfw.rst

This snippet shows how to load the LFW dataset for face verification tasks using `fetch_lfw_pairs`. It allows loading subsets like 'train', 'test', or '10_folds'. The loaded data includes pairs of images, their corresponding targets (same or different person), and the shape of the data.

```python
from sklearn.datasets import fetch_lfw_pairs
lfw_pairs_train = fetch_lfw_pairs(subset='train')

print(list(lfw_pairs_train.target_names))
print(lfw_pairs_train.pairs.shape)
print(lfw_pairs_train.data.shape)
print(lfw_pairs_train.target.shape)
```

--------------------------------

### Fetch LFW People Data for Face Identification with scikit-learn

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/lfw.rst

This code snippet demonstrates how to use `fetch_lfw_people` from scikit-learn to load the LFW dataset for face identification. It includes parameters for filtering individuals by the number of faces per person and resizing images. The output includes data, target names, and image shapes.

```python
from sklearn.datasets import fetch_lfw_people
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

for name in lfw_people.target_names:
    print(name)

print(lfw_people.data.dtype)
print(lfw_people.data.shape)
print(lfw_people.images.shape)
print(lfw_people.target.shape)
print(list(lfw_people.target[:10]))
```

--------------------------------

### Masked Array Division: Without Filling

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/numpy/ma/README.rst

Illustrates dividing masked arrays without pre-filling. This method is faster but may lead to floating-point exceptions like division by zero, resulting in 'inf' or 'nan' values in the output.

```python
  d1 = x._data # d1 = array([1., 2., 3., 4.])
  d2 = y._data # array([-1.,  0.,  1.,  2.])
  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m = array([True,False,False,True])
  result = (d1/d2).view(ma.MaskedArray) # masked_array([-1. inf, 3., 2.])
  result._mask = numpy.logical_or(m, dm)
```

--------------------------------

### Community Creation and Management

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Allows for the creation of public/private communities for group meetings and retrieval of all existing communities.

```APIDOC
## POST /api/communities

### Description
Creates a new community for group meetings. Communities can be public or private.

### Method
POST

### Endpoint
/api/communities

### Parameters
#### Request Body
- **title** (string) - Required - The title of the community.
- **category** (string) - Required - The category of the community (e.g., "식사").
- **location** (string) - Required - The location of the community's meetings.
- **date_time** (string) - Required - The date and time for the meeting (YYYY-MM-DD HH:MM).
- **max_members** (integer) - Required - The maximum number of members allowed in the community.
- **description** (string) - Optional - A description of the community.
- **tags** (array of strings) - Optional - Tags associated with the community.

### Request Example
```json
{
    "title": "주말 맛집 탐방",
    "category": "식사",
    "location": "강남",
    "date_time": "2025-12-15 18:00",
    "max_members": 6,
    "description": "강남 맛집을 함께 탐방할 모임입니다",
    "tags": ["한식", "맛집투어", "강남"]
}
```

### Response
#### Success Response (201)
- **id** (string) - The unique ID of the created community.
- **host_id** (integer) - The ID of the community host.
- **title** (string) - The title of the community.
- **category** (string) - The category of the community.
- **location** (string) - The location of the community.
- **date_time** (string) - The date and time of the meeting.
- **max_members** (integer) - The maximum number of members.
- **description** (string) - The description of the community.
- **tags** (array of strings) - Tags associated with the community.
- **rating** (float) - The community's rating.
- **current_members** (array of objects) - A list of current members with their details.
  - **id** (integer) - Member's ID.
  - **name** (string) - Member's name.
  - **avatar** (string) - Member's avatar representation.
  - **manner** (float) - Member's manner score.

#### Response Example
```json
{
    "id": "uuid-string",
    "host_id": 1,
    "title": "주말 맛집 탐방",
    "category": "식사",
    "location": "강남",
    "date_time": "2025-12-15 18:00",
    "max_members": 6,
    "description": "강남 맛집을 함께 탐방할 모임입니다",
    "tags": ["한식", "맛집투어", "강남"],
    "rating": 5.0,
    "current_members": [{"id": 1, "name": "Host", "avatar": "👤", "manner": 36.5}]
}
```

## GET /api/communities

### Description
Retrieves a list of all available communities.

### Method
GET

### Endpoint
/api/communities

### Parameters
None

### Response
#### Success Response (200)
- Returns a list of community objects, similar to the response of `POST /api/communities`.

#### Response Example
```json
[
  {
    "id": "uuid-string-1",
    "host_id": 1,
    "title": "주말 맛집 탐방",
    "category": "식사",
    "location": "강남",
    "date_time": "2025-12-15 18:00",
    "max_members": 6,
    "description": "강남 맛집을 함께 탐방할 모임입니다",
    "tags": ["한식", "맛집투어", "강남"],
    "rating": 5.0,
    "current_members": [{"id": 1, "name": "Host", "avatar": "👤", "manner": 36.5}]
  },
  {
    "id": "uuid-string-2",
    "host_id": 2,
    "title": "스터디 그룹",
    "category": "학습",
    "location": "온라인",
    "date_time": "2025-12-10 14:00",
    "max_members": 10,
    "description": "함께 공부할 스터디 그룹",
    "tags": ["공부", "온라인"],
    "rating": 4.5,
    "current_members": [{"id": 2, "name": "Study Leader", "avatar": "🧑‍🏫", "manner": 40.0}]
  }
]
```
```

