```python
#!/usr/bin/env python3
"""
AI Forge E-commerce Automation Code Generator
Generates custom automation scripts (data pipelines, bots) with predictive ML optimization
Freelancer-focused premium AI integration for high-demand clients
"""

import os
import json
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import LabelEncoder
import joblib
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import ast
import subprocess
import warnings
warnings.filterwarnings('ignore')

class EcommerceDataAnalyzer:
    """Analyzes e-commerce business data to predict optimal automation strategies"""
    
    def __init__(self):
        self.model = None
        self.label_encoder = LabelEncoder()
        self.feature_importance = {}
        
    def load_business_data(self, file_path):
        """Load e-commerce business data"""
        try:
            df = pd.read_csv(file_path)
            print(f"Loaded business data with {len(df)} records")
            return df
        except Exception as e:
            print(f"Error loading data: {e}")
            return None
    
    def extract_features(self, df):
        """Extract features for automation strategy prediction"""
        features = []
        
        # Business metrics
        business_features = ['monthly_revenue', 'inventory_turnover', 'order_volume', 'customer_count']
        
        # Process categorical variables
        categorical_cols = ['business_type', 'platform', 'marketing_strategy']
        
        for col in categorical_cols:
            if col in df.columns:
                dummies = pd.get_dummies(df[col], prefix=col)
                features.append(dummies)
        
        # Add numerical features
        for feature in business_features:
            if feature in df.columns:
                features.append(df[[feature]]))
        
        # Time-based features
        if 'date' in df.columns:
            df['month'] = pd.to_datetime(df['date']).dt.month
            features.append(pd.get_dummies(df['month'], prefix='month'))
        
        X = pd.concat(features, axis=1)
        return X
    
    def prepare_automation_labels(self, df):
        """Prepare labels for automation strategy classification"""
        strategies = []
        
        for _, row in df.iterrows():
            strategy = self._determine_optimal_strategy(row)
            strategies.append(strategy)
        
        return self.label_encoder.fit_transform(strategies)
    
    def _determine_optimal_strategy(self, business_data):
        """Determine optimal automation strategy based on business metrics"""
        
        revenue = business_data.get('monthly_revenue', 0)
        inventory_turnover = business_data.get('inventory_turnover', 0)
        order_volume = business_data.get('order_volume', 0)
        
        # Define strategy categories
        if revenue > 50000 and inventory_turnover < 4:
            return "inventory_optimization"
        elif revenue > 100000 and order_volume > 1000:
            return "advanced_ai_pipeline"
        elif revenue > 25000 and order_volume > 500:
            return "marketing_automation"
        elif order_volume > 2000:
            return "data_processing_bot"
        elif revenue > 10000:
            return "basic_automation"
        else:
            return "manual_processes"
    
    def train_strategy_predictor(self, X, y):
        """Train Random Forest model to predict optimal automation strategies"""
        
        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=0.2, random_state=42)
        
        self.model = RandomForestClassifier(
            n_estimators=100,
            max_depth=10,
            random_state=42
        )
        
        # Train model
        self.model.fit(X_train, y_train)
        
        # Evaluate model
        y_pred = self.model.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        
        # Feature importance
        self.feature_importance = dict(zip(X.columns, self.model.feature_importances_))
        
        print(f"Strategy predictor trained - Accuracy: {accuracy:.4f}")
        
        # Cross-validation
        cv_scores = cross_val_score(self.model, X, y, cv=5)
        print(f"Cross-validation scores: {cv_scores}")
        print(f"Mean CV accuracy: {cv_scores.mean():.4f}")
        
        return self.model
    
    def predict_optimal_strategy(self, business_data):
        """Predict optimal automation strategy for new business"""
        if self.model is None:
            print("Model not trained yet")
            return None
        
        X_new = self.extract_features(business_data)
        strategy_idx = self.model.predict(X_new)[0]
        strategy = self.label_encoder.inverse_transform([strategy_idx])[0]
        
        return strategy

class AutomationCodeGenerator:
    """Generates custom automation code based on predicted strategies"""
    
    def __init__(self):
        self.templates = self._load_code_templates()
        
    def _load_code_templates(self):
        """Load code templates for different automation strategies"""
        
        templates = {
            "inventory_optimization": {
                "description": "AI-powered inventory management and restocking",
                "language": "python",
                "template": '''#!/usr/bin/env python3
"""
AI-Powered Inventory Optimization System
Generated by AI Forge for {business_name}
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')

class InventoryOptimizer:
    """AI-powered inventory optimization system"""
    
    def __init__(self):
        self.model = None
        
    def load_inventory_data(self, file_path):
        """Load inventory and sales data"""
        try:
            df = pd.read_csv(file_path)
            return df
        except Exception as e:
            print(f"Error loading inventory data: {e}")
            return None
    
    def train_demand_predictor(self, df):
        """Train demand prediction model"""
        features = ['product_id', 'current_stock', 'lead_time', 'seasonality_factor']
        return df[features]
    
    def predict_restocking(self, inventory_data):
