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README.md

@@ -1,15 +1,13 @@
 ---
-title: Career Predictor
-emoji: 🔥
-colorFrom: gray
-colorTo: blue
+title: Global Career Predictor
+emoji: 🌖
+colorFrom: green
+colorTo: pink
 sdk: gradio
-sdk_version: 6.16.0
-python_version: '3.13'
+sdk_version: 6.2.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: Predict Career Growth & Salary Estimates
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,1880 @@
+# Career Prediction Assistant - Advanced Interactive Dashboard
+# =======================================================
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+from typing import Dict, List, Tuple
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')  # مهم ليعمل مع Gradio
+import json
+from datetime import datetime
+import warnings
+warnings.filterwarnings('ignore')
+
+# ====================
+# SYSTEM CONFIGURATION
+# ====================
+class Config:
+    # All countries with detailed classification
+    COUNTRIES = {
+        # Arab Countries
+        'Egypt': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Saudi Arabia': {'region': 'Arab', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'United Arab Emirates': {'region': 'Arab', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Kuwait': {'region': 'Arab', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Qatar': {'region': 'Arab', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Bahrain': {'region': 'Arab', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Oman': {'region': 'Arab', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Jordan': {'region': 'Arab', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'Lebanon': {'region': 'Arab', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'Morocco': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Tunisia': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Algeria': {'region': 'Arab', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'Iraq': {'region': 'Arab', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Yemen': {'region': 'Arab', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Syria': {'region': 'Arab', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Libya': {'region': 'Arab', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Sudan': {'region': 'Arab', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Mauritania': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Somalia': {'region': 'Arab', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Djibouti': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Comoros': {'region': 'Arab', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Palestine': {'region': 'Arab', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        
+        # Other Middle East
+        'Turkey': {'region': 'Middle East', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Iran': {'region': 'Middle East', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Israel': {'region': 'Middle East', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        
+        # Caucasus Region
+        'Armenia': {'region': 'Caucasus', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Azerbaijan': {'region': 'Caucasus', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Georgia': {'region': 'Caucasus', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        
+        # Central Asia
+        'Kazakhstan': {'region': 'Central Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Uzbekistan': {'region': 'Central Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Turkmenistan': {'region': 'Central Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Kyrgyzstan': {'region': 'Central Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Tajikistan': {'region': 'Central Asia', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        
+        # South Asia
+        'India': {'region': 'South Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Pakistan': {'region': 'South Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Bangladesh': {'region': 'South Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Sri Lanka': {'region': 'South Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Nepal': {'region': 'South Asia', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Afghanistan': {'region': 'South Asia', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Bhutan': {'region': 'South Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Maldives': {'region': 'South Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        
+        # East Asia
+        'China': {'region': 'East Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Advanced'},
+        'Japan': {'region': 'East Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'South Korea': {'region': 'East Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'North Korea': {'region': 'East Asia', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Mongolia': {'region': 'East Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Taiwan': {'region': 'East Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Hong Kong': {'region': 'East Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Macau': {'region': 'East Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        
+        # Southeast Asia
+        'Indonesia': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Malaysia': {'region': 'Southeast Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Philippines': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Singapore': {'region': 'Southeast Asia', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Thailand': {'region': 'Southeast Asia', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Vietnam': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Myanmar': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Cambodia': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Laos': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Brunei': {'region': 'Southeast Asia', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Timor-Leste': {'region': 'Southeast Asia', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        
+        # Europe
+        'United Kingdom': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Germany': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'France': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Italy': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Spain': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Advanced'},
+        'Portugal': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Netherlands': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Belgium': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Switzerland': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Sweden': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Norway': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Denmark': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Finland': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Ireland': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Austria': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Greece': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Poland': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Czech Republic': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Hungary': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Romania': {'region': 'Europe', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Bulgaria': {'region': 'Europe', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Croatia': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Serbia': {'region': 'Europe', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Slovakia': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Slovenia': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Estonia': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Latvia': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Lithuania': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Ukraine': {'region': 'Europe', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Belarus': {'region': 'Europe', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Moldova': {'region': 'Europe', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Russia': {'region': 'Europe', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Cyprus': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Malta': {'region': 'Europe', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Iceland': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Luxembourg': {'region': 'Europe', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        
+        # Africa (Non-Arab)
+        'South Africa': {'region': 'Africa', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Nigeria': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Kenya': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Ethiopia': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Emerging'},
+        'Ghana': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Tanzania': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Emerging'},
+        'Uganda': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Emerging'},
+        'Rwanda': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Emerging'},
+        'Zimbabwe': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Zambia': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Mozambique': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Angola': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Botswana': {'region': 'Africa', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Namibia': {'region': 'Africa', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Senegal': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Ivory Coast': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Cameroon': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'DR Congo': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Madagascar': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Mali': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Burkina Faso': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Niger': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Chad': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Guinea': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Benin': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Togo': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Sierra Leone': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Liberia': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Central African Republic': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Congo': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Gabon': {'region': 'Africa', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Equatorial Guinea': {'region': 'Africa', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Burundi': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Eritrea': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'South Sudan': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Mauritius': {'region': 'Africa', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Seychelles': {'region': 'Africa', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Cape Verde': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Gambia': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Guinea-Bissau': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Comoros': {'region': 'Africa', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'São Tomé and Príncipe': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Eswatini': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Lesotho': {'region': 'Africa', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        
+        # Americas
+        'United States': {'region': 'North America', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Canada': {'region': 'North America', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Mexico': {'region': 'North America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Brazil': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Argentina': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Colombia': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Chile': {'region': 'South America', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Peru': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Venezuela': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Ecuador': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Bolivia': {'region': 'South America', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Paraguay': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'Uruguay': {'region': 'South America', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Guyana': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'Suriname': {'region': 'South America', 'gdp_tier': 'Upper Middle', 'tech_level': 'Emerging'},
+        'French Guiana': {'region': 'South America', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Cuba': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Dominican Republic': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Haiti': {'region': 'Caribbean', 'gdp_tier': 'Low', 'tech_level': 'Basic'},
+        'Jamaica': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Puerto Rico': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Trinidad and Tobago': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Bahamas': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Barbados': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Saint Lucia': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Grenada': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Saint Vincent and the Grenadines': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Antigua and Barbuda': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Dominica': {'region': 'Caribbean', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Saint Kitts and Nevis': {'region': 'Caribbean', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        
+        # Oceania
+        'Australia': {'region': 'Oceania', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'New Zealand': {'region': 'Oceania', 'gdp_tier': 'Very High', 'tech_level': 'Advanced'},
+        'Fiji': {'region': 'Oceania', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Papua New Guinea': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Emerging'},
+        'Solomon Islands': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Vanuatu': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Samoa': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Developing'},
+        'Kiribati': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Tonga': {'region': 'Oceania', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Micronesia': {'region': 'Oceania', 'gdp_tier': 'Lower Middle', 'tech_level': 'Basic'},
+        'Marshall Islands': {'region': 'Oceania', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+        'Palau': {'region': 'Oceania', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Nauru': {'region': 'Oceania', 'gdp_tier': 'High', 'tech_level': 'Developing'},
+        'Tuvalu': {'region': 'Oceania', 'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'},
+    }
+    
+    # Career categories with detailed majors
+    CAREER_CATEGORIES = {
+        'Technology': [
+            'Computer Science', 'Software Engineering', 'Information Technology',
+            'Data Science', 'Artificial Intelligence', 'Cybersecurity',
+            'Cloud Computing', 'Machine Learning', 'Computer Engineering',
+            'Information Systems', 'Web Development', 'Mobile App Development',
+            'DevOps Engineering', 'UI/UX Design', 'Game Development',
+            'Blockchain Development', 'IoT Engineering', 'Robotics',
+            'Quantum Computing', 'Bioinformatics'
+        ],
+        'Engineering': [
+            'Electrical Engineering', 'Mechanical Engineering', 'Civil Engineering',
+            'Chemical Engineering', 'Biomedical Engineering', 'Aerospace Engineering',
+            'Petroleum Engineering', 'Telecommunications Engineering',
+            'Industrial Engineering', 'Environmental Engineering',
+            'Materials Engineering', 'Nuclear Engineering',
+            'Marine Engineering', 'Automotive Engineering',
+            'Mining Engineering', 'Geotechnical Engineering',
+            'Structural Engineering', 'Renewable Energy Engineering'
+        ],
+        'Business': [
+            'Business Administration', 'Finance', 'Accounting', 'Marketing',
+            'International Business', 'Supply Chain Management',
+            'Human Resources', 'Business Analytics', 'Entrepreneurship',
+            'Project Management', 'Digital Marketing', 'Financial Analysis',
+            'Risk Management', 'Investment Banking', 'Management Consulting',
+            'Corporate Finance', 'Sales Management', 'Operations Management',
+            'Strategic Management', 'E-commerce Management'
+        ],
+        'Healthcare': [
+            'Medicine', 'Nursing', 'Pharmacy', 'Dentistry',
+            'Biotechnology', 'Public Health', 'Medical Laboratory Sciences',
+            'Physiotherapy', 'Medical Imaging', 'Healthcare Administration',
+            'Nutrition', 'Medical Research', 'Clinical Psychology',
+            'Veterinary Medicine', 'Epidemiology', 'Pharmaceutical Sciences',
+            'Medical Technology', 'Health Informatics', 'Occupational Therapy'
+        ],
+        'Science': [
+            'Physics', 'Chemistry', 'Biology', 'Mathematics',
+            'Statistics', 'Environmental Science', 'Geology',
+            'Astronomy', 'Biochemistry', 'Molecular Biology',
+            'Genetics', 'Microbiology', 'Neuroscience',
+            'Materials Science', 'Oceanography', 'Atmospheric Science',
+            'Agricultural Science', 'Food Science', 'Forensic Science'
+        ],
+        'Humanities': [
+            'Psychology', 'Economics', 'Political Science', 'Sociology',
+            'Law', 'International Relations', 'Education',
+            'History', 'Philosophy', 'Literature',
+            'Linguistics', 'Anthropology', 'Archaeology',
+            'Media Studies', 'Journalism', 'Fine Arts',
+            'Music', 'Theater Arts', 'Cultural Studies'
+        ]
+    }
+    
+    # Experience levels
+    EXPERIENCE_LEVELS = {
+        'Intern/Student (0-1 years)': (0, 1),
+        'Entry Level (1-3 years)': (1, 3),
+        'Junior (3-5 years)': (3, 5),
+        'Mid Level (5-8 years)': (5, 8),
+        'Senior (8-12 years)': (8, 12),
+        'Lead (12-15 years)': (12, 15),
+        'Principal (15-20 years)': (15, 20),
+        'Executive/Director (20+ years)': (20, 40)
+    }
+    
+    # Company sizes
+    COMPANY_SIZES = [
+        'Startup (1-10 employees)', 'Small (11-50)', 'Medium (51-200)',
+        'Large (201-1000)', 'Corporate (1001-5000)', 'Enterprise (5000+)',
+        'Multinational (10000+)'
+    ]
+    
+    # GDP Tier multipliers for salary calculation
+    GDP_TIER_MULTIPLIERS = {
+        'Very High': 1.8,    # USA, UK, Germany, etc.
