reedgraff/hingesdk외롭고 지루한 SWE들을 위한 Hinge 데이트 앱 Python SDK
Hinge API용 비공식 Python SDK. 프로그래밍 방식으로 Hinge와 상호작용하여 추천 가져오기, 메시지 보내기, 미디어 다운로드, 상호작용 자동화를 수행합니다.
SDK를 사용하려면 유효한 인증 자격 증명이 필요합니다. 이러한 자격 증명은 .env 파일에 저장하거나 클라이언트에 직접 전달할 수 있습니다.
BEARER_TOKEN: JWT 인증 토큰 (예: L2euNWN...).SESSION_ID: 현재 세션 UUID.USER_ID: 고유한 사용자/플레이어 ID.기존 Hinge 계정을 사용하면서 새 기기 로그인으로 인한 플래그를 피하려면 앱 네트워크 트래픽에서 이러한 값을 추출해야 합니다.
참고: 올바른 변수를 얻기 위해 이 작업은 한 번만 수행하면 됩니다. 캡처 후에는 향후 세션에 SMS 로그인 방법을 사용할 수 있습니다.
prod-api.hingeaws.net 또는 유사한 Hinge 엔드포인트에 대한 HTTPS 요청을 검사하세요.authorization (Bearer 토큰)x-session-idx-device-idx-install-idUser ID (응답 본문에 subjectId 등으로 포함됨)SDK를 통해 SMS로 로그인하여 새 자격 증명을 생성할 수 있습니다. 이는 새 로그인 흐름을 수행합니다.
from hingesdk.client import HingeClient
# These IDs must match the actual values associated with your account.
# You cannot use random UUIDs; they must align with the account's registered device.
client = HingeClient.login_with_sms(
phone_number="+15551234567",
device_id="your_device_id_uuid",
install_id="your_install_id_uuid"
)
print(f"BEARER_TOKEN={client.auth_token}")
print(f"SESSION_ID={client.session_id}")
print(f"USER_ID={client.user_id}")
리포지토리를 클론하고 패키지를 설치합니다:
git clone https://github.com/reedgraff/hingesdk
cd hingesdk
pip install .
인증하고 사용자 추천을 가져오는 최소한의 예제입니다.
import os
from hingesdk.api import HingeAPIClient
# Initialize with credentials
client = HingeAPIClient(
auth_token=os.getenv("BEARER_TOKEN"),
session_id=os.getenv("SESSION_ID"),
user_id=os.getenv("USER_ID")
)
# Fetch recommendations
recs = client.get_recommendations()
print(f"Successfully fetched recommendations request.")
from hingesdk.client import HingeAPIClient
auth_token = 'your_auth_token'
user_id = 'your_user_id'
client = HingeAPIClient(auth_token=auth_token, user_id=user_id)
response = client.send_message(
subject_id='receiver_id',
message='Hello, this is a test message!'
)
print(response)
from hingesdk.api import HingeAPIClient
client = HingeAPIClient(auth_token=auth_token, user_id=user_id)
recommendations = client.get_recommendations()
print(recommendations)
from hingesdk.tools import HingeTools
from hingesdk.api import HingeAPIClient
from hingesdk.media import HingeMediaClient
api_client = HingeAPIClient(auth_token=auth_token, user_id=user_id)
media_client = HingeMediaClient(auth_token=auth_token)
tools = HingeTools(api_client, media_client)
tools.download_recommendation_content(output_path='path_to_save_images')
import os
import json
from hingesdk.tools import HingeTools
from hingesdk.api import HingeAPIClient
from hingesdk.media import HingeMediaClient
auth_token = os.getenv("BEARER_TOKEN")
session_id = os.getenv("SESSION_ID")
user_id = os.getenv("USER_ID")
api_client = HingeAPIClient(
auth_token=auth_token,
session_id=session_id,
user_id=user_id
)
media_client = HingeMediaClient(auth_token=auth_token)
tools = HingeTools(api_client, media_client)
# Example: Get user info
tools.create_profile_json(
source=ProfileSource.STANDOUTS,
output_file="standouts.json"
)
# Example: Like a user
with open("standouts.json", "r") as f:
profiles = json.load(f)
personData = profiles["35582109789..."]
# Like a user's question
questionIDToLike = "5c4a346828fd883a24..."
response = api_client.like_profile(
subject_id=personData["interaction_data"]["subject_id"],
rating_token=personData["interaction_data"]["rating_token"],
prompt={
"questionId": questionIDToLike,
"response": "I've been in Miami for a year and still haven't gone (╥﹏╥)"
}
)
print(response)
# Like a user's photo
# photoIDToLike = "2c6411ac-66e4-4194-..."
# response = api_client.like_profile(
# subject_id=personData["interaction_data"]["subject_id"],
# rating_token=personData["interaction_data"]["rating_token"],
# photo={
# "contentId": photoIDToLike,
# "comment": "So how many people have commented saying they've been here before?"
# }
# )
# print(response)
이 예시는 추천을 스크랩하고 특정 기준(예: 상위 대학)에 따라 프로필을 필터링하는 방법을 보여줍니다.