        """Predict optimal restocking quantities"""
        # Implementation details
        pass

def main():
    optimizer = InventoryOptimizer()
    # Add your implementation here
    pass

if __name__ == "__main__":
    main()
'''
            },
            "advanced_ai_pipeline": {
                "description": "Multi-stage AI pipeline for e-commerce operations",
                "language": "python",
                "template": '''#!/usr/bin/env python3
"""
Advanced AI Pipeline for E-commerce Operations
Generated by AI Forge for {business_name}
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import joblib

class AdvancedAIPipeline:
    """Comprehensive AI pipeline for e-commerce automation"""
    
    def __init__(self):
        self.models = {{}}
    
    def process_data_pipeline(self):
        """Multi-stage data processing pipeline"""
        pass

def main():
    pipeline = AdvancedAIPipeline()
    pass

if __name__ == "__main__":
    main()
'''
            },
            "marketing_automation": {
                "description": "Automated marketing campaign management",
                "language": "python",
                "template": '''#!/usr/bin/env python3
"""
Marketing Automation System
Generated by AI Forge for {business_name}
"""

import pandas as pd
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime, timedelta
import json

class MarketingAutomator:
    """AI-driven marketing campaign automation"""
    
    def __init__(self):
        self.campaign_data = {{}}
    
    def automate_campaigns(self):
        """Automated campaign management"""
        pass

if __name__ == "__main__":
    automator = MarketingAutomator()
    # Implementation
    pass
'''
            },
            "data_processing_bot": {
                "description": "Intelligent data processing and analysis bot",
                "language": "python",
                "template": '''#!/usr/bin/env python3
"""
Data Processing Automation Bot
Generated by AI Forge for {business_name}
"""

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import schedule
import time

class DataProcessingBot:
    """Automated data processing and analysis system"""
    
    def __init__(self):
        self.processed_data = {{}}
    
    def run_data_pipeline(self):
        """Complete data processing pipeline"""
        pass

def main():
    bot = DataProcessingBot()
    # Add implementation
    pass

if __name__ == "__main__":
    main()
'''
            }
        }
        
        return templates
    
    def generate_custom_code(self, strategy, business_name, custom_params=None):
        """Generate custom automation code based on strategy"""
        
        if strategy not in self.templates:
            raise ValueError(f"Unknown strategy: {strategy}")
        
        template = self.templates[strategy]
        code = template["template"].format(
            business_name=business_name,
            custom_params=custom_params or {}
        )
        
        return {
            "strategy": strategy,
            "description": template["description"],
            "language": template["language"],
            "code": code
        }

class CodeValidator:
    """Validates generated code for syntax correctness"""
    
    def __init__(self):
        pass
    
    def validate_python_syntax(self, code):
        """Validate Python code syntax using ast module"""
        try:
            ast.parse(code)
            return True
        except SyntaxError as e:
            return False, str(e)
    
    def test_code_execution(self, code_file_path):
        """Test if generated code can be executed without errors"""
        try:
            result = subprocess.run(
                ['python', '-m', 'py_compile', code_file_path],
                capture_output=True,
                text=True,
                timeout=30
            )
            
            if result.returncode == 0:
                return True, "Code compiled successfully"
            else:
                return False, result.stderr
    
    def check_dependencies(self, code):
        """Check for required dependencies in the code"""
        dependencies = set()
        
        # Simple dependency extraction (enhance for production)
        if 'pandas' in code:
                dependencies.add('pandas')
            if 'numpy' in code:
                dependencies.add('numpy')
            if 'sklearn' in code:
                dependencies.add('scikit-learn')
            if 'joblib' in code:
                dependencies.add('joblib')
            if 'requests' in code:
                dependencies.add('requests')
            
            return list(dependencies)

class DeploymentManager:
    """Manages deployment of generated automation systems"""
    
    def __init__(self):
        self.deployment_templates = self._load_deployment_templates()
    
    def _load_deployment_templates(self):
        """Load deployment configuration templates"""
        
        templates = {
            "docker": {
                "template": '''FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "generated_system.py"]
        },
        "fastapi": {
            "template": '''from fastapi import FastAPI
import uvicorn

app = FastAPI()

@app.get("/")
def root():
    return {{"message": "AI Automation System Deployed"}}
        }
        
        return templates
    
    def generate_deployment_config(self, strategy, system_name):
        """Generate deployment configuration files"""
        
        if strategy not in self.deployment_templates:
            return None
        
        return self.deployment_templates[strategy]["template"]

class EcommerceAutomationAPI:
    """FastAPI microservice for the automation code generation system"""
    
    def __init__(self):
        self.data_analyzer = EcommerceDataAnalyzer()
        self.code_generator = AutomationCodeGenerator()
        self.validator = CodeValidator()
        self.deployment_manager = DeploymentManager()
        
    def initialize_system(self):
        """Initialize the complete automation system"""
        print("Initializing E-commerce Automation Code Generator...")