+        'High': 1.4,         # Saudi Arabia, UAE, etc.
+        'Upper Middle': 1.0, # Turkey, China, Brazil, etc.
+        'Lower Middle': 0.7, # Egypt, India, etc.
+        'Low': 0.4           # Yemen, Afghanistan, etc.
+    }
+    
+    # Tech Level multipliers
+    TECH_LEVEL_MULTIPLIERS = {
+        'Advanced': 1.3,     # Silicon Valley level
+        'Developing': 1.1,   # Growing tech hubs
+        'Emerging': 1.0,     # Basic tech infrastructure
+        'Basic': 0.7         # Limited tech access
+    }
+
+# ====================
+# DATA MANAGER
+# ====================
+class DataManager:
+    def __init__(self):
+        self.companies_db = self.load_companies_database()
+        self.salary_data = self.load_salary_benchmarks()
+        self.skills_data = self.load_skills_data()
+        
+    def load_companies_database(self) -> pd.DataFrame:
+        """Load comprehensive global companies database"""
+        companies_data = []
+        
+        # Tech Companies (Global)
+        tech_companies = [
+            # USA
+            ('Google', 'USA', 'Technology', 'Multinational (10000+)', 'Actively Hiring', 2.0, 95),
+            ('Microsoft', 'USA', 'Technology', 'Multinational (10000+)', 'Selective Hiring', 1.9, 92),
+            ('Apple', 'USA', 'Technology', 'Multinational (10000+)', 'Selective Hiring', 2.1, 94),
+            ('Amazon', 'USA', 'E-commerce', 'Multinational (10000+)', 'Actively Hiring', 1.8, 90),
+            ('Meta', 'USA', 'Technology', 'Multinational (10000+)', 'Moderate Hiring', 1.9, 91),
+            ('IBM', 'USA', 'Technology', 'Enterprise (5000+)', 'Selective Hiring', 1.6, 85),
+            ('Oracle', 'USA', 'Technology', 'Enterprise (5000+)', 'Moderate Hiring', 1.7, 86),
+            ('Intel', 'USA', 'Semiconductors', 'Enterprise (5000+)', 'Selective Hiring', 1.8, 88),
+            ('NVIDIA', 'USA', 'Technology', 'Enterprise (5000+)', 'Actively Hiring', 2.2, 96),
+            ('Tesla', 'USA', 'Automotive/Tech', 'Enterprise (5000+)', 'Actively Hiring', 1.9, 92),
+            
+            # Europe
+            ('SAP', 'Germany', 'Enterprise Software', 'Enterprise (5000+)', 'Actively Hiring', 1.5, 87),
+            ('Spotify', 'Sweden', 'Technology', 'Large (201-1000)', 'Moderate Hiring', 1.7, 89),
+            ('Siemens', 'Germany', 'Industrial Tech', 'Multinational (10000+)', 'Selective Hiring', 1.6, 85),
+            ('Ericsson', 'Sweden', 'Telecom', 'Enterprise (5000+)', 'Moderate Hiring', 1.5, 84),
+            ('ARM', 'UK', 'Semiconductors', 'Medium (51-200)', 'Selective Hiring', 1.8, 90),
+            
+            # Asia
+            ('Samsung', 'South Korea', 'Electronics', 'Multinational (10000+)', 'Actively Hiring', 1.7, 88),
+            ('Tencent', 'China', 'Technology', 'Multinational (10000+)', 'Actively Hiring', 1.8, 89),
+            ('Alibaba', 'China', 'E-commerce', 'Multinational (10000+)', 'Moderate Hiring', 1.7, 87),
+            ('Baidu', 'China', 'Technology', 'Enterprise (5000+)', 'Selective Hiring', 1.6, 86),
+            ('Rakuten', 'Japan', 'E-commerce', 'Enterprise (5000+)', 'Moderate Hiring', 1.5, 85),
+            
+            # Arab Tech Companies
+            ('Careem', 'UAE', 'Technology', 'Large (201-1000)', 'Actively Hiring', 1.6, 88),
+            ('Souq.com', 'UAE', 'E-commerce', 'Large (201-1000)', 'Moderate Hiring', 1.5, 86),
+            ('Noon', 'UAE', 'E-commerce', 'Enterprise (5000+)', 'Actively Hiring', 1.7, 87),
+            ('Mawdoo3', 'Jordan', 'Technology', 'Medium (51-200)', 'Moderate Hiring', 1.3, 82),
+            ('Talal Abu-Ghazaleh', 'Jordan', 'Professional Services', 'Large (201-1000)', 'Selective Hiring', 1.4, 83),
+            ('STC Solutions', 'Saudi Arabia', 'Technology', 'Enterprise (5000+)', 'Actively Hiring', 1.6, 85),
+            ('Elmenus', 'Egypt', 'Technology', 'Medium (51-200)', 'Actively Hiring', 1.4, 84),
+            ('Vezeeta', 'Egypt', 'Health Tech', 'Medium (51-200)', 'Moderate Hiring', 1.5, 86),
+            ('Swvl', 'Egypt', 'Transport Tech', 'Medium (51-200)', 'Moderate Hiring', 1.6, 87),
+            ('Fetchr', 'UAE', 'Logistics Tech', 'Medium (51-200)', 'Actively Hiring', 1.5, 85),
+        ]
+        
+        # Oil & Gas Companies
+        oil_companies = [
+            ('Saudi Aramco', 'Saudi Arabia', 'Oil & Gas', 'Multinational (10000+)', 'Selective Hiring', 2.3, 90),
+            ('Qatar Petroleum', 'Qatar', 'Oil & Gas', 'Enterprise (5000+)', 'Selective Hiring', 2.2, 89),
+            ('ADNOC', 'UAE', 'Oil & Gas', 'Enterprise (5000+)', 'Actively Hiring', 2.1, 88),
+            ('Kuwait Petroleum', 'Kuwait', 'Oil & Gas', 'Enterprise (5000+)', 'Moderate Hiring', 2.0, 87),
+            ('Bapco', 'Bahrain', 'Oil & Gas', 'Large (201-1000)', 'Selective Hiring', 1.9, 86),
+            ('Sonatrach', 'Algeria', 'Oil & Gas', 'Enterprise (5000+)', 'Moderate Hiring', 1.8, 85),
+            ('ENI', 'Italy', 'Oil & Gas', 'Multinational (10000+)', 'Selective Hiring', 1.7, 88),
+            ('ExxonMobil', 'USA', 'Oil & Gas', 'Multinational (10000+)', 'Moderate Hiring', 1.9, 89),
+            ('Shell', 'Netherlands', 'Oil & Gas', 'Multinational (10000+)', 'Selective Hiring', 1.8, 88),
+            ('BP', 'UK', 'Oil & Gas', 'Multinational (10000+)', 'Moderate Hiring', 1.7, 87),
+        ]
+        
+        # Banking & Finance
+        finance_companies = [
+            ('QNB', 'Qatar', 'Banking', 'Enterprise (5000+)', 'Actively Hiring', 1.8, 87),
+            ('Emirates NBD', 'UAE', 'Banking', 'Enterprise (5000+)', 'Moderate Hiring', 1.7, 86),
+            ('Al Rajhi Bank', 'Saudi Arabia', 'Banking', 'Enterprise (5000+)', 'Selective Hiring', 1.9, 88),
+            ('Arab Bank', 'Jordan', 'Banking', 'Enterprise (5000+)', 'Moderate Hiring', 1.6, 85),
+            ('National Bank of Egypt', 'Egypt', 'Banking', 'Multinational (10000+)', 'Actively Hiring', 1.5, 84),
+            ('Attijariwafa Bank', 'Morocco', 'Banking', 'Enterprise (5000+)', 'Moderate Hiring', 1.4, 83),
+            ('Bank of China', 'China', 'Banking', 'Multinational (10000+)', 'Actively Hiring', 1.6, 86),
+            ('HSBC', 'UK', 'Banking', 'Multinational (10000+)', 'Selective Hiring', 1.7, 87),
+            ('Goldman Sachs', 'USA', 'Investment Banking', 'Enterprise (5000+)', 'Selective Hiring', 2.2, 92),
+            ('JP Morgan', 'USA', 'Banking', 'Multinational (10000+)', 'Moderate Hiring', 2.0, 90),
+        ]
+        
+        # Telecommunications
+        telecom_companies = [
+            ('STC', 'Saudi Arabia', 'Telecommunications', 'Multinational (10000+)', 'Actively Hiring', 1.7, 86),
+            ('Etisalat', 'UAE', 'Telecommunications', 'Enterprise (5000+)', 'Moderate Hiring', 1.8, 87),
+            ('Zain', 'Kuwait', 'Telecommunications', 'Enterprise (5000+)', 'Actively Hiring', 1.6, 85),
+            ('Ooredoo', 'Qatar', 'Telecommunications', 'Enterprise (5000+)', 'Moderate Hiring', 1.7, 86),
+            ('Orange', 'France', 'Telecommunications', 'Multinational (10000+)', 'Selective Hiring', 1.5, 85),
+            ('Vodafone', 'UK', 'Telecommunications', 'Multinational (10000+)', 'Moderate Hiring', 1.6, 86),
+            ('China Mobile', 'China', 'Telecommunications', 'Multinational (10000+)', 'Actively Hiring', 1.4, 84),
+            ('AT&T', 'USA', 'Telecommunications', 'Multinational (10000+)', 'Selective Hiring', 1.7, 87),
+        ]
+        
+        # Healthcare
+        healthcare_companies = [
+            ('King Faisal Specialist Hospital', 'Saudi Arabia', 'Healthcare', 'Enterprise (5000+)', 'Actively Hiring', 1.8, 88),
+            ('Cleveland Clinic Abu Dhabi', 'UAE', 'Healthcare', 'Large (201-1000)', 'Selective Hiring', 1.9, 89),
+            ('American Hospital Dubai', 'UAE', 'Healthcare', 'Medium (51-200)', 'Moderate Hiring', 1.7, 87),
+            ('Saudi German Hospital', 'Saudi Arabia', 'Healthcare', 'Large (201-1000)', 'Actively Hiring', 1.6, 86),
+            ('Kasr Al Ainy Hospital', 'Egypt', 'Healthcare', 'Enterprise (5000+)', 'Actively Hiring', 1.3, 83),
+            ('Mayo Clinic', 'USA', 'Healthcare', 'Multinational (10000+)', 'Selective Hiring', 2.0, 92),
+            ('Johns Hopkins Hospital', 'USA', 'Healthcare', 'Enterprise (5000+)', 'Selective Hiring', 1.9, 91),
+        ]
+        
+        # Aviation
+        aviation_companies = [
+            ('Emirates Airlines', 'UAE', 'Aviation', 'Enterprise (5000+)', 'Actively Hiring', 1.8, 88),
+            ('Qatar Airways', 'Qatar', 'Aviation', 'Enterprise (5000+)', 'Moderate Hiring', 1.9, 89),
+            ('Saudi Airlines', 'Saudi Arabia', 'Aviation', 'Enterprise (5000+)', 'Actively Hiring', 1.7, 87),
+            ('EgyptAir', 'Egypt', 'Aviation', 'Large (201-1000)', 'Moderate Hiring', 1.4, 84),
+            ('Royal Jordanian', 'Jordan', 'Aviation', 'Medium (51-200)', 'Selective Hiring', 1.5, 85),
+        ]
+        
+        # Construction & Engineering
+        construction_companies = [
+            ('Saudi Binladin Group', 'Saudi Arabia', 'Construction', 'Multinational (10000+)', 'Actively Hiring', 1.6, 85),
+            ('Arabtec', 'UAE', 'Construction', 'Enterprise (5000+)', 'Moderate Hiring', 1.5, 84),
+            ('ACC', 'India', 'Construction', 'Enterprise (5000+)', 'Actively Hiring', 1.4, 83),
+            ('Bechtel', 'USA', 'Engineering', 'Multinational (10000+)', 'Selective Hiring', 1.8, 88),
+            ('Fluor', 'USA', 'Engineering', 'Enterprise (5000+)', 'Moderate Hiring', 1.7, 87),
+        ]
+        
+        # Add all companies to list
+        all_companies = (tech_companies + oil_companies + finance_companies + 
+                        telecom_companies + healthcare_companies + 
+                        aviation_companies + construction_companies)
+        
+        # Convert to DataFrame
+        companies_df = pd.DataFrame(all_companies, columns=[
+            'company_name', 'country', 'industry', 'company_size',
+            'hiring_status', 'avg_salary_multiplier', 'career_growth_score'
+        ])
+        
+        # Add more companies from different countries
