ai_engineer.py

#!/usr/bin/env python3
"""
Ingénieur Automatique pour Développement IA
Système intelligent de développement, optimisation et déploiement d'IA
"""

import asyncio
import logging
import json
import re
import ast
import inspect
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime
import hashlib
import subprocess
import sys
import os

@dataclass
class AIPipeline:
    """Pipeline de développement IA"""
    name: str
    version: str
    components: Dict[str, Any]
    dependencies: List[str]
    performance_metrics: Dict[str, float]
    training_history: List[Dict]

@dataclass
class CodeAnalysis:
    """Analyse de code IA"""
    quality_score: float
    issues: List[Dict]
    optimizations: List[Dict]
    security_concerns: List[str]
    performance_recommendations: List[str]

class AutomaticAIEngineer:
    """
    Ingénieur automatique pour le développement d'IA
    """
    
    def __init__(self):
        self.logger = logging.getLogger("ai_engineer")
        self.pipelines: Dict[str, AIPipeline] = {}
        self.code_bases: Dict[str, Any] = {}
        
        # Templates d'architectures IA
        self.ai_templates = {
            "neural_network": {
                "type": "deep_learning",
                "framework": "pytorch",
                "structure": self._get_nn_template(),
                "dependencies": ["torch", "torchvision", "numpy"]
            },
            "transformer": {
                "type": "nlp",
                "framework": "transformers",
                "structure": self._get_transformer_template(),
                "dependencies": ["transformers", "torch", "tokenizers"]
            },
            "computer_vision": {
                "type": "cv",
                "framework": "opencv_pytorch",
                "structure": self._get_cv_template(),
                "dependencies": ["torch", "opencv-python", "pillow"]
            },
            "reinforcement_learning": {
                "type": "rl",
                "framework": "stable_baselines3",
                "structure": self._get_rl_template(),
                "dependencies": ["stable-baselines3", "gym", "numpy"]
            }
        }
        
        # Règles d'optimisation IA
        self.optimization_rules = {
            "performance": {
                "batch_size": "Ajustement dynamique selon la mémoire disponible",
                "learning_rate": "Scheduling adaptatif",
                "architecture": "Optimisation des couches et connexions"
            },
            "memory": {
                "gradient_checkpointing": "Réduction mémoire pendant l'entraînement",
                "mixed_precision": "Utilisation de float16 quand possible",
                "model_pruning": "Élagage des poids non essentiels"
            },
            "training": {
                "early_stopping": "Arrêt automatique si sur-entraînement",
                "data_augmentation": "Augmentation automatique des données",
                "cross_validation": "Validation croisée intégrée"
            }
        }
    
    async def create_ai_pipeline(self, pipeline_type: str, requirements: Dict) -> Dict[str, Any]:
        """Crée un pipeline IA automatique basé sur les requirements"""
        try:
            if pipeline_type not in self.ai_templates:
                return {
                    "success": False,
                    "error": f"Type de pipeline non supporté: {pipeline_type}",
                    "available_types": list(self.ai_templates.keys())
                }
            
            template = self.ai_templates[pipeline_type]
            pipeline_id = f"{pipeline_type}_{hashlib.md5(str(requirements).encode()).hexdigest()[:8]}"
            
            # Génération du code IA
            generated_code = await self._generate_ai_code(template, requirements)
            
            # Création des fichiers
            file_structure = await self._create_project_structure(pipeline_id, generated_code, template)
            
            # Installation des dépendances
            dependencies_result = await self._install_dependencies(template['dependencies'])
            
            pipeline = AIPipeline(
                name=pipeline_id,
                version="1.0.0",
                components=generated_code,
                dependencies=template['dependencies'],
                performance_metrics={},
                training_history=[]
            )
            
            self.pipelines[pipeline_id] = pipeline
            
            return {
                "success": True,
                "pipeline_id": pipeline_id,
                "files_created": file_structure,
                "dependencies_installed": dependencies_result,
                "next_steps": await self._get_next_steps(pipeline_type),
                "code_examples": await self._get_usage_examples(pipeline_type)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur création pipeline: {e}")
            return {"success": False, "error": str(e)}
    
    async def analyze_ai_code(self, code: str, code_type: str = "python") -> CodeAnalysis:
        """Analyse et optimise du code IA automatiquement"""
        analysis = CodeAnalysis(
            quality_score=0.0,
            issues=[],
            optimizations=[],
            security_concerns=[],
            performance_recommendations=[]
        )
        
        try:
            # Analyse syntaxique
            syntax_issues = await self._check_syntax(code, code_type)
            analysis.issues.extend(syntax_issues)
            
            # Analyse des performances
            performance_analysis = await self._analyze_performance(code)
            analysis.performance_recommendations.extend(performance_analysis)
            
            # Vérification de sécurité
            security_checks = await self._check_security(code)
            analysis.security_concerns.extend(security_checks)
            
            # Optimisations IA spécifiques
            ai_optimizations = await self._optimize_ai_code(code)
            analysis.optimizations.extend(ai_optimizations)
            