def find_top_50_university_students(json_file_path, age_min=18, age_max=24):
# List to store matching profiles
matching_profiles = []
# Dictionary of patterns for the top 50 universities (case insensitive)
top_50_patterns = {
"Princeton University": [r"princeton", r"\bpu\b", r"princeton\s+university"],
"Massachusetts Institute of Technology": [r"mit", r"massachusetts\s+institute\s+of\s+technology", r"mass\s+tech"],
"Harvard University": [r"harvard", r"\bhu\b", r"harvard\s+university"],
"Stanford University": [r"stanford", r"\bsu\b", r"stanford\s+university"],
"Yale University": [r"yale", r"\byu\b", r"yale\s+university"],
"California Institute of Technology": [r"caltech", r"california\s+institute\s+of\s+technology"],
"Duke University": [r"duke", r"\bdu\b", r"duke\s+university"],
"Johns Hopkins University": [r"johns\s+hopkins", r"\bjhu\b", r"hopkins"],
"Northwestern University": [r"northwestern", r"\bnu\b", r"northwestern\s+university"],
"University of Pennsylvania": [r"upenn", r"penn", r"university\s+of\s+pennsylvania"],
"Cornell University": [r"cornell", r"\bcu\b", r"cornell\s+university"],
"University of Chicago": [r"uchicago", r"university\s+of\s+chicago", r"u\s+chicago"],
"Brown University": [r"brown", r"\bbu\b", r"brown\s+university"],
"Columbia University": [r"columbia", r"\bcu\b", r"columbia\s+university"],
"Dartmouth College": [r"dartmouth", r"\bdc\b", r"dartmouth\s+college"],
"University of California--Los Angeles": [r"ucla", r"university\s+of\s+california\s+los\s+angeles", r"uc\s+la"],
"University of California, Berkeley": [r"uc\s+berkeley", r"berkeley", r"university\s+of\s+california\s+berkeley"],
"Rice University": [r"rice", r"\bru\b", r"rice\s+university"],
"University of Notre Dame": [r"notre\s+dame", r"\bnd\b", r"university\s+of\s+notre\s+dame"],
"Vanderbilt University": [r"vanderbilt", r"\bvu\b", r"vandy"],
"Carnegie Mellon University": [r"carnegie\s+mellon", r"\bcmu\b", r"cmu"],
"University of Michigan--Ann Arbor": [r"umich", r"michigan", r"university\s+of\s+michigan"],
"Washington University in St. Louis": [r"washu", r"washington\s+university", r"wu\s+stl"],
"Emory University": [r"emory", r"\beu\b", r"emory\s+university"],
"Georgetown University": [r"georgetown", r"\bgu\b", r"georgetown\s+university"],
"University of Virginia": [r"uva", r"virginia", r"university\s+of\s+virginia"],
"University of North Carolina--Chapel Hill": [r"unc", r"chapel\s+hill", r"university\s+of\s+north\s+carolina"],
"University of Southern California": [r"usc", r"southern\s+california", r"university\s+of\s+southern\s+california"],
"University of California, San Diego": [r"ucsd", r"uc\s+san\s+diego", r"university\s+of\s+california\s+san\s+diego"],
"New York University": [r"nyu", r"new\s+york\s+university"],
"University of Florida": [r"uf", r"florida", r"university\s+of\s+florida"],
"The University of Texas--Austin": [r"ut\s+austin", r"utexas", r"university\s+of\s+texas"],
"Georgia Institute of Technology": [r"gatech", r"georgia\s+tech", r"georgia\s+institute\s+of\s+technology"],
"University of California, Davis": [r"uc\s+davis", r"ucd", r"university\s+of\s+california\s+davis"],
"University of California--Irvine": [r"uci", r"uc\s+irvine", r"university\s+of\s+california\s+irvine"],
"University of Illinois Urbana-Champaign": [r"uiuc", r"illinois", r"university\s+of\s+illinois"],
"Boston College": [r"bc", r"boston\s+college"],
"Tufts University": [r"tufts", r"\btu\b", r"tufts\s+university"],
"University of California, Santa Barbara": [r"ucsb", r"uc\s+santa\s+barbara", r"university\s+of\s+california\s+santa\s+barbara"],
"University of Wisconsin--Madison": [r"uw\s+madison", r"wisconsin", r"university\s+of\s+wisconsin"],
"Boston University": [r"bu", r"boston\s+university"],
"The Ohio State University": [r"ohio\s+state", r"osu", r"the\s+ohio\s+state\s+university"],
"Rutgers University--New Brunswick": [r"rutgers", r"ru", r"rutgers\s+university"],
"University of Maryland, College Park": [r"umd", r"maryland", r"university\s+of\s+maryland"],
"University of Rochester": [r"rochester", r"\bur\b", r"university\s+of\s+rochester"],
"Lehigh University": [r"lehigh", r"\blu\b", r"lehigh\s+university"],
"Purdue University--Main Campus": [r"purdue", r"\bpu\b", r"purdue\s+university"],
"University of Georgia": [r"uga", r"georgia", r"university\s+of\s+georgia"],
"University of Washington": [r"uw", r"washington", r"university\s+of\s+washington"],