--------------------------------

### User Login API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Authenticates existing users using email and password. Upon successful authentication, it returns a JWT access token, token type, user ID, and user name.

```python
import requests

response = requests.post(
    "http://localhost:8000/api/login",
    data={
        "username": "user@example.com",  # OAuth2 uses 'username' field
        "password": "securepassword123"
    }
)

# Response
# {
#     "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
#     "token_type": "bearer",
#     "user_id": 1,
#     "name": "John Doe"
# }
```

--------------------------------

### Type Compiler for DDL Generation in SQLAlchemy Dialects

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sqlalchemy/dialects/type_migration_guidelines.txt

Illustrates the use of `compiler.GenericTypeCompiler` subclasses for handling Data Definition Language (DDL) generation in SQLAlchemy dialects. It explains how to override `visit_` methods for both uppercase (generic) and lowercase (interpreted) types to customize DDL output, add arguments, and map types.

```python
class MyTypeCompiler(compiler.GenericTypeCompiler):
    def visit_TIMESTAMP(self, type_):
        # Override to add specific arguments or flags
        return "TIMESTAMP with time zone"

    def visit_large_binary(self, type_):
        # Override to map a generic type to a specific backend type
        return self.visit_BIT(type_)

```

--------------------------------

### User Authentication API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Authenticates a user with their email and password. Upon successful authentication, it returns a JWT access token and essential user information.

```APIDOC
## POST /api/login

### Description
Authenticates a user using their email (as 'username') and password. Returns a JWT access token and user details upon successful login.

### Method
POST

### Endpoint
/api/login

### Parameters
#### Request Body
- **username** (string) - Required - The user's email address.
- **password** (string) - Required - The user's password.

### Request Example
```json
{
    "username": "user@example.com",
    "password": "securepassword123"
}
```

### Response
#### Success Response (200)
- **access_token** (string) - JWT token for authentication.
- **token_type** (string) - Type of token, typically 'bearer'.
- **user_id** (integer) - The unique identifier for the user.
- **name** (string) - The user's name.

#### Response Example
```json
{
    "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
    "token_type": "bearer",
    "user_id": 1,
    "name": "John Doe"
}
```
```

--------------------------------

### Importing PostgreSQL Dialect Types in SQLAlchemy

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sqlalchemy/dialects/type_migration_guidelines.txt

Demonstrates how to import and use specific type objects from the PostgreSQL dialect namespace within SQLAlchemy. This allows for direct use of backend-specific types like INTEGER, VARCHAR, and INET, even though they are ultimately derived from sqlalchemy.types.

```python
from sqlalchemy.dialects.postgresql import base as pg

t = Table('mytable', metadata,
          Column('id', pg.INTEGER, primary_key=True),
          Column('name', pg.VARCHAR(300)),
          Column('inetaddr', pg.INET)
)