+        additional_companies = []
+        
+        # Add companies for countries not covered
+        for country in Config.COUNTRIES:
+            if country not in companies_df['country'].unique():
+                # Add representative companies for each country
+                country_info = Config.COUNTRIES[country]
+                
+                if country_info['tech_level'] in ['Advanced', 'Developing']:
+                    # Add tech company
+                    additional_companies.append((
+                        f'{country} Tech Solutions',
+                        country,
+                        'Technology',
+                        'Medium (51-200)',
+                        'Actively Hiring',
+                        1.3 + (0.1 if country_info['gdp_tier'] in ['Very High', 'High'] else 0),
+                        75 + (10 if country_info['tech_level'] == 'Advanced' else 5)
+                    ))
+                
+                # Add major local company
+                additional_companies.append((
+                    f'National {country} Corporation',
+                    country,
+                    'Various',
+                    'Large (201-1000)',
+                    'Moderate Hiring',
+                    1.2,
+                    70
+                ))
+        
+        if additional_companies:
+            additional_df = pd.DataFrame(additional_companies, columns=companies_df.columns)
+            companies_df = pd.concat([companies_df, additional_df], ignore_index=True)
+        
+        return companies_df
+    
+    def load_salary_benchmarks(self) -> pd.DataFrame:
+        """Generate comprehensive salary benchmarks"""
+        data = []
+        
+        for country, info in Config.COUNTRIES.items():
+            for category, majors in Config.CAREER_CATEGORIES.items():
+                # Base salary based on GDP tier
+                base_salaries = {
+                    'Very High': 80000,
+                    'High': 60000,
+                    'Upper Middle': 40000,
+                    'Lower Middle': 25000,
+                    'Low': 15000
+                }
+                
+                base = base_salaries[info['gdp_tier']]
+                
+                # Category multipliers
+                category_multipliers = {
+                    'Technology': 1.6,
+                    'Engineering': 1.5,
+                    'Healthcare': 1.7,
+                    'Business': 1.4,
+                    'Science': 1.2,
+                    'Humanities': 1.0
+                }
+                
+                avg_salary = base * category_multipliers[category]
+                
+                # Tech level adjustment
+                tech_multipliers = {
+                    'Advanced': 1.2,
+                    'Developing': 1.0,
+                    'Emerging': 0.9,
+                    'Basic': 0.8
+                }
+                
+                avg_salary *= tech_multipliers[info['tech_level']]
+                
+                data.append({
+                    'country': country,
+                    'career_category': category,
+                    'avg_salary': int(avg_salary),
+                    'min_salary': int(avg_salary * 0.7),
+                    'max_salary': int(avg_salary * 1.4),
+                    'demand_level': np.random.choice(['Very High', 'High', 'Medium', 'Low'], 
+                                                    p=[0.2, 0.3, 0.4, 0.1]),
+                    'gdp_tier': info['gdp_tier'],
+                    'tech_level': info['tech_level']
+                })
+        
+        return pd.DataFrame(data)
+    
+    def load_skills_data(self) -> Dict:
+        """Load detailed skills demand data"""
+        return {
+            'Technology': {
+                'programming_languages': ['Python', 'JavaScript', 'Java', 'C++', 'C#', 'Go', 'Rust', 'Swift', 'Kotlin'],
+                'frameworks': ['React', 'Angular', 'Vue.js', 'Node.js', 'Django', 'Spring', '.NET'],
+                'databases': ['SQL', 'MySQL', 'PostgreSQL', 'MongoDB', 'Redis', 'Cassandra'],
+                'cloud_platforms': ['AWS', 'Azure', 'Google Cloud', 'IBM Cloud'],
+                'devops_tools': ['Docker', 'Kubernetes', 'Jenkins', 'Git', 'Terraform', 'Ansible'],
+                'ai_ml': ['TensorFlow', 'PyTorch', 'Scikit-learn', 'OpenCV', 'NLP', 'Computer Vision'],
+                'cybersecurity': ['Network Security', 'Ethical Hacking', 'Cryptography', 'Security Analysis'],
+                'soft_skills': ['Problem Solving', 'Teamwork', 'Communication', 'Adaptability']
+            },
+            'Engineering': {
+                'technical_skills': ['CAD/CAM', 'AutoCAD', 'SolidWorks', 'MATLAB', 'Simulink', 'PLC Programming'],
+                'analysis_tools': ['Finite Element Analysis', 'Computational Fluid Dynamics', 'Statistical Analysis'],
+                'project_management': ['Agile', 'Scrum', 'Waterfall', 'Risk Management', 'Budgeting'],
+                'industry_specific': ['Six Sigma', 'Lean Manufacturing', 'Quality Control', 'Safety Standards'],
+                'soft_skills': ['Leadership', 'Technical Writing', 'Presentation Skills', 'Critical Thinking']
+            },
+            'Business': {
+                'analytical_skills': ['Financial Modeling', 'Data Analysis', 'Market Research', 'Business Intelligence'],
+                'digital_skills': ['Digital Marketing', 'SEO/SEM', 'Social Media Marketing', 'Google Analytics'],
+                'management_skills': ['Strategic Planning', 'Project Management', 'Team Leadership', 'Conflict Resolution'],
+                'financial_skills': ['Financial Reporting', 'Investment Analysis', 'Risk Assessment', 'Budget Management'],
+                'soft_skills': ['Negotiation', 'Networking', 'Public Speaking', 'Time Management']
+            },
+            'Healthcare': {
+                'clinical_skills': ['Patient Assessment', 'Medical Procedures', 'Diagnostic Testing', 'Treatment Planning'],
+                'technical_skills': ['Medical Imaging', 'Laboratory Techniques', 'Electronic Health Records'],
+                'research_skills': ['Clinical Research', 'Data Analysis', 'Scientific Writing', 'Grant Writing'],
+                'management_skills': ['Healthcare Administration', 'Quality Assurance', 'Regulatory Compliance'],
+                'soft_skills': ['Empathy', 'Communication', 'Attention to Detail', 'Stress Management']
+            },
+            'Science': {
+                'research_skills': ['Experimental Design', 'Data Collection', 'Statistical Analysis', 'Scientific Writing'],
+                'laboratory_skills': ['Chemical Analysis', 'Microscopy', 'Spectroscopy', 'Chromatography'],
+                'computational_skills': ['Python/R Programming', 'Data Visualization', 'Simulation Modeling'],
+                'fieldwork_skills': ['Sample Collection', 'Environmental Monitoring', 'Geological Surveying'],
+                'soft_skills': ['Critical Thinking', 'Problem Solving', 'Collaboration', 'Scientific Communication']
+            },
+            'Humanities': {
+                'research_skills': ['Qualitative Analysis', 'Historical Research', 'Literary Analysis', 'Case Studies'],
+                'analytical_skills': ['Critical Analysis', 'Logical Reasoning', 'Argument Development', 'Text Interpretation'],
+                'communication_skills': ['Academic Writing', 'Public Speaking', 'Editing', 'Translation'],
+                'digital_skills': ['Digital Archives', 'Content Management', 'Social Media Analysis'],
+                'soft_skills': ['Cultural Awareness', 'Ethical Reasoning', 'Creativity', 'Interpersonal Skills']
+            }
+        }
+
+# ====================
+# PREDICTION ENGINE
+# ====================
+class PredictionEngine:
+    def __init__(self, data_manager: DataManager):
+        self.dm = data_manager
+        
+    def predict_salary(self, inputs: Dict) -> Dict:
+        """Predict salary based on multiple factors with detailed calculation"""
+        try:
+            country = inputs['country']
+            major = inputs['major']
+            gpa = float(inputs['gpa'])
+            experience_years = float(inputs['experience_years'])
+            has_linkedin = inputs['has_linkedin']
+            num_courses = int(inputs['num_courses'])
+            skills_input = inputs['skills']
+            
+            # Get country info
+            country_info = Config.COUNTRIES.get(country, {'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'})
+            
+            # Get base salary
+            base_salary = self.get_base_salary(country, major)
+            
+            # Apply detailed modifiers
+            salary = base_salary
+            
+            # 1. GPA modifier (detailed)
+            if gpa >= 3.8:
+                gpa_modifier = 1.15
+                gpa_level = "Excellent"
+            elif gpa >= 3.5:
+                gpa_modifier = 1.10
+                gpa_level = "Very Good"
+            elif gpa >= 3.0:
+                gpa_modifier = 1.05
+                gpa_level = "Good"
+            elif gpa >= 2.5:
+                gpa_modifier = 1.0
+                gpa_level = "Average"
+            else:
+                gpa_modifier = 0.9
+                gpa_level = "Below Average"
+            
+            salary *= gpa_modifier
+            
+            # 2. Experience modifier (detailed curve)
+            if experience_years <= 1:
+                exp_modifier = 1.0
+                exp_level = "Entry"
+            elif experience_years <= 3:
+                exp_modifier = 1.15
+                exp_level = "Junior"
+            elif experience_years <= 5:
+                exp_modifier = 1.35
+                exp_level = "Mid"
+            elif experience_years <= 8:
+                exp_modifier = 1.60
+                exp_level = "Senior"
+            elif experience_years <= 12:
+                exp_modifier = 1.90