            # Calcul du score de qualité
            analysis.quality_score = await self._calculate_quality_score(analysis)
            
            return analysis
            
        except Exception as e:
            self.logger.error(f"Erreur analyse code: {e}")
            analysis.issues.append({"type": "analysis_error", "message": str(e)})
            return analysis
    
    async def auto_train_model(self, pipeline_id: str, dataset_config: Dict) -> Dict[str, Any]:
        """Lance l'entraînement automatique du modèle IA"""
        try:
            if pipeline_id not in self.pipelines:
                return {"success": False, "error": "Pipeline non trouvé"}
            
            pipeline = self.pipelines[pipeline_id]
            
            # Préparation des données
            data_prep = await self._prepare_training_data(dataset_config)
            
            # Configuration de l'entraînement
            training_config = await self._auto_configure_training(pipeline, dataset_config)
            
            # Lancement de l'entraînement
            training_result = await self._execute_training(pipeline_id, training_config)
            
            # Analyse des résultats
            performance_metrics = await self._analyze_training_results(training_result)
            
            # Mise à jour du pipeline
            pipeline.performance_metrics = performance_metrics
            pipeline.training_history.append({
                "timestamp": datetime.now().isoformat(),
                "config": training_config,
                "results": training_result,
                "metrics": performance_metrics
            })
            
            return {
                "success": True,
                "training_id": f"train_{pipeline_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
                "config": training_config,
                "results": training_result,
                "metrics": performance_metrics,
                "recommendations": await self._get_training_recommendations(performance_metrics)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur entraînement: {e}")
            return {"success": False, "error": str(e)}
    
    async def optimize_model(self, pipeline_id: str, optimization_target: str = "performance") -> Dict[str, Any]:
        """Optimisation automatique du modèle IA"""
        try:
            pipeline = self.pipelines[pipeline_id]
            optimizations = []
            
            if optimization_target == "performance":
                optimizations = await self._optimize_performance(pipeline)
            elif optimization_target == "memory":
                optimizations = await self._optimize_memory(pipeline)
            elif optimization_target == "accuracy":
                optimizations = await self._optimize_accuracy(pipeline)
            else:
                optimizations = await self._optimize_all(pipeline)
            
            # Application des optimisations
            applied_optimizations = await self._apply_optimizations(pipeline_id, optimizations)
            
            return {
                "success": True,
                "optimizations_proposed": optimizations,
                "optimizations_applied": applied_optimizations,
                "performance_improvement": await self._measure_improvement(pipeline_id)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur optimisation: {e}")
            return {"success": False, "error": str(e)}
    
    async def deploy_model(self, pipeline_id: str, deployment_target: str = "huggingface") -> Dict[str, Any]:
        """Déploiement automatique du modèle IA"""
        try:
            pipeline = self.pipelines[pipeline_id]
            
            deployment_config = {
                "huggingface": await self._prepare_huggingface_deployment(pipeline),
                "api": await self._prepare_api_deployment(pipeline),
                "mobile": await self._prepare_mobile_deployment(pipeline)
            }
            
            if deployment_target not in deployment_config:
                return {
                    "success": False, 
                    "error": f"Cible de déploiement non supportée: {deployment_target}",
                    "supported_targets": list(deployment_config.keys())
                }
            
            deployment_steps = deployment_config[deployment_target]
            deployment_result = await self._execute_deployment(pipeline_id, deployment_steps)
            
            return {
                "success": True,
                "deployment_target": deployment_target,
                "steps": deployment_steps,
                "result": deployment_result,
                "access_urls": await self._get_deployment_urls(pipeline_id, deployment_target)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur déploiement: {e}")
            return {"success": False, "error": str(e)}
    
    async def debug_ai_model(self, pipeline_id: str, issue_description: str) -> Dict[str, Any]:
        """Débogage automatique des modèles IA"""
        try:
            analysis = await self._analyze_issues(pipeline_id, issue_description)
            fixes = await self._generate_fixes(analysis)
            applied_fixes = await self._apply_fixes(pipeline_id, fixes)
            
            return {
                "success": True,
                "issue_analysis": analysis,
                "proposed_fixes": fixes,
                "applied_fixes": applied_fixes,
                "verification": await self._verify_fixes(pipeline_id)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur débogage: {e}")
            return {"success": False, "error": str(e)}
    
    # Méthodes d'implémentation
    async def _generate_ai_code(self, template: Dict, requirements: Dict) -> Dict[str, str]:
        """Génère du code IA basé sur le template et les requirements"""
        code_files = {}
        
        if template['type'] == 'deep_learning':
            code_files = {
                "model.py": self._generate_model_architecture(requirements),
                "train.py": self._generate_training_script(requirements),
                "config.py": self._generate_config_file(requirements),
                "utils.py": self._generate_utility_functions(requirements)
            }
        elif template['type'] == 'nlp':
            code_files = {
                "model.py": self._generate_transformer_model(requirements),
                "tokenizer.py": self._generate_tokenizer_script(requirements),
                "train.py": self._generate_nlp_training(requirements)
            }
        
        return code_files
    
    async def _create_project_structure(self, pipeline_id: str, code_files: Dict, template: Dict) -> List[str]:
        """Crée la structure de projet pour le pipeline IA"""
        project_path = f"projects/{pipeline_id}"
        os.makedirs(project_path, exist_ok=True)
        
        created_files = []
        for filename, content in code_files.items():
            filepath = os.path.join(project_path, filename)
            with open(filepath, 'w', encoding='utf-8') as f:
                f.write(content)
            created_files.append(filepath)
        
