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='مرحبًا، هذه رسالة اختبار!'
)
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)

# مثال: الحصول على معلومات المستخدم
tools.create_profile_json(
    source=ProfileSource.STANDOUTS,
    output_file="standouts.json"
)

# مثال: إعجاب مستخدم
with open("standouts.json", "r") as f:
    profiles = json.load(f)

personData = profiles["35582109789..."]

# إعجاب سؤال مستخدم
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)

# إعجاب صورة مستخدم
# 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)

يوضح هذا المثال كيفية كشط التوصيات وتصفية الملفات الشخصية بناءً على معايير محددة (مثل أفضل الجامعات).