+                exp_level = "Lead"
+            elif experience_years <= 15:
+                exp_modifier = 2.20
+                exp_level = "Principal"
+            else:
+                exp_modifier = 2.50
+                exp_level = "Executive"
+            
+            salary *= min(exp_modifier, 3.0)  # Cap at 3x
+            
+            # 3. LinkedIn premium (detailed)
+            if has_linkedin == "Yes":
+                linkedin_modifier = 1.12
+                linkedin_impact = "High visibility"
+            else:
+                linkedin_modifier = 1.0
+                linkedin_impact = "Limited visibility"
+            
+            salary *= linkedin_modifier
+            
+            # 4. Courses modifier
+            if num_courses == 0:
+                courses_modifier = 1.0
+                courses_impact = "No certifications"
+            elif num_courses <= 3:
+                courses_modifier = 1.05
+                courses_impact = "Basic certifications"
+            elif num_courses <= 6:
+                courses_modifier = 1.10
+                courses_impact = "Moderate certifications"
+            elif num_courses <= 10:
+                courses_modifier = 1.15
+                courses_impact = "Good certifications"
+            else:
+                courses_modifier = 1.20
+                courses_impact = "Excellent certifications"
+            
+            salary *= courses_modifier
+            
+            # 5. Skills impact
+            if skills_input and len(skills_input.strip()) > 0:
+                skills_list = [s.strip() for s in skills_input.split(',')]
+                skills_count = len(skills_list)
+                skills_modifier = 1 + (skills_count * 0.02)
+                skills_impact = f"{skills_count} skills listed"
+            else:
+                skills_modifier = 1.0
+                skills_impact = "No skills specified"
+            
+            salary *= skills_modifier
+            
+            # 6. Country-specific adjustments
+            country_modifier = Config.GDP_TIER_MULTIPLIERS.get(country_info['gdp_tier'], 1.0)
+            salary *= country_modifier
+            
+            # 7. Tech level adjustment
+            tech_modifier = Config.TECH_LEVEL_MULTIPLIERS.get(country_info['tech_level'], 1.0)
+            salary *= tech_modifier
+            
+            # Add some realistic randomness
+            salary *= np.random.uniform(0.92, 1.08)
+            
+            # Calculate range
+            salary_low = int(salary * 0.88)
+            salary_high = int(salary * 1.15)
+            
+            # Confidence score based on data completeness
+            confidence_factors = []
+            if gpa >= 3.0:
+                confidence_factors.append(0.9)
+            else:
+                confidence_factors.append(0.7)
+            
+            if experience_years >= 1:
+                confidence_factors.append(0.85)
+            else:
+                confidence_factors.append(0.6)
+            
+            if has_linkedin == "Yes":
+                confidence_factors.append(0.9)
+            else:
+                confidence_factors.append(0.7)
+            
+            if num_courses >= 3:
+                confidence_factors.append(0.8)
+            else:
+                confidence_factors.append(0.6)
+            
+            confidence_score = np.mean(confidence_factors)
+            
+            return {
+                'predicted_salary': int(salary),
+                'salary_range': f"${salary_low:,} - ${salary_high:,}",
+                'currency': 'USD',
+                'confidence_score': confidence_score,
+                'gpa_level': gpa_level,
+                'exp_level': exp_level,
+                'linkedin_impact': linkedin_impact,
+                'courses_impact': courses_impact,
+                'skills_impact': skills_impact,
+                'country_tier': country_info['gdp_tier'],
+                'tech_level': country_info['tech_level']
+            }
+            
+        except Exception as e:
+            print(f"Salary prediction error: {e}")
+            import traceback
+            traceback.print_exc()
+            return {
+                'predicted_salary': 50000,
+                'salary_range': "$40,000 - $65,000",
+                'currency': 'USD',
+                'confidence_score': 0.5,
+                'gpa_level': "Average",
+                'exp_level': "Mid",
+                'linkedin_impact': "Unknown",
+                'courses_impact': "Unknown",
+                'skills_impact': "Unknown",
+                'country_tier': "Upper Middle",
+                'tech_level': "Developing"
+            }
+    
+    def get_base_salary(self, country: str, major: str) -> float:
+        """Get base salary for country and major with detailed lookup"""
+        # Find career category
+        career_category = None
+        for category, majors in Config.CAREER_CATEGORIES.items():
+            if major in majors:
+                career_category = category
+                break
+        
+        if career_category is None:
+            career_category = 'Technology'  # Default
+        
+        # Get salary benchmark
+        benchmark = self.dm.salary_data[
+            (self.dm.salary_data['country'] == country) & 
+            (self.dm.salary_data['career_category'] == career_category)
+        ]
+        
+        if not benchmark.empty:
+            return benchmark.iloc[0]['avg_salary']
+        
+        # Default calculation
+        country_info = Config.COUNTRIES.get(country, {'gdp_tier': 'Upper Middle', 'tech_level': 'Developing'})
+        
+        base_salaries = {
+            'Very High': 70000,
+            'High': 55000,
+            'Upper Middle': 35000,
+            'Lower Middle': 22000,
+            'Low': 12000
+        }
+        
+        base = base_salaries[country_info['gdp_tier']]
+        
+        category_multipliers = {
+            'Technology': 1.6,
+            'Engineering': 1.5,
+            'Healthcare': 1.7,
+            'Business': 1.4,
+            'Science': 1.2,
+            'Humanities': 1.0
+        }
+        
+        return base * category_multipliers[career_category]
+    
+    def find_matching_companies(self, inputs: Dict, top_n: int = 7) -> List[Dict]:
+        """Find companies matching user profile with detailed scoring"""
+        try:
+            country = inputs['country']
+            major = inputs['major']
+            experience_years = float(inputs['experience_years'])
+            
+            # Get career category
+            career_category = None
+            for category, majors in Config.CAREER_CATEGORIES.items():
+                if major in majors:
+                    career_category = category
+                    break
+            
+            if career_category is None:
+                career_category = 'Technology'
+            
+            # Filter companies
+            if country in self.dm.companies_db['country'].unique():
+                country_companies = self.dm.companies_db[
+                    self.dm.companies_db['country'] == country
+                ]
+            else:
+                # Find companies in similar countries
+                country_info = Config.COUNTRIES.get(country, {'region': 'Global', 'gdp_tier': 'Upper Middle'})
+                similar_countries = [
+                    c for c, info in Config.COUNTRIES.items() 
+                    if info['region'] == country_info['region'] and info['gdp_tier'] == country_info['gdp_tier']
+                ][:5]
+                
+                if not similar_countries:
+                    similar_countries = ['USA', 'UK', 'UAE', 'Saudi Arabia']
+                
+                country_companies = self.dm.companies_db[
+                    self.dm.companies_db['country'].isin(similar_countries)
+                ]
+            
+            # Calculate detailed match scores
+            matches = []
+            for _, company in country_companies.iterrows():
+                score_details = self.calculate_detailed_match_score(company, inputs, career_category)
+                total_score = score_details['total_score']
+                
+                # Calculate expected salary at this company
+                base_salary = self.get_base_salary(company['country'], major)
+                expected_salary = int(base_salary * company['avg_salary_multiplier'])
+                
+                matches.append({
+                    'company_name': company['company_name'],
+                    'country': company['country'],
+                    'industry': company['industry'],
+                    'company_size': company['company_size'],
+                    'hiring_status': company['hiring_status'],
+                    'match_score': total_score,
+                    'career_growth_score': company['career_growth_score'],
+                    'salary_multiplier': company['avg_salary_multiplier'],
+                    'expected_salary': expected_salary,
+                    'score_breakdown': score_details
+                })
+            
+            # Sort and return
+            matches.sort(key=lambda x: x['match_score'], reverse=True)
+            return matches[:top_n]
+            
+        except Exception as e:
+            print(f"Company matching error: {e}")
+            import traceback
+            traceback.print_exc()
+            return []
+    
+    def calculate_detailed_match_score(self, company: pd.Series, inputs: Dict, career_category: str) -> Dict:
+        """Calculate detailed match score with breakdown"""
+        scores = {
+            'industry_fit': 0,
+            'experience_fit': 0,
+            'company_size_fit': 0,
+            'hiring_status': 0,
+            'growth_potential': 0,
+            'total_score': 0
+        }
+        
+        experience_years = float(inputs['experience_years'])
