reedgraff/hingesdkHinge Dating App SDK für Python für einsame gelangweilte SWEs
Inoffizielles Python SDK für die Hinge-API. Interagiere programmatisch mit Hinge, um Empfehlungen abzurufen, Nachrichten zu senden, Medien herunterzuladen und Interaktionen zu automatisieren.
Um dieses SDK zu verwenden, benötigst du gültige Authentifizierungsdaten. Du kannst sie in einer .env-Datei speichern oder direkt an den Client übergeben.
BEARER_TOKEN: Dein JWT-Authentifizierungstoken (z. B. L2euNWN...).SESSION_ID: Die aktuelle Session-UUID.USER_ID: Deine eindeutige Benutzer-/Spieler-ID.Wenn du dein bestehendes Hinge-Konto nutzen möchtest, ohne Risiken durch Anmeldungen von neuen Geräten einzugehen, musst du diese Werte aus dem Netzwerkverkehr der App extrahieren.
Hinweis: Du musst dies nur einmal tun, um die korrekten Variablen zu erhalten. Nachdem du sie erfasst hast, kannst du für zukünftige Sitzungen die SMS-Anmeldemethode verwenden.
prod-api.hingeaws.net oder ähnliche Hinge-Endpunkte.authorization (Bearer-Token)x-session-idx-device-idx-install-idUser ID (oft im Antworttext als subjectId oder ähnlich)Du kannst neue Anmeldedaten generieren, indem du dich über das SDK per SMS anmeldest. Dies führt einen frischen Anmeldevorgang durch.
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}")
Klone das Repository und installiere das Paket:
git clone https://github.com/reedgraff/hingesdk
cd hingesdk
pip install .
Hier ist ein minimales Beispiel zur Authentifizierung und zum Abrufen von Benutzerempfehlungen.
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)
This example demonstrates how to scrape recommendations and filter profiles based on specific criteria (e.g., top universities).
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}")
hingesdk/
├── __init__.py
├── client.py # Base HingeClient: Handles HTTP requests, headers, and Auth
├── api.py # HingeAPIClient: Core API methods (like, message, get_recs)
├── media.py # HingeMediaClient: Helpers for downloading/processing images
├── tools.py # HingeTools: High-level workflows (batch scraping, exports)
├── models.py # Pydantic models and data structures (if applicable)
├── exceptions.py # Custom exception classes (HingeAPIError, HingeAuthError)
└── assets/ # Static resources (e.g., prompt definitions)
Dieses Projekt ist unter der MIT-Lizenz lizenziert.