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)

def find_top_50_university_students(json_file_path, age_min=18, age_max=24):
    # قائمة لتخزين الملفات الشخصية المتطابقة
    matching_profiles = []
    
    # قاموس بأنماط أفضل 50 جامعة (غير حساس لحالة الأحرف)
    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:
        # قراءة ملف JSON
        with open(json_file_path, 'r') as file:
            data = json.load(file)
            
        # التكرار عبر كل ملف شخصي للمستخدم
        for user_id, profile in data.items():
            profile_info = profile.get('profile_info', {})
            
            # التحقق من نطاق العمر (الافتراضي إلى الحد الأدنى/الأقصى القابل للتكوين)
            age = profile_info.get('age', 0)
            if not (age_min <= age <= age_max):
                continue
                
            # الحصول على قائمة التعليم (الافتراضي إلى قائمة فارغة إذا لم تكن موجودة)
            educations = profile_info.get('educations', [])
            
            # التحقق من كل سلسلة تعليمية للإشارات إلى أفضل 50 جامعة
            found_match = False
            matched_university = None
            for edu in educations:
                if not isinstance(edu, str):
                    continue
                    
                # تحويل إلى أحرف صغيرة للمطابقة غير حساسة لحالة الأحرف
                edu_lower = edu.lower()
                
                # التحقق من أنماط كل جامعة
                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
            
            # إذا وجدنا تطابقًا، أضف الملف الشخصي إلى نتائجنا
            if found_match:
                # الحصول على قائمة الصور (الافتراضي إلى قائمة فارغة إذا لم تكن موجودة)
                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"خطأ: الملف {json_file_path} غير موجود.")
        return []
    except json.JSONDecodeError:
        print(f"خطأ: تنسيق JSON غير صالح في {json_file_path}.")
        return []
    except Exception as e:
        print(f"خطأ غير متوقع: {str(e)}")
        return []



def main():
    # تهيئة العملاء باستخدام رمز المصادقة الخاص بك
    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: الكشط الجماعي
    # tools.scrape_recommendations_multiple(
    #     iterations=40, # 40 يبدو الحد الأقصى قبل الحاجة إلى تخطي الأشخاص...
    #     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"تم العثور على {len(results)} ملفًا شخصيًا متطابقًا.")

    # تعريف رؤوس CSV مع أعمدة منفصلة لكل صورة
    headers = ['timestamp', 'user_id', 'name', 'age', 'location', 'education', 
            'image1', 'image2', 'image3', 'image4', 'image5', 'image6']

    # فتح ملف CSV في وضع الإلحاق
    with open(csv_file_path, 'a', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        
        # كتابة الرؤوس إذا كان الملف فارغًا
        if csvfile.tell() == 0:
            writer.writeheader()
        
        # كتابة كل ملف شخصي كصف
        for profile in results:
            # إنشاء قاموس للصف
            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'])
            }
            
            # إضافة روابط الصور إلى أعمدة منفصلة
            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"تم إلحاق النتائج إلى {csv_file_path}")