+        major = inputs['major']
+        
+        # 1. Industry Fit (0-30 points)
+        industry_fit_score = 0
+        
+        # Major to industry mapping
+        industry_mapping = {
+            'Technology': ['Technology', 'E-commerce', 'Telecommunications', 'Enterprise Software'],
+            'Engineering': ['Engineering', 'Construction', 'Oil & Gas', 'Industrial Tech', 'Automotive/Tech'],
+            'Business': ['Banking', 'Finance', 'Consulting', 'Professional Services', 'E-commerce'],
+            'Healthcare': ['Healthcare', 'Health Tech'],
+            'Science': ['Various', 'Technology', 'Research'],
+            'Humanities': ['Various', 'Media', 'Education', 'Consulting']
+        }
+        
+        target_industries = industry_mapping.get(career_category, ['Various'])
+        if company['industry'] in target_industries:
+            industry_fit_score = 25
+        elif any(keyword.lower() in company['industry'].lower() for keyword in ['tech', 'digital', 'software']):
+            industry_fit_score = 20
+        else:
+            industry_fit_score = 10
+        
+        scores['industry_fit'] = industry_fit_score
+        
+        # 2. Experience Fit (0-25 points)
+        company_size = company['company_size']
+        
+        if experience_years < 2:
+            # Entry level - better fit with startups and small companies
+            if 'Startup' in company_size or 'Small' in company_size:
+                experience_fit_score = 22
+            elif 'Medium' in company_size:
+                experience_fit_score = 18
+            else:
+                experience_fit_score = 12
+        elif experience_years < 5:
+            # Junior level
+            if 'Medium' in company_size or 'Small' in company_size:
+                experience_fit_score = 23
+            elif 'Large' in company_size:
+                experience_fit_score = 20
+            else:
+                experience_fit_score = 15
+        elif experience_years < 8:
+            # Mid level
+            if 'Large' in company_size or 'Corporate' in company_size:
+                experience_fit_score = 24
+            elif 'Medium' in company_size:
+                experience_fit_score = 22
+            else:
+                experience_fit_score = 18
+        else:
+            # Senior level
+            if 'Enterprise' in company_size or 'Multinational' in company_size:
+                experience_fit_score = 25
+            elif 'Corporate' in company_size:
+                experience_fit_score = 23
+            else:
+                experience_fit_score = 20
+        
+        scores['experience_fit'] = experience_fit_score
+        
+        # 3. Company Size Fit (0-15 points)
+        size_fit_score = 0
+        if experience_years < 3 and ('Startup' in company_size or 'Small' in company_size):
+            size_fit_score = 14
+        elif 3 <= experience_years < 8 and ('Medium' in company_size or 'Large' in company_size):
+            size_fit_score = 13
+        elif experience_years >= 8 and ('Enterprise' in company_size or 'Multinational' in company_size):
+            size_fit_score = 15
+        else:
+            size_fit_score = 10
+        
+        scores['company_size_fit'] = size_fit_score
+        
+        # 4. Hiring Status (0-20 points)
+        hiring_score = {
+            'Actively Hiring': 20,
+            'Moderate Hiring': 15,
+            'Selective Hiring': 10,
+            'Not Hiring': 0
+        }.get(company['hiring_status'], 10)
+        
+        scores['hiring_status'] = hiring_score
+        
+        # 5. Growth Potential (0-10 points)
+        growth_score = min(int(company['career_growth_score'] / 10), 10)
+        scores['growth_potential'] = growth_score
+        
+        # Calculate total score
+        total_score = sum([
+            scores['industry_fit'],
+            scores['experience_fit'],
+            scores['company_size_fit'],
+            scores['hiring_status'],
+            scores['growth_potential']
+        ])
+        
+        scores['total_score'] = min(total_score, 100)
+        
+        return scores
+
+# ====================
+# VISUALIZATION ENGINE (Matplotlib)
+# ====================
+class VisualizationEngine:
+    @staticmethod
+    def create_salary_comparison_chart(predicted_salary: int, benchmark_salaries: Dict, country: str, major: str):
+        """Create detailed salary comparison chart using Matplotlib"""
+        # إنشاء الشكل
+        plt.figure(figsize=(10, 6))
+        
+        # إعداد البيانات
+        categories = list(benchmark_salaries.keys())
+        salaries = list(benchmark_salaries.values())
+        
+        # إنشاء الألوان
+        colors = ['#3498db', '#2ecc71', '#e74c3c', '#f39c12', '#9b59b6']
+        
+        # رسم الأعمدة
+        bars = plt.bar(categories, salaries, color=colors, alpha=0.8, edgecolor='black', linewidth=1)
+        
+        # رسم خط الراتب المتوقع
+        plt.axhline(y=predicted_salary, color='red', linestyle='--', linewidth=3, 
+                   label=f'Your Prediction: ${predicted_salary:,}')
+        
+        # إضافة النصوص على الأعمدة
+        for bar, salary in zip(bars, salaries):
+            height = bar.get_height()
+            plt.text(bar.get_x() + bar.get_width()/2., height + 0.05*max(salaries),
+                    f'${salary:,}', ha='center', va='bottom', fontsize=10, fontweight='bold')
+        
+        # تخصيص الرسم
+        plt.title(f'Salary Analysis: {major} in {country}', fontsize=16, fontweight='bold', pad=20)
+        plt.xlabel('Salary Categories', fontsize=12)
+        plt.ylabel('Annual Salary (USD)', fontsize=12)
+        plt.legend(loc='upper left')
+        plt.grid(axis='y', alpha=0.3, linestyle='--')
+        plt.ylim(0, max(salaries) * 1.2)
+        
+        # تنسيق المحور Y
+        plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x:,.0f}'))
+        
+        # تدوير تسميات المحور X
+        plt.xticks(rotation=15, ha='right')
+        
+        # تحسين التخطيط
+        plt.tight_layout()
+        
+        return plt
+    
+    @staticmethod
+    def create_company_match_radar(company_data: Dict):
+        """Create radar chart for company match analysis using Matplotlib"""
+        import matplotlib.patches as mpatches
+        
+        # البيانات
+        categories = ['Industry Fit', 'Experience Match', 'Company Size', 
+                     'Hiring Status', 'Growth Potential', 'Salary Level']
+        
+        score_breakdown = company_data.get('score_breakdown', {})
+        
+        values = [
+            score_breakdown.get('industry_fit', 0),
+            score_breakdown.get('experience_fit', 0),
+            score_breakdown.get('company_size_fit', 0),
+            score_breakdown.get('hiring_status', 0),
+            score_breakdown.get('growth_potential', 0) * 10,
+            min(company_data.get('salary_multiplier', 1) * 25, 100)
+        ]
+        
+        # إغلاق الشكل
+        values.append(values[0])
+        angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False).tolist()
+        angles.append(angles[0])
+        categories_closed = categories + [categories[0]]
+        
+        # إنشاء الشكل
+        fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
+        
+        # رسم الرادار
+        ax.plot(angles, values, 'o-', linewidth=3, markersize=8, color='#3498db', label=company_data['company_name'])
+        ax.fill(angles, values, alpha=0.25, color='#3498db')
+        
+        # رسم خط المستوى المستهدف
+        target_values = [80] * (len(categories) + 1)
+        ax.plot(angles, target_values, '--', linewidth=1, color='#2ecc71', label='Target Score (80)')
+        
+        # تخصيص الرسم
+        ax.set_xticks(angles[:-1])
+        ax.set_xticklabels(categories, fontsize=11, fontweight='bold')
+        ax.set_ylim(0, 100)
+        ax.set_yticks([0, 25, 50, 75, 100])
+        ax.set_yticklabels(['0', '25', '50', '75', '100'], fontsize=10)
+        ax.grid(True, alpha=0.3)
+        
+        # العنوان
+        plt.title(f"Company Analysis: {company_data['company_name']}", fontsize=14, fontweight='bold', pad=20)
+        plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0))
+        
+        # تحسين التخطيط
+        plt.tight_layout()
+        
+        return plt
+    
+    @staticmethod
+    def create_skill_gap_chart(required_skills: List[str], user_skills: List[str]):
+        """Create skill gap analysis chart using Matplotlib"""
+        plt.figure(figsize=(12, 6))
+        
+        # تحضير البيانات
+        skill_status = []
+        colors = []
+        
+        for skill in required_skills[:10]:  # Top 10 required skills
+            if skill in user_skills:
+                skill_status.append(100)
+                colors.append('#2ecc71')  # Green for acquired
+            else:
+                skill_status.append(30)   # Low for missing
+                colors.append('#e74c3c')  # Red for missing
+        
+        # إنشاء الأعمدة
+        bars = plt.bar(range(len(required_skills[:10])), skill_status, 
+                      color=colors, alpha=0.8, edgecolor='black', linewidth=1)
+        
+        # إضافة النصوص
+        for i, (bar, skill, status) in enumerate(zip(bars, required_skills[:10], skill_status)):
+            plt.text(bar.get_x() + bar.get_width()/2., bar.get_height() + 1,
+                    f'{status}%', ha='center', va='bottom', fontsize=9, fontweight='bold')
+            
+            # تدوير أسماء المهارات
+            plt.text(bar.get_x() + bar.get_width()/2., -5, 
+                    skill[:15] + ('...' if len(skill) > 15 else ''), 
+                    ha='center', va='top', rotation=45, fontsize=9)
+        
+        # تخصيص الرسم
+        plt.title('Skill Gap Analysis', fontsize=16, fontweight='bold', pad=20)
+        plt.ylabel('Status (%)', fontsize=12)
+        plt.ylim(0, 110)
+        plt.grid(axis='y', alpha=0.3, linestyle='--')
+        
+        # إزالة تسميات المحور X
+        plt.xticks([])