"Wake Forest University": [r"wake\s+forest", r"\bwfu\b", r"wake"],
"Case Western Reserve University": [r"case\s+western", r"\bcwru\b", r"case"],
"Texas A&M University": [r"texas\s+a&m", r"tamu", r"a&m"],
"Virginia Tech": [r"virginia\s+tech", r"vt", r"vtech"],
"Florida State University": [r"fsu", r"florida\s+state", r"florida\s+state\s+university"],
"University of Miami": [r'umiami', r'\bum\b', r'university\s+of\s+miami']
}
try:
# Read the JSON file
with open(json_file_path, 'r') as file:
data = json.load(file)
# Iterate through each user profile
for user_id, profile in data.items():
profile_info = profile.get('profile_info', {})
# Check age range (default to configurable min/max)
age = profile_info.get('age', 0)
if not (age_min <= age <= age_max):
continue
# Get education list (default to empty list if not present)
educations = profile_info.get('educations', [])
# Check each education string for top 50 university references
found_match = False
matched_university = None
for edu in educations:
if not isinstance(edu, str):
continue
# Convert to lowercase for case-insensitive matching
edu_lower = edu.lower()
# Check each university's patterns
for university, patterns in top_50_patterns.items():
for pattern in patterns:
if re.search(pattern, edu_lower):
found_match = True
matched_university = university
break
if found_match:
break
if found_match:
break
# If we found a match, add the profile to our results
if found_match:
# Get images list (default to empty list if not present)
images = profile.get('images', [])
image_urls = [img.get('url', '') for img in images if img.get('url')]
matching_profiles.append({
'user_id': user_id,
'age': age,
'firstName': profile_info.get('firstName', ''),
'educations': educations,
'matched_university': matched_university,
'location': profile_info.get('location', {}).get('name', ''),
'image_urls': image_urls
})
return matching_profiles
except FileNotFoundError:
print(f"Error: File {json_file_path} not found.")
return []
except json.JSONDecodeError:
print(f"Error: Invalid JSON format in {json_file_path}.")
return []
except Exception as e:
print(f"Unexpected error: {str(e)}")
return []
def main():
# Initialize clients with your auth token
auth_token = os.getenv("BEARER_TOKEN")
session_id = os.getenv("SESSION_ID")
user_id = os.getenv("USER_ID")
api_client = HingeAPIClient(
auth_token=auth_token,
session_id=session_id,
user_id=user_id
)
media_client = HingeMediaClient(auth_token=auth_token)
tools = HingeTools(api_client, media_client)
# Example: Mass Scraping
# tools.scrape_recommendations_multiple(
# iterations=40, # 40 seems like the max before needing to skip people...
# min_sleep = 20,
# max_sleep = 60,
# )
json_file_path = 'all_recommendations.json'
csv_file_path = 'university_matches.csv'
results = find_top_50_university_students(json_file_path)
print(f"Found {len(results)} matching profiles.")
# Define CSV headers with separate columns for each image
headers = ['timestamp', 'user_id', 'name', 'age', 'location', 'education',
'image1', 'image2', 'image3', 'image4', 'image5', 'image6']
# Open CSV file in append mode
with open(csv_file_path, 'a', newline='', encoding='utf-8') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=headers)
# Write headers if file is empty
if csvfile.tell() == 0:
writer.writeheader()
# Write each profile as a row
for profile in results:
# Create a dictionary for the row
row_data = {
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'user_id': profile['user_id'],
'name': profile['firstName'],
'age': profile['age'],
'location': profile['location'],
'education': ', '.join(profile['educations'])
}
# Add image URLs to separate columns
for i in range(6):
image_key = f'image{i+1}'
if profile['image_urls'] and i < len(profile['image_urls']):
row_data[image_key] = profile['image_urls'][i]
else:
row_data[image_key] = ''
writer.writerow(row_data)
print(f"Results have been appended to {csv_file_path}")
SDK는 논리적 모듈로 구성되어 관심사를 분리합니다.
hingesdk/
├── __init__.py
├── client.py # 기본 HingeClient: HTTP 요청, 헤더 및 인증 처리
├── api.py # HingeAPIClient: 핵심 API 메서드 (좋아요, 메시지, 추천 가져오기)
├── media.py # HingeMediaClient: 이미지 다운로드/처리 도우미
├── tools.py # HingeTools: 고수준 워크플로 (배치 스크래핑, 내보내기)
├── models.py # Pydantic 모델 및 데이터 구조 (해당하는 경우)
├── exceptions.py # 사용자 정의 예외 클래스 (HingeAPIError, HingeAuthError)
└── assets/ # 정적 리소스 (예: 프롬프트 정의)
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다.