        # Création du fichier requirements
        requirements_file = os.path.join(project_path, "requirements.txt")
        with open(requirements_file, 'w') as f:
            for dep in template['dependencies']:
                f.write(f"{dep}\n")
        created_files.append(requirements_file)
        
        return created_files
    
    async def _install_dependencies(self, dependencies: List[str]) -> Dict[str, Any]:
        """Installe les dépendances automatiquement"""
        results = {}
        
        for dep in dependencies:
            try:
                # Simulation d'installation - dans la réalité on utiliserait subprocess
                results[dep] = {"status": "success", "version": "latest"}
            except Exception as e:
                results[dep] = {"status": "failed", "error": str(e)}
        
        return results
    
    def _get_nn_template(self) -> str:
        """Template de réseau de neurones"""
        return '''
import torch
import torch.nn as nn

class NeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_sizes, output_size, dropout=0.3):
        super(NeuralNetwork, self).__init__()
        
        layers = []
        prev_size = input_size
        
        for i, hidden_size in enumerate(hidden_sizes):
            layers.append(nn.Linear(prev_size, hidden_size))
            layers.append(nn.ReLU())
            layers.append(nn.Dropout(dropout))
            prev_size = hidden_size
        
        layers.append(nn.Linear(prev_size, output_size))
        
        self.network = nn.Sequential(*layers)
    
    def forward(self, x):
        return self.network(x)

# Configuration automatique
def auto_configure_model(input_dim, output_dim, complexity='medium'):
    if complexity == 'simple':
        hidden_layers = [64, 32]
    elif complexity == 'medium':
        hidden_layers = [128, 64, 32]
    else:  # complex
        hidden_layers = [256, 128, 64, 32]
    
    return NeuralNetwork(input_dim, hidden_layers, output_dim)
'''
    
    def _get_transformer_template(self) -> str:
        """Template de modèle Transformer"""
        return '''
from transformers import AutoModel, AutoTokenizer
import torch.nn as nn

class TransformerClassifier(nn.Module):
    def __init__(self, model_name='bert-base-uncased', num_classes=2, dropout=0.1):
        super(TransformerClassifier, self).__init__()
        
        self.transformer = AutoModel.from_pretrained(model_name)
        self.dropout = nn.Dropout(dropout)
        self.classifier = nn.Linear(self.transformer.config.hidden_size, num_classes)
    
    def forward(self, input_ids, attention_mask):
        outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        output = self.dropout(pooled_output)
        return self.classifier(output)

# Utilisation automatique
def create_transformer_model(task_type='classification', model_size='base'):
    model_map = {
        'base': 'bert-base-uncased',
        'large': 'bert-large-uncased',
        'distilled': 'distilbert-base-uncased'
    }
    
    return TransformerClassifier(model_name=model_map[model_size])
'''
    
    async def _analyze_performance(self, code: str) -> List[str]:
        """Analyse les performances du code IA"""
        recommendations = []
        
        # Détection de patterns non optimaux
        patterns = {
            "for loops": "Remplacez les boucles Python par des opérations vectorisées",
            "explicit loops": "Utilisez torch.optimisé ou numpy vectorisé",
            "memory copy": "Évitez les copies inutiles de tenseurs",
            "device transfer": "Minimisez les transferts CPU/GPU"
        }
        
        for pattern, recommendation in patterns.items():
            if pattern in code.lower():
                recommendations.append(recommendation)
        
        return recommendations
    
    async def _optimize_ai_code(self, code: str) -> List[Dict]:
        """Propose des optimisations pour le code IA"""
        optimizations = []
        
        # Optimisations automatiques détectées
        if "for i in range" in code and "torch" in code:
            optimizations.append({
                "type": "vectorization",
                "description": "Remplacer la boucle par des opérations vectorisées PyTorch",
                "priority": "high",
                "estimated_improvement": "70%"
            })
        
        if "model.eval()" not in code and "with torch.no_grad()" not in code:
            optimizations.append({
                "type": "inference_optimization", 
                "description": "Ajouter model.eval() et torch.no_grad() pour l'inférence",
                "priority": "medium",
                "estimated_improvement": "30%"
            })
        
        return optimizations

# Exemple d'utilisation
async def main():
    engineer = AutomaticAIEngineer()
    
    # Création d'un pipeline IA
    result = await engineer.create_ai_pipeline("neural_network", {
        "input_size": 784,
        "output_size": 10, 
        "complexity": "medium",
        "task": "classification"
    })
    
    print("Pipeline créé:", result)

if __name__ == "__main__":
    asyncio.run(main())