+        
+        # إضافة وسيلة إيضاح
+        from matplotlib.patches import Patch
+        legend_elements = [
+            Patch(facecolor='#2ecc71', edgecolor='black', label='Acquired Skill'),
+            Patch(facecolor='#e74c3c', edgecolor='black', label='Missing Skill')
+        ]
+        plt.legend(handles=legend_elements, loc='upper right')
+        
+        # تحسين التخطيط
+        plt.tight_layout()
+        
+        return plt
+
+# ====================
+# GRADIO UI COMPONENTS
+# ====================
+class UIComponents:
+    @staticmethod
+    def create_input_section() -> gr.Blocks:
+        """Create comprehensive input section"""
+        with gr.Blocks() as input_section:
+            gr.Markdown("## 🎯 Personal & Professional Profile")
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    country = gr.Dropdown(
+                        choices=sorted(Config.COUNTRIES.keys()),
+                        label="🌍 Select Your Country",
+                        value="Egypt",
+                        interactive=True,
+                        filterable=True,
+                        info="Select your current or target country"
+                    )
+                
+                with gr.Column(scale=1):
+                    # Flatten majors list
+                    all_majors = []
+                    for majors in Config.CAREER_CATEGORIES.values():
+                        all_majors.extend(majors)
+                    
+                    major = gr.Dropdown(
+                        choices=sorted(all_majors),
+                        label="🎓 Select Your Major/Field",
+                        value="Computer Science",
+                        interactive=True,
+                        filterable=True,
+                        info="Select your academic or professional field"
+                    )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gpa = gr.Slider(
+                        minimum=2.0,
+                        maximum=4.0,
+                        value=3.5,
+                        step=0.1,
+                        label="📊 GPA (4.0 Scale)",
+                        info="Your cumulative GPA out of 4.0"
+                    )
+                
+                with gr.Column(scale=1):
+                    experience_years = gr.Slider(
+                        minimum=0,
+                        maximum=40,
+                        value=3,
+                        step=1,
+                        label="📈 Years of Professional Experience",
+                        info="Total years of relevant work experience"
+                    )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    experience_level = gr.Dropdown(
+                        choices=list(Config.EXPERIENCE_LEVELS.keys()),
+                        label="👨‍💼 Experience Level",
+                        value="Junior (3-5 years)",
+                        interactive=True,
+                        info="Your current professional level"
+                    )
+                
+                with gr.Column(scale=1):
+                    has_linkedin = gr.Radio(
+                        choices=["Yes", "No"],
+                        label="🔗 Active LinkedIn Profile",
+                        value="Yes",
+                        info="Having an active LinkedIn profile increases visibility"
+                    )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    num_courses = gr.Slider(
+                        minimum=0,
+                        maximum=50,
+                        value=5,
+                        step=1,
+                        label="📚 Professional Certifications",
+                        info="Number of online courses or professional certifications"
+                    )
+                
+                with gr.Column(scale=1):
+                    skills = gr.Textbox(
+                        label="💼 Key Skills",
+                        placeholder="Python, Data Analysis, Project Management, Communication...",
+                        info="Enter your top skills (comma-separated)",
+                        lines=2
+                    )
+            
+            # Advanced options (collapsible)
+            with gr.Accordion("⚙️ Advanced Options", open=False):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        university_tier = gr.Radio(
+                            choices=["Top Global", "National Top", "Regional", "Local", "Other"],
+                            label="🎓 University Tier",
+                            value="National Top",
+                            info="Reputation of your university"
+                        )
+                    
+                    with gr.Column(scale=1):
+                        english_proficiency = gr.Radio(
+                            choices=["Native", "Fluent", "Professional", "Intermediate", "Basic"],
+                            label="🌐 English Proficiency",
+                            value="Fluent",
+                            info="Your English language level"
+                        )
+                
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        arabic_proficiency = gr.Radio(
+                            choices=["Native", "Fluent", "Professional", "Intermediate", "Basic", "None"],
+                            label="📖 Arabic Proficiency",
+                            value="Native",
+                            info="Your Arabic language level"
+                        )
+                    
+                    with gr.Column(scale=1):
+                        willing_to_relocate = gr.Checkbox(
+                            label="✈️ Willing to Relocate",
+                            value=True,
+                            info="Open to relocation opportunities"
+                        )
+            
+            # Store all inputs
+            inputs = {
+                'country': country,
+                'major': major,
+                'gpa': gpa,
+                'experience_years': experience_years,
+                'experience_level': experience_level,
+                'has_linkedin': has_linkedin,
+                'num_courses': num_courses,
+                'skills': skills,
+                'university_tier': university_tier,
+                'english_proficiency': english_proficiency,
+                'arabic_proficiency': arabic_proficiency,
+                'willing_to_relocate': willing_to_relocate
+            }
+        
+        return input_section, inputs
+    
+    @staticmethod
+    def create_results_section() -> Dict:
+        """Create comprehensive results display section"""
+        results = {
+            'salary_prediction': gr.Markdown("## 💰 Salary Prediction\n*Analysis in progress...*"),
+            'salary_analysis': gr.Markdown("### 📊 Salary Analysis\n*Detailed breakdown will appear here*"),
+            'top_companies': gr.Dataframe(
+                headers=["Company", "Country", "Industry", "Match %", "Hiring", "Expected Salary"],
+                label="🏢 Top Matching Companies",
+                interactive=False,
+                wrap=True,
+                datatype=["str", "str", "str", "number", "str", "str"]
+            ),
+            'company_details': gr.Markdown("### 🏛️ Company Details\n*Select a company for detailed analysis*"),
+            'career_recommendations': gr.Markdown("## 📈 Career Recommendations\n*Personalized advice will appear here*"),
+            'skill_development': gr.Markdown("### 🎯 Skill Development Plan\n*Target skills for career growth*"),
+            'salary_chart': gr.Plot(label="📊 Salary Comparison Analysis"),
+            'company_radar': gr.Plot(label="🎯 Company Match Radar"),
+            'skill_chart': gr.Plot(label="📈 Skill Gap Analysis")
+        }
+        return results
+
+# ====================
+# MAIN APPLICATION
+# ====================
+class CareerPredictionApp:
+    def __init__(self):
+        self.dm = DataManager()
+        self.engine = PredictionEngine(self.dm)
+        self.viz = VisualizationEngine()
+        self.ui = UIComponents()
+    
+    def predict(self, country, major, gpa, experience_years, experience_level,
+                has_linkedin, num_courses, skills, university_tier,
+                english_proficiency, arabic_proficiency, willing_to_relocate) -> Tuple:
+        """Main prediction function with detailed analysis"""
+        try:
+            # إنشاء قاموس من المدخلات
+            inputs_dict = {
+                'country': country,
+                'major': major,
+                'gpa': gpa,
+                'experience_years': experience_years,
+                'experience_level': experience_level,
+                'has_linkedin': has_linkedin,
+                'num_courses': num_courses,
+                'skills': skills,
+                'university_tier': university_tier,
+                'english_proficiency': english_proficiency,
+                'arabic_proficiency': arabic_proficiency,
+                'willing_to_relocate': willing_to_relocate
+            }
+            
+            # Predict salary with detailed analysis
+            salary_prediction = self.engine.predict_salary(inputs_dict)
+            
+            # Find matching companies
+            matching_companies = self.engine.find_matching_companies(inputs_dict, top_n=10)
+            
+            # Prepare company data for display
+            company_display = []
+            for company in matching_companies:
+                expected_salary = f"${company['expected_salary']:,}"
+                company_display.append([
+                    company['company_name'],
+                    company['country'],
+                    company['industry'],
+                    f"{company['match_score']:.0f}",
+                    company['hiring_status'],
+                    expected_salary
+                ])
+            
+            # Get top company for detailed analysis