```

--------------------------------

### Load RCV1 Dataset using Scikit-learn

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/rcv1.rst

This snippet demonstrates how to load the RCV1 dataset using the `fetch_rcv1` function from scikit-learn. The function downloads the dataset if not already present and returns a dictionary-like object containing the data and associated metadata. Dependencies include the scikit-learn library.

```python
from sklearn.datasets import fetch_rcv1

rcv1 = fetch_rcv1()
```

--------------------------------

### Create Community API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Creates a new public or private community for group meetings using a POST request to the /api/communities endpoint. Requires JWT authentication and accepts community details like title, category, location, date, time, and member limits.

```python
# POST /api/communities
import requests

headers = {"Authorization": "Bearer YOUR_JWT_TOKEN"}
response = requests.post(
    "http://localhost:8000/api/communities",
    headers=headers,
    json={
        "title": "주말 맛집 탐방",
        "category": "식사",
        "location": "강남",
        "date_time": "2025-12-15 18:00",
        "max_members": 6,
        "description": "강남 맛집을 함께 탐방할 모임입니다",
        "tags": ["한식", "맛집투어", "강남"]
    }
)

# Response
# {
#     "id": "uuid-string",
#     "host_id": 1,
#     "title": "주말 맛집 탐방",
#     "category": "식사",
#     "location": "강남",
#     "date_time": "2025-12-15 18:00",
#     "max_members": 6,
#     "description": "강남 맛집을 함께 탐방할 모임입니다",
#     "tags": ["한식", "맛집투어", "강남"],
#     "rating": 5.0,
#     "current_members": [{"id": 1, "name": "Host", "avatar": "👤", "manner": 36.5}]
# }
```

--------------------------------

### Venue Recommendation API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Provides personalized venue recommendations based on user inputs. Supports single-user searches and multi-user midpoint calculations, considering group preferences, purpose, tags, and location.

--------------------------------

### Accept Friend Request

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Accepts a pending friend request using the provided request ID.

```APIDOC
## POST /api/friends/accept

### Description
Accepts a pending friend request.

### Method
POST

### Endpoint
/api/friends/accept

### Parameters
#### Request Body
- **request_id** (integer) - Required - The ID of the friend request to accept.

### Request Example
```json
{
    "request_id": 5
}
```

### Response
#### Success Response (200)
- **message** (string) - Confirmation message indicating the friend request was accepted.

#### Response Example
```json
{
    "message": "친구 수락 완료"
}
```
```

--------------------------------

### Create Calendar Event API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Creates a new calendar event for a user via a POST request to the /api/events endpoint. Requires JWT authentication and accepts user ID, event details including title, date, time, duration, location, and purpose.

```python
# POST /api/events
headers = {"Authorization": "Bearer YOUR_JWT_TOKEN"}
event_response = requests.post(
    "http://localhost:8000/api/events",
    headers=headers,
    json={
        "user_id": 1,
        "title": "팀 회식",
        "date": "2025-12-15",
        "time": "19:00",
        "duration_hours": 2.0,
        "location_name": "강남 맛집",
        "purpose": "식사"
    }
)
```

--------------------------------

### Fetch 20 Newsgroups Dataset (Python)

Source: https://github.com/ymjo140/wemeet-project/blob/main/backend/venv/Lib/site-packages/sklearn/datasets/descr/twenty_newsgroups.rst

Loads the 20 Newsgroups dataset, providing raw text data. It can download and cache the data, and allows specifying subsets ('train' or 'test') and specific categories. Returns data, filenames, target names, and target labels.

```python
from sklearn.datasets import fetch_20newsgroups

# Load the training subset
newsgroups_train = fetch_20newsgroups(subset='train')

# Print target names
from pprint import pprint
pprint(list(newsgroups_train.target_names))
```

```python
from sklearn.datasets import fetch_20newsgroups

# Load specific categories from the training subset
cats = ['alt.atheism', 'sci.space']
newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)

# Print loaded category names and data shapes
print(list(newsgroups_train.target_names))
print(newsgroups_train.filenames.shape)
print(newsgroups_train.target.shape)
```

