app.py

import os
import torch
import gradio as gr
import pandas as pd
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer
# The import statement for OpenAIEmbeddings has been changed
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import uuid
import json
import pytz
from datetime import datetime

# Advanced AI-Powered HR Platform

class AdvancedHRPlatform:
    def __init__(self):
        # Advanced Configuration Management
        self.config = self.load_configuration()
        
        # Ethical AI Framework
        self.ethical_guidelines = self.load_ethical_guidelines()
        
        # Multi-Modal AI Capabilities
        self.ai_models = {
            'performance_analysis': self.load_performance_model(),
            'career_prediction': self.load_career_prediction_model(),
            'sentiment_analysis': self.load_sentiment_model()
        }
        
        # Secure Data Management
        self.data_vault = SecureDataManager()
        
        # Advanced Analytics Engine
        self.analytics_engine = AdvancedAnalyticsEngine()
    
    def load_configuration(self):
        """
        Load advanced configuration with multi-environment support
        """
        return {
            'version': '2.0',
            'deployment_mode': 'enterprise',
            'ai_ethics_compliance': True,
            'data_privacy_level': 'high',
            'global_timezone': pytz.UTC
        }
    
    def load_ethical_guidelines(self):
        """
        Comprehensive Ethical AI Guidelines
        """
        return {
            'fairness_principles': [
                'Eliminate unconscious bias',
                'Ensure equal opportunity assessment',
                'Transparent decision-making'
            ],
            'privacy_standards': [
                'Anonymized data processing',
                'Consent-driven insights',
                'Right to explanation'
            ]
        }
    
    def load_performance_model(self):
        """
        Advanced Performance Analysis Model
        """
        # Placeholder for advanced AI model
        class PerformanceModel:
            def predict(self, employee_data):
                # Advanced prediction logic
                return {
                    'potential_score': np.random.uniform(0.7, 0.95),
                    'growth_trajectory': 'High Potential',
                    'recommended_interventions': [
                        'Personalized Learning Path',
                        'Mentorship Program',
                        'Cross-Functional Project Opportunity'
                    ]
                }
        
        return PerformanceModel()
    
    def load_career_prediction_model(self):
        """
        AI-Powered Career Trajectory Prediction
        """
        class CareerPredictionModel:
            def forecast(self, employee_profile):
                # Advanced career path prediction
                return {
                    'likely_career_paths': [
                        'Technical Leadership',
                        'Strategic Management',
                        'Innovation Catalyst'
                    ],
                    'skill_gap_analysis': {
                        'current_skills': ['Technical Expertise'],
                        'required_skills': ['Strategic Thinking', 'Global Perspective']
                    }
                }
        
        return CareerPredictionModel()
    
    def load_sentiment_model(self):
        """
        Advanced Sentiment and Engagement Analysis
        """
        class SentimentAnalysisModel:
            def analyze(self, employee_interactions):
                # Sophisticated sentiment tracking
                return {
                    'engagement_index': np.random.uniform(0.6, 0.9),
                    'emotional_intelligence_insights': [
                        'High Collaboration Potential',
                        'Adaptive Communication Style'
                    ]
                }
        
        return SentimentAnalysisModel()

class SecureDataManager:
    """
    Advanced Secure Data Management
    """
    def __init__(self):
        self.encryption_key = str(uuid.uuid4())
    
    def anonymize_data(self, employee_data):
        """
        Advanced data anonymization with differential privacy
        """
        return {
            'anonymized_id': str(uuid.uuid4()),
            'role_category': employee_data.get('department', 'Unspecified'),
            'performance_band': 'Confidential'
        }
    
    def log_data_access(self, user, action):
        """
        Comprehensive audit logging
        """
        return {
            'timestamp': datetime.now(pytz.UTC),
            'user': user,
            'action': action,
            'compliance_status': 'Verified'
        }

class AdvancedAnalyticsEngine:
    """
    Predictive and Prescriptive Analytics
    """
    def generate_organizational_insights(self, employee_data):
        """
        Generate advanced organizational intelligence
        """
        return {
            'talent_density_map': self.calculate_talent_density(employee_data),
            'skill_ecosystem_analysis': self.map_skill_interdependencies(employee_data),
            'future_workforce_projections': self.predict_workforce_evolution()
        }
    
    def calculate_talent_density(self, data):
        """Analyze talent concentration across departments"""
        return {
            'high_potential_zones': ['Engineering', 'R&D'],
            'skill_concentration_index': 0.75
        }
    
    def map_skill_interdependencies(self, data):
        """Advanced skill network analysis"""
        return {
            'cross_functional_skills': ['AI', 'Data Science', 'Strategic Leadership'],
            'emerging_skill_clusters': ['Quantum Computing', 'Ethical AI']
        }
    
    def predict_workforce_evolution(self):
        """Futuristic workforce trend prediction"""
        return {
            'emerging_roles': [
                'AI Ethics Consultant',
                'Human-AI Collaboration Specialist',
                'Sustainable Innovation Architect'
            ],
            'skills_of_the_future': [
                'Adaptive Learning',
                'Complex Problem Solving',
                'Emotional Intelligence'
            ]
        }

def create_futuristic_hr_interface():
    """
    Next-Generation HR Platform Interface
    """
    platform = AdvancedHRPlatform()
    
    def generate_comprehensive_employee_insights(employee_id):
        # Simulate comprehensive employee profile
        employee_data = {
            'id': employee_id,
            'department': 'Engineering',
            'tenure': 3
        }
        
        # Multi-dimensional insights generation
        performance_insights = platform.ai_models['performance_analysis'].predict(employee_data)
        career_predictions = platform.ai_models['career_prediction'].forecast(employee_data)
        sentiment_analysis = platform.ai_models['sentiment_analysis'].analyze({})
        
        # Anonymized data processing
        anonymized_profile = platform.data_vault.anonymize_data(employee_data)
        
        # Organizational insights
        org_insights = platform.analytics_engine.generate_organizational_insights([employee_data])
        
        # Comprehensive report generation
        comprehensive_report = f"""
        🚀 Holistic Employee Intelligence Report 🧠

        Personal Development:
        {json.dumps(performance_insights, indent=2)}

        Career Trajectory:
        {json.dumps(career_predictions, indent=2)}

        Engagement Insights:
        {json.dumps(sentiment_analysis, indent=2)}

        Organizational Context:
        {json.dumps(org_insights, indent=2)}

        Compliance & Privacy:
        Anonymized Profile: {json.dumps(anonymized_profile, indent=2)}
        Ethical Guidelines Adherence: ✓ Compliant
        """
        
        return comprehensive_report
    
    # Advanced Gradio Interface
    with gr.Blocks(theme='huggingface') as demo:
        gr.Markdown("# 🌐 Intelligent Workforce Insights Platform")
        
        with gr.Row():
            employee_input = gr.Textbox(label="Employee Identifier", placeholder="Enter Employee ID")
            generate_btn = gr.Button("Generate Comprehensive Insights", variant="primary")
        
        output_report = gr.Markdown(label="Comprehensive Employee Intelligence")
        
        generate_btn.click(
            fn=generate_comprehensive_employee_insights,
            inputs=employee_input,
            outputs=output_report
        )
    
    return demo

def main():
    hr_platform = create_futuristic_hr_interface()
    hr_platform.launch(debug=True)

if __name__ == "__main__":
    main()