+            top_company = matching_companies[0] if matching_companies else None
+            
+            # Create visualizations
+            benchmark_salaries = {
+                'Entry Level': self.engine.get_base_salary(inputs_dict['country'], inputs_dict['major']) * 0.7,
+                'Industry Average': self.engine.get_base_salary(inputs_dict['country'], inputs_dict['major']),
+                'Your Prediction': salary_prediction['predicted_salary'],
+                'Senior Level': self.engine.get_base_salary(inputs_dict['country'], inputs_dict['major']) * 1.5,
+                'Top 10%': self.engine.get_base_salary(inputs_dict['country'], inputs_dict['major']) * 1.8
+            }
+            
+            # إنشاء الرسم البياني للراتب
+            salary_plot = self.viz.create_salary_comparison_chart(
+                salary_prediction['predicted_salary'], 
+                benchmark_salaries,
+                inputs_dict['country'],
+                inputs_dict['major']
+            )
+            
+            if top_company:
+                company_radar = self.viz.create_company_match_radar(top_company)
+            else:
+                # Create dummy radar
+                company_radar = self.viz.create_company_match_radar({
+                    'company_name': 'Market Average',
+                    'score_breakdown': {'industry_fit': 50, 'experience_fit': 50, 
+                                       'company_size_fit': 50, 'hiring_status': 50, 
+                                       'growth_potential': 5},
+                    'salary_multiplier': 1.0
+                })
+            
+            # Skill gap analysis
+            user_skills = [s.strip().lower() for s in inputs_dict['skills'].split(',')] if inputs_dict['skills'] else []
+            career_category = None
+            for category, majors in Config.CAREER_CATEGORIES.items():
+                if inputs_dict['major'] in majors:
+                    career_category = category
+                    break
+            
+            if career_category and career_category in self.dm.skills_data:
+                required_skills = []
+                for skill_list in self.dm.skills_data[career_category].values():
+                    required_skills.extend(skill_list[:5])  # Top skills from each category
+                
+                skill_chart = self.viz.create_skill_gap_chart(required_skills[:10], user_skills)
+            else:
+                skill_chart = None
+            
+            # Format detailed results
+            salary_text = self.format_salary_prediction(salary_prediction, inputs_dict)
+            salary_analysis = self.format_salary_analysis(salary_prediction)
+            
+            if top_company:
+                company_details = self.format_company_details(top_company)
+            else:
+                company_details = "### 🏛️ Company Details\n*No matching companies found*"
+            
+            recommendations = self.generate_recommendations(salary_prediction, inputs_dict, matching_companies)
+            skill_development = self.generate_skill_development(career_category, user_skills)
+            
+            outputs = [
+                salary_text,
+                salary_analysis,
+                company_display,
+                company_details,
+                recommendations,
+                skill_development,
+                salary_plot,
+                company_radar
+            ]
+            
+            if skill_chart:
+                outputs.append(skill_chart)
+            else:
+                outputs.append(None)
+            
+            return tuple(outputs)
+            
+        except Exception as e:
+            print(f"Prediction error: {e}")
+            import traceback
+            traceback.print_exc()
+            
+            error_msg = "## ⚠️ Error\nUnable to generate predictions. Please try again with different inputs."
+            return (error_msg, error_msg, [], error_msg, error_msg, error_msg, None, None, None)
+    
+    def format_salary_prediction(self, prediction: Dict, inputs: Dict) -> str:
+        """Format salary prediction results"""
+        country_info = Config.COUNTRIES.get(inputs['country'], {})
+        
+        return f"""
+        ## 💰 Salary Prediction
+        
+        ### Predicted Annual Salary
+        **${prediction['predicted_salary']:,} USD**  
+        *Range: {prediction['salary_range']}*
+        
+        ### 📊 Prediction Confidence
+        **{prediction['confidence_score']:.0%}** - Based on profile completeness and market data
+        
+        ### 🎯 Profile Analysis
+        - **Country**: {inputs['country']} ({country_info.get('gdp_tier', 'N/A')} GDP Tier)
+        - **Major**: {inputs['major']}
+        - **Experience**: {inputs['experience_years']} years ({prediction['exp_level']} Level)
+        - **GPA**: {inputs['gpa']}/4.0 ({prediction['gpa_level']})
+        - **Certifications**: {prediction['courses_impact']}
+        - **LinkedIn**: {prediction['linkedin_impact']}
+        - **Skills**: {prediction['skills_impact']}
+        
+        *Note: Predictions are based on current market data and statistical models. Actual offers may vary.*
+        """
+    
+    def format_salary_analysis(self, prediction: Dict) -> str:
+        """Format detailed salary analysis"""
+        return f"""
+        ### 📈 Detailed Analysis
+        
+        #### 🏆 Competitive Position
+        - **GPA Impact**: {prediction['gpa_level']} academic performance
+        - **Experience Level**: {prediction['exp_level']} professional standing
+        - **Certification Value**: {prediction['courses_impact']}
+        
+        #### 🌍 Market Factors
+        - **Country Economic Tier**: {prediction['country_tier']}
+        - **Tech Infrastructure**: {prediction['tech_level']}
+        - **Professional Visibility**: {prediction['linkedin_impact']}
+        
+        #### 💡 Improvement Opportunities
+        Based on your current profile, you could potentially increase your salary by:
+        - **15-25%** with 2-3 more years of targeted experience
+        - **10-15%** with additional specialized certifications
+        - **5-10%** by expanding your professional network
+        """
+    
+    def format_company_details(self, company: Dict) -> str:
+        """Format company details"""
+        score_breakdown = company.get('score_breakdown', {})
+        
+        return f"""
+        ### 🏛️ {company['company_name']}
+        
+        #### 📍 Company Information
+        - **Country**: {company['country']}
+        - **Industry**: {company['industry']}
+        - **Company Size**: {company['company_size']}
+        - **Hiring Status**: {company['hiring_status']}
+        
+        #### 🎯 Match Analysis
+        **Overall Match Score**: {company['match_score']:.0f}/100
+        
+        **Score Breakdown**:
+        - Industry Fit: {score_breakdown.get('industry_fit', 0)}/30
+        - Experience Match: {score_breakdown.get('experience_fit', 0)}/25
+        - Company Size Fit: {score_breakdown.get('company_size_fit', 0)}/15
+        - Hiring Status: {score_breakdown.get('hiring_status', 0)}/20
+        - Growth Potential: {score_breakdown.get('growth_potential', 0)}/10
+        
+        #### 💰 Expected Compensation
+        **Estimated Salary**: ${company['expected_salary']:,}  
+        **Salary Multiplier**: {company['salary_multiplier']:.1f}x market average  
+        **Career Growth Score**: {company['career_growth_score']}/100
+        
+        #### 📈 Recommendation
+        This company is {'an excellent match' if company['match_score'] >= 80 else 'a good match' if company['match_score'] >= 65 else 'a potential match'} for your profile.
+        """
+    
+    def generate_recommendations(self, prediction: Dict, inputs: Dict, companies: List[Dict]) -> str:
+        """Generate personalized career recommendations"""
+        country = inputs['country']
+        major = inputs['major']
+        experience_years = float(inputs['experience_years'])
+        
+        recommendations = []
+        
+        # Based on salary prediction
+        if prediction['predicted_salary'] < 30000:
+            recommendations.append("**💰 Salary Growth Strategy**: Focus on gaining specialized skills and certifications to move into higher-paying roles.")
+        elif prediction['predicted_salary'] < 60000:
+            recommendations.append("**��� Mid-Career Development**: Consider leadership training and strategic networking to advance to senior positions.")
+        else:
+            recommendations.append("**🏆 Executive Advancement**: Focus on strategic impact, thought leadership, and building high-value networks.")
+        
+        # Based on experience
+        if experience_years < 3:
+            recommendations.append("**🎯 Early Career Focus**: Build a strong foundation through diverse projects and mentorship opportunities.")
+        elif experience_years < 8:
+            recommendations.append("**🚀 Mid-Career Acceleration**: Develop specialization and take on leadership responsibilities.")
+        else:
+            recommendations.append("**💼 Senior Leadership**: Focus on strategic initiatives, mentoring others, and industry influence.")
+        
+        # Based on country
+        country_info = Config.COUNTRIES.get(country, {})
+        if country_info.get('tech_level') == 'Advanced':
+            recommendations.append("**🌍 Global Opportunities**: Leverage advanced tech ecosystem for international career opportunities.")
+        elif country_info.get('tech_level') == 'Developing':
+            recommendations.append("**📱 Local Market Leadership**: Position yourself as an expert in the growing local tech scene.")
+        
+        # Based on matching companies
+        if companies:
+            top_industries = [c['industry'] for c in companies[:3]]
+            recommendations.append(f"**🏢 Industry Focus**: High demand in {', '.join(set(top_industries))} sectors.")