        
        # Load sample data for training
        sample_data = self._generate_sample_business_data()
        
        # Extract features and labels
        X = self.data_analyzer.extract_features(sample_data)
        y = self.data_analyzer.prepare_automation_labels(sample_data)
        
        # Train strategy predictor
        self.data_analyzer.train_strategy_predictor(X, y)
        
        print("System initialized successfully")
    
    def _generate_sample_business_data(self):
        """Generate sample business data for system training"""
        
        sample_data = []
        business_types = ['clothing', 'electronics', 'home_goods', 'beauty', 'sports']]
        
        for i in range(100):
            sample_data.append({
                'business_id': i+1,
                'business_type': np.random.choice(business_types),
                'monthly_revenue': np.random.randint(5000, 200000),
            'inventory_turnover': np.random.uniform(2, 8),
            'order_volume': np.random.randint(100, 5000),
            'customer_count': np.random.randint(50, 5000),
            'platform': np.random.choice(['shopify', 'woocommerce', 'magento', 'custom']),
            'marketing_strategy': np.random.choice(['social_media', 'email', 'seo', 'ppc']),
            'date': pd.Timestamp('2024-01-01') + pd.Timedelta(days=i),
            'platform': np.random.choice(['shopify', 'woocommerce', 'magento', 'custom']),
            'monthly_revenue': np.random.randint(5000, 200000),
            'inventory_turnover': np.random.uniform(2, 8),
            'order_volume': np.random.randint(100, 5000),
            'customer_count': np.random.randint(50, 5000),
            'inventory_value': np.random.randint(10000, 500000),
            'employee_count': np.random.randint(1, 50)
        })
        
        return pd.DataFrame(sample_data)
    
    def process_automation_request(self, business_data):
        """Complete workflow: analyze business data, predict strategy, generate code"
        
        # Predict optimal automation strategy
        strategy = self.data_analyzer.predict_optimal_strategy(business_data)
        
        # Generate custom code
        code_result = self.code_generator.generate_custom_code(
            strategy,
            business_data.get('business_name', 'Client Business'),
            business_data.get('custom_params', {})
        )
        
        # Validate code syntax
        is_valid = self.validator.validate_python_syntax(code_result["code"])
        
        if not is_valid:
            raise ValueError("Generated code has syntax errors")
        
        # Generate deployment configuration
        deployment_config = self.deployment_manager.generate_deployment_config(strategy, business_data.get('business_name')))
        
        return {
            "strategy": strategy,
            "generated_code": code_result,
            "validation": {
                "syntax_valid": is_valid,
                "dependencies": self.validator.check_dependencies(code_result["code"])
        }

# FastAPI Application
app = FastAPI(
    title="AI Forge E-commerce Automation Generator",
    description="Premium AI-powered code generation for e-commerce automation",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"]
)

# Initialize system
automation_system = EcommerceAutomationAPI()

@app.on_event("startup")
async def startup_event():
    """Initialize system on startup"""
    automation_system.initialize_system()

class AutomationRequest:
    business_name: str
    monthly_revenue: float
    order_volume: int
    inventory_turnover: float
    business_type: str
    custom_params: dict = {}

class AutomationResponse:
    success: bool
    message: str
    strategy: str
    generated_code: dict = None
    deployment_config: str = None

@app.get("/")
async def root():
    return {"message": "AI Forge E-commerce Automation Code Generator API"}

@app.post("/api/generate-automation", response_model=AutomationResponse)
async def generate_automation(request: AutomationRequest):
    """Generate custom automation code for e-commerce business"""
    try:
        business_data = {
            'business_name': request.business_name,
            'monthly_revenue': request.monthly_revenue,
            'order_volume': request.order_volume,
            'inventory_turnover': request.inventory_turnover,
            'business_type': request.business_type,
            'custom_params': request.custom_params
        }
        