--------------------------------

### Real-time Chat with WebSocket

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Establishes a WebSocket connection for real-time group chat, supporting text messages, voting cards, and system notifications.

```APIDOC
## WebSocket /ws/room_id/user_id

### Description
Establishes a WebSocket connection for real-time group chat within a specific room. Supports various message types including text, vote cards, and system notifications.

### Method
WebSocket Connection

### Endpoint
`ws://localhost:8000/ws/<room_id>/<user_id>`

### Parameters
#### Path Parameters
- **room_id** (string) - Required - The ID of the chat room.
- **user_id** (integer) - Required - The ID of the user connecting.

### Client-Side Actions
#### Connecting
```javascript
const ws = new WebSocket('ws://localhost:8000/ws/room_id/user_id');

ws.onopen = () => {
    console.log('Connected to chat');
};
```

#### Receiving Messages
```javascript
ws.onmessage = (event) => {
    const message = JSON.parse(event.data);
    console.log('Received:', message);
    // Example message structure:
    // {
    //     "user_id": 1,
    //     "name": "Alice",
    //     "avatar": "👤",
    //     "content": "{\"type\": \"text\", \"text\": \"Hello!\"}",
    //     "type": "text",
    //     "message_id": 123,
    //     "timestamp": "14:30"
    // }
};
```

#### Sending Text Messages
```javascript
ws.send(JSON.stringify({
    type: 'text',
    content: 'Hello everyone!'
}));
```

#### Sending Vote Card (Place Recommendation)
```javascript
ws.send(JSON.stringify({
    type: 'vote_card',
    text: 'How about this place?',
    place: {
        name: '맛집',
        category: 'restaurant',
        tags: ['한식', '분위기']
    }
}));
```

### Server-Side Message Structure (Example)
```json
{
    "user_id": 1,
    "name": "Alice",
    "avatar": "👤",
    "content": "{\"type\": \"text\", \"text\": \"Hello!\"}",
    "type": "text",
    "message_id": 123,
    "timestamp": "14:30"
}
```
```

--------------------------------

### Calendar Event Management

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Provides endpoints for creating, retrieving, updating, and deleting user calendar events, with support for natural language schedule parsing.

```APIDOC
## POST /api/ai/parse-schedule

### Description
Parses a natural language text string to extract schedule details like title, date, time, and location.

### Method
POST

### Endpoint
/api/ai/parse-schedule

### Parameters
#### Request Body
- **text** (string) - Required - The natural language text describing the schedule.

### Request Example
```json
{
    "text": "내일 저녁 7시에 강남에서 회식"
}
```

### Response
#### Success Response (200)
- **title** (string) - The parsed event title.
- **date** (string) - The parsed event date (YYYY-MM-DD).
- **time** (string) - The parsed event time (HH:MM).
- **location_name** (string) - The parsed event location name.
- **purpose** (string) - The parsed purpose of the event.

#### Response Example
```json
{
    "title": "회식",
    "date": "2025-12-09",
    "time": "19:00",
    "location_name": "강남",
    "purpose": "식사"
}
```

## POST /api/events

### Description
Creates a new calendar event for a user.

### Method
POST

### Endpoint
/api/events

### Parameters
#### Request Body
- **user_id** (integer) - Required - The ID of the user for whom the event is created.
- **title** (string) - Required - The title of the event.
- **date** (string) - Required - The date of the event (YYYY-MM-DD).
- **time** (string) - Required - The time of the event (HH:MM).
- **duration_hours** (float) - Required - The duration of the event in hours.
- **location_name** (string) - Optional - The name of the event location.
- **purpose** (string) - Optional - The purpose of the event.

### Request Example
```json
{
    "user_id": 1,
    "title": "팀 회식",
    "date": "2025-12-15",
    "time": "19:00",
    "duration_hours": 2.0,
    "location_name": "강남 맛집",
    "purpose": "식사"
}
```

### Response
#### Success Response (201)
- Returns the created event object, including its unique ID.

## GET /api/events

### Description
Retrieves all calendar events for the authenticated user.

### Method
GET

### Endpoint
/api/events

### Parameters
None

### Response
#### Success Response (200)
- Returns a list of calendar event objects.

## PUT /api/events/{event_id}

### Description
Updates an existing calendar event.

### Method
PUT

### Endpoint
/api/events/{event_id}

### Parameters
#### Path Parameters
- **event_id** (integer) - Required - The ID of the event to update.

#### Request Body
- Fields to update (e.g., title, date, time, duration_hours, location_name, purpose).

### Request Example
```json
{
    "title": "Updated Title",
    "date": "2025-12-16",
    "time": "18:00"
}
```

### Response
#### Success Response (200)
- Returns the updated event object.

## DELETE /api/events/{event_id}

### Description
Deletes a calendar event.

### Method
DELETE

### Endpoint
/api/events/{event_id}

### Parameters
#### Path Parameters
- **event_id** (integer) - Required - The ID of the event to delete.

### Response
#### Success Response (204)
- No content, indicating successful deletion.
```