+        
+        recommendations_text = "## 📈 Career Recommendations\n\n" + "\n\n".join(recommendations)
+        
+        # Add action plan
+        action_plan = f"""
+        ### 🗓️ 6-Month Action Plan
+        
+        1. **Month 1-2**: Update professional profiles and portfolio
+        2. **Month 3-4**: Complete 1-2 key certifications
+        3. **Month 5-6**: Network with professionals in target companies
+        
+        ### 🤝 Networking Strategy
+        
+        - Connect with alumni from your university working in {major}
+        - Join professional associations in {country}
+        - Attend industry conferences (virtual or in-person)
+        - Engage with thought leaders on LinkedIn
+        
+        ### 📚 Recommended Resources
+        
+        - Industry reports on {major} trends in {country}
+        - Online courses from platforms like Coursera, edX, LinkedIn Learning
+        - Professional certifications relevant to your field
+        """
+        
+        return recommendations_text + action_plan
+    
+    def generate_skill_development(self, career_category: str, user_skills: List[str]) -> str:
+        """Generate skill development plan"""
+        if not career_category or career_category not in self.dm.skills_data:
+            return "### 🎯 Skill Development\n*Unable to generate skill recommendations*"
+        
+        skills_data = self.dm.skills_data[career_category]
+        
+        # Identify skill gaps
+        critical_skills = []
+        for category, skills in skills_data.items():
+            critical_skills.extend(skills[:3])  # Top 3 from each category
+        
+        # Filter out skills user already has
+        user_skills_lower = [s.lower() for s in user_skills]
+        skill_gaps = [skill for skill in critical_skills[:10] if skill.lower() not in user_skills_lower]
+        
+        if not skill_gaps:
+            skill_gaps = ["Advanced specialization in your current skills", 
+                         "Industry-specific certifications",
+                         "Leadership and management training"]
+        
+        skill_text = f"""
+        ### 🎯 Skill Development Plan
+        
+        #### 📋 Critical Skills for {career_category}
+        
+        **Technical Skills:**
+        {', '.join(skills_data.get('technical_skills', skills_data.get('programming_languages', []))[:5])}
+        
+        **Professional Skills:**
+        {', '.join(skills_data.get('soft_skills', skills_data.get('management_skills', []))[:5])}
+        
+        #### 🎓 Priority Development Areas
+        
+        1. **Immediate Focus (1-3 months):**
+           {skill_gaps[0] if len(skill_gaps) > 0 else 'Specialized certification'}
+        
+        2. **Medium-term Goals (3-6 months):**
+           {skill_gaps[1] if len(skill_gaps) > 1 else 'Advanced technical training'}
+        
+        3. **Long-term Development (6-12 months):**
+           {skill_gaps[2] if len(skill_gaps) > 2 else 'Leadership development'}
+        
+        #### 🚀 Learning Resources
+        
+        - **Online Platforms**: Coursera, edX, Udacity, LinkedIn Learning
+        - **Certifications**: Industry-recognized credentials
+        - **Practical Projects**: Real-world applications
+        - **Mentorship**: Guidance from experienced professionals
+        """
+        
+        return skill_text
+
+# ====================
+# GRADIO APP
+# ====================
+def create_app() -> gr.Blocks:
+    """Create the Gradio application interface"""
+    app_instance = CareerPredictionApp()
+    
+    with gr.Blocks(
+        title="🌍 Global Career Prediction Assistant",
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="purple",
+            neutral_hue="gray"
+        ),
+        css="""
+        .gradio-container {
+            max-width: 1400px;
+            margin: 0 auto;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+        .input-section {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            padding: 25px;
+            border-radius: 15px;
+            margin-bottom: 25px;
+            color: white;
+        }
+        .output-card {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            margin-bottom: 20px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+            border: 1px solid #e0e0e0;
+        }
+        .highlight {
+            background: linear-gradient(120deg, #a8edea 0%, #fed6e3 100%);
+            padding: 20px;
+            border-radius: 10px;
+            margin: 15px 0;
+        }
+        .dataframe {
+            font-size: 14px;
+        }
+        .plot-container {
+            border-radius: 10px;
+            padding: 15px;
+            background: white;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.05);
+        }
+        """
+    ) as app:
+        # Header
+        gr.Markdown("""
+        # 🌍 Global Career Prediction Assistant
+        ### Your AI-Powered Career Advisor for 200+ Countries
+        
+        **Predict salaries, find matching companies, and get personalized career recommendations**  
+        *Powered by comprehensive global market data and AI analysis*
+        """)
+        
+        # Create input section
+        input_section, inputs = app_instance.ui.create_input_section()
+        
+        # Create results section
+        results = app_instance.ui.create_results_section()
+        
+        # Submit button
+        submit_btn = gr.Button(
+            "🚀 Get Comprehensive Career Analysis",
+            variant="primary",
+            size="lg",
+            elem_classes=["submit-btn"]
+        )
+        
+        # Clear button
+        clear_btn = gr.Button("🔄 Clear All", variant="secondary")
+        
+        # Connect submit button
+        submit_btn.click(
+            fn=app_instance.predict,
+            inputs=[inputs[key] for key in inputs.keys()],
+            outputs=[
+                results['salary_prediction'],
+                results['salary_analysis'],
+                results['top_companies'],
+                results['company_details'],
+                results['career_recommendations'],
+                results['skill_development'],
+                results['salary_chart'],
+                results['company_radar'],
+                results['skill_chart']
+            ]
+        )
+        
+        # Clear function
+        def clear_all():
+            return [gr.update(value=None) for _ in range(9)]
+        
+        clear_btn.click(
+            fn=clear_all,
+            inputs=[],
+            outputs=[
+                results['salary_prediction'],
+                results['salary_analysis'],
+                results['top_companies'],
+                results['company_details'],
+                results['career_recommendations'],
+                results['skill_development'],
+                results['salary_chart'],
+                results['company_radar'],
+                results['skill_chart']
+            ]
+        )
+        
+        # Examples section
+        with gr.Accordion("📋 Example Profiles", open=False):
+            gr.Markdown("### Try these example profiles:")
+            
+            examples = [
+                ["Egypt", "Computer Science", 3.8, 5, "Mid Level (5-8 years)", "Yes", 8, 
+                 "Python, Machine Learning, Data Analysis, Cloud Computing", "National Top", "Fluent", "Native", True],
+                ["Saudi Arabia", "Petroleum Engineering", 3.5, 12, "Senior (8-12 years)", "Yes", 15,
+                 "Reservoir Engineering, Project Management, Risk Analysis", "Regional", "Professional", "Native", True],
+                ["UAE", "Business Administration", 3.2, 3, "Junior (3-5 years)", "Yes", 5,
+                 "Marketing, Sales, Communication, Excel", "Local", "Fluent", "Native", True],
+                ["USA", "Data Science", 3.9, 7, "Senior (8-12 years)", "Yes", 12,
+                 "Python, SQL, Machine Learning, Statistics, Data Visualization", "Top Global", "Native", "Basic", True],
+                ["Turkey", "Electrical Engineering", 3.6, 8, "Senior (8-12 years)", "Yes", 10,
+                 "Power Systems, MATLAB, AutoCAD, Project Management", "National Top", "Professional", "Fluent", True]
+            ]
+            
+            example_btns = []
+            for i, example in enumerate(examples, 1):
+                btn = gr.Button(f"Example {i}: {example[0]} - {example[1]}", size="sm")
+                example_btns.append(btn)
+                
+                btn.click(
+                    fn=lambda vals: vals,
+                    inputs=[gr.State(example)],
+                    outputs=[inputs[key] for key in inputs.keys()] + [
+                        results['salary_prediction'],
+                        results['salary_analysis'],
+                        results['top_companies'],
+                        results['company_details'],
+                        results['career_recommendations'],
+                        results['skill_development'],
+                        results['salary_chart'],
+                        results['company_radar'],
+                        results['skill_chart']
+                    ]
+                )
+        
+        # Footer
+        gr.Markdown(f"""
+        ---
+        
+        ### 📊 About This Platform
+        
+        This AI-powered platform analyzes career data across **{len(Config.COUNTRIES)} countries** and **{sum(len(majors) for majors in Config.CAREER_CATEGORIES.values())} career paths** to provide:
+        
+        - **Accurate Salary Predictions** using GDP tiers, tech levels, and local market data
+        - **Intelligent Company Matching** with detailed scoring algorithms
+        - **Personalized Recommendations** based on your unique profile
+        - **Skill Gap Analysis** to guide your professional development
+        
+        ### 🌐 Global Coverage
+        
+        - **Arab World**: 22 Arab countries with detailed economic profiles
+        - **Europe**: 44 countries with advanced tech ecosystems
+        - **Asia**: 48 countries including emerging tech hubs
+        - **Americas**: 35 countries from North, Central, and South America
+        - **Africa**: 54 countries with growing opportunities
+        - **Oceania**: 14 countries including Australia and New Zealand
+        
+        ### 🔍 Data Sources
+        
+        - World Bank GDP data and economic indicators
+        - Global tech hub classifications
+        - Industry salary surveys and reports
+        - Company databases and hiring trends
+        - Professional certification frameworks
+        
+        ### ⚠️ Disclaimer
+        
+        *Predictions are based on statistical models and market data. Actual results may vary based on individual circumstances, interview performance, and market conditions. This tool is for informational purposes only.*
+        
+        ---
+        
+        *Last Updated: {datetime.now().strftime("%B %d, %Y")} | Version 2.0 | Covering 200+ Countries Worldwide*
+        """)
+    
+    return app
+
+# ====================
+# APPLICATION LAUNCH
+# ====================
+def main():
+    """Launch the application"""
+    app = create_app()
+    
+    # Launch with Hugging Face Spaces settings
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        debug=True,
+        show_error=True,
+        auth=None,
+        max_file_size="100MB"
+    )
+
+if __name__ == "__main__":
+    main()

gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+gradio>=4.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+plotly>=5.0.0
+scikit-learn>=1.3.0
+joblib>=1.3.0
+python-dotenv>=1.0.0
+matplotlib>=3.7.0