        result = automation_system.process_automation_request(business_data)
        
        return AutomationResponse(
            success=True,
            message=f"Successfully generated {result['strategy']} automation system")
        strategy=result['strategy'],
        generated_code=result['generated_code'],
        deployment_config=result.get('deployment_config')
        )
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")

@app.get("/api/strategy-recommendation")
async def get_strategy_recommendation(
    monthly_revenue: float,
    order_volume: int,
    inventory_turnover: float
):
    """Get automation strategy recommendation based on business metrics"""
    try:
        sample_business = pd.DataFrame([{
            'monthly_revenue': monthly_revenue,
            'order_volume': order_volume,
    inventory_turnover: float
):
    """Generate strategy recommendation based on business metrics"""
    business_data = pd.DataFrame([{
        'monthly_revenue': monthly_revenue,
        'order_volume': order_volume,
            'inventory_turnover': inventory_turnover,
            'business_type': 'electronics',  # Example
            'platform': 'shopify',  # Example
            'customer_count': 1000,  # Example
            'business_type': 'electronics',
            'inventory_turnover': inventory_turnover
    }])
    
    strategy = automation_system.data_analyzer.predict_optimal_strategy(business_data)
    
    return {"strategy": strategy}

@app.get("/api/health")
async def health_check():
    return {"status": "healthy", "service": "ecommerce_automation_generator"}

def generate_sample_business_data():
    """Generate comprehensive sample business data for testing"""
    
    businesses = []
    for i in range(50):
        businesses.append({
            'business_id': i+1,
            'business_name': f"Sample Business {i+1}",
        'monthly_revenue': monthly_revenue,
        'order_volume': order_volume,
        'inventory_turnover': np.random.uniform(2, 8),
            'monthly_revenue': np.random.randint(10000, 150000),
            'inventory_turnover': np.random.uniform(3, 7),
            'customer_count': np.random.randint(100, 3000),
            'business_type': np.random.choice(['clothing', 'electronics', 'home_goods', 'beauty', 'sports']),
            'platform': np.random.choice(['shopify', 'woocommerce', 'magento', 'custom']),
            'marketing_strategy': np.random.choice(['social_media', 'email', 'seo']),
            'inventory_value': np.random.randint(50000, 300000),
            'employee_count': np.random.randint(2, 25)
        })
    
    df = pd.DataFrame(businesses)
    df.to_csv("./data/sample_businesses.csv", index=False)
    print("Sample business data generated")

def main():
    """Main execution function"""
    print("="*70)
    print("AI FORGE E-COMMERCE AUTOMATION CODE GENERATOR")
    print("Optimized for 220% YoY demand growth in AI automation")
    print("Premium service for freelancers and high-demand clients")
    print("="*70)
    
    # Generate sample data
    generate_sample_business_data()
    
    # Initialize and test the system
    automation_system.initialize_system()
    
    # Sample automation request
    sample_request = AutomationRequest(
        business_name="TechGadgets Inc.",
        monthly_revenue=125000,
        order_volume=2870,
        inventory_turnover=4.2,
        business_type="electronics",
        custom_params={
            'api_key': 'your_api_key_here',
            'webhook_url': 'https://your-webhook.com'
    )
    
    # Process sample request
    result = automation_system.process_automation_request({
        'business_name': sample_request.business_name,
        'monthly_revenue': sample_request.monthly_revenue,
        'order_volume': sample_request.order_volume,
        'inventory_turnover': sample_request.inventory_turnover,
        'business_type': sample_request.business_type,
        'custom_params': sample_request.custom_params
    )
    
    print(f"Generated {result['strategy']} automation system")
    print(f"Code validation: {result['validation']['syntax_valid']}")
    print(f"Dependencies: {result['validation']['dependencies']}")
    
    print("\nSystem ready for premium client automation projects!")
    print("API endpoints available at http://localhost:8000")
    
    # Start the FastAPI server
    uvicorn.run(
        "ecommerce_automation_generator:app",
        host="0.0.0.0",
        port=8000,
        reload=True
    )

if __name__ == "__main__":
    main()
```