--------------------------------

### Kakao OAuth Login API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Facilitates user authentication via Kakao. It exchanges a Kakao authorization code for a user access token and either creates a new user account or retrieves an existing one associated with the Kakao account.

```python
import requests

response = requests.post(
    "http://localhost:8000/api/auth/kakao",
    json={
        "code": "kakao_authorization_code_from_redirect"
    }
)

# Response
# {
#     "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
#     "token_type": "bearer",
#     "user_id": 5,
#     "name": "카카오유저"
# }
```

--------------------------------

### Venue Recommendation API

Source: https://context7.com/ymjo140/wemeet-project/llms.txt

Generates personalized venue recommendations based on various criteria including user group, purpose, selected tags, and location. It supports both individual and group-based searches, including midpoint calculations for multiple users.

```APIDOC
## POST /api/recommendations/venues

### Description
Provides AI-driven venue recommendations tailored to user inputs. This endpoint can handle single-user preferences or calculate optimal meeting points for groups, considering location and various filters.

### Method
POST

### Endpoint
/api/recommendations/venues

### Parameters
#### Headers
- **Authorization** (string) - Optional - Bearer token for authentication if user-specific preferences are to be strongly considered.

#### Request Body
- **purpose** (string) - Required - The purpose of the meeting (e.g., "dinner", "study", "casual chat").
- **num_people** (integer) - Required - The number of people attending.
- **current_locations** (array of objects) - Optional - List of current locations for each participant. Each object should contain `lat` and `lng`.
  - **lat** (float) - Latitude.
  - **lng** (float) - Longitude.
- **tags** (array of strings) - Optional - Specific tags or keywords to filter venues (e.g., "romantic", "live music", "quiet").
- **radius_km** (integer) - Optional - Search radius in kilometers.
- **user_preferences** (object) - Optional - User-specific preferences to override or supplement profile settings.
  - **foods** (array of strings) - Preferred food types.
  - **vibes** (array of strings) - Preferred atmosphere types.
  - **avg_spend** (integer) - Average spending per visit.

### Request Example
```json
{
    "purpose": "dinner",
    "num_people": 4,
    "current_locations": [
        {"lat": 37.500, "lng": 127.050},
        {"lat": 37.520, "lng": 127.020},
        {"lat": 37.480, "lng": 127.080},
        {"lat": 37.510, "lng": 127.000}
    ],
    "tags": ["Italian", "cozy"],
    "radius_km": 5,
    "user_preferences": {
        "avg_spend": 30000
    }
}
```

### Response
#### Success Response (200)
- **recommendations** (array of objects) - A list of recommended venues.
  - **venue_id** (integer) - Unique identifier for the venue.
  - **name** (string) - Name of the venue.
  - **address** (string) - Address of the venue.
  - **latitude** (float) - Latitude of the venue.
  - **longitude** (float) - Longitude of the venue.
  - **rating** (float) - Average rating of the venue.
  - **distance_km** (float) - Distance from the calculated midpoint or provided location.
  - **tags** (array of strings) - Tags associated with the venue.

#### Response Example
```json
{
    "recommendations": [
        {
            "venue_id": 456,
            "name": "Bella Italia",
            "address": "123 Main St, Seoul",
            "latitude": 37.515,
            "longitude": 127.035,
            "rating": 4.5,
            "distance_km": 1.2,
            "tags": ["Italian", "cozy", "romantic"]
        },
        {
            "venue_id": 789,
            "name": "Pasta Place",
            "address": "456 Oak Ave, Seoul",
            "latitude": 37.505,
            "longitude": 127.045,
            "rating": 4.2,
            "distance_km": 0.9,
            "tags": ["Italian", "casual"]
        }
    ]
}
```
```
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