# 🤖 NovaLang AI & Machine Learning Integration

## TensorFlow Integration
```nova
@MLModel(
    framework: "tensorflow",
    modelPath: "models/recommendation.pb",
    version: "1.0.0"
)
class RecommendationService {
    
    @TensorFlowModel("recommendation.pb")
    private model: TensorFlowModel
    
    @PreProcess
    private preprocessor: DataPreprocessor
    
    @PostProcess
    private postprocessor: ResultPostprocessor
    
    @Predict
    async getRecommendations(
        @Input("user_features") userFeatures: UserFeatures,
        @Input("context") context: RecommendationContext
    ): List<ProductRecommendation> {
        
        // Auto-preprocessing
        let processedFeatures = preprocessor.process(userFeatures)
        
        // TensorFlow inference
        let predictions = await model.predict({
            "user_features": processedFeatures.toTensor(),
            "context": context.toTensor()
        })
        
        // Auto-postprocessing
        return postprocessor.convertToRecommendations(predictions)
    }
    
    @BatchPredict(batchSize: 32)
    async getBatchRecommendations(
        users: List<UserFeatures>
    ): List<List<ProductRecommendation>> {
        // Automatic batching for efficiency
        return users.map(user -> getRecommendations(user, defaultContext))
    }
}
```

## PyTorch Integration
```nova
@MLModel(framework: "pytorch")
class ImageClassificationService {
    
    @PyTorchModel("resnet50.pth")
    private model: PyTorchModel
    
    @GPUEnabled(deviceId: 0)
    @Cached(ttl: "1h", keyGenerator: "imageHash")
    async classifyImage(@Input image: Image): ClassificationResult {
        
        // Automatic GPU acceleration if available
        let tensor = image.toTensor().normalize().resize(224, 224)
        
        let output = await model.forward(tensor)
        let probabilities = output.softmax(dim: 1)
        
        return ClassificationResult.builder()
            .topK(probabilities, k: 5)
            .confidence(probabilities.max())
            .build()
    }
    
    @RealTimeInference
    @WebSocket("/classify")
    streamClassification(): Publisher<ClassificationResult> {
        return cameraStream
            .map(frame -> classifyImage(frame))
            .throttle(100) // 10 FPS
    }
}
```

## AutoML Pipeline
```nova
@AutoML(
    targetMetric: "accuracy",
    maxTrials: 100,
    maxEpochs: 50
)
class CustomerChurnPrediction {
    
    @Dataset("customer_data.csv")
    @FeatureEngineering(
        categorical: ["country", "gender", "product_category"],
        numerical: ["age", "tenure", "monthly_charges"],
        target: "churn"
    )
    private dataset: Dataset
    
    @AutoTune
    @CrossValidation(folds: 5)
    async trainModel(): Model {
        // AutoML automatically tries different:
        // - Feature selection techniques
        // - Model architectures (RF, XGBoost, Neural Networks)
        // - Hyperparameter combinations
        // - Preprocessing pipelines
        
        let pipeline = AutoMLPipeline.builder()
            .dataset(dataset)
            .target("churn")
            .metricToOptimize("f1_score")
            .timeLimit(Duration.hours(2))
            .build()
        
        return await pipeline.fit()
    }
    
    @ModelValidation
    @ABTest(variants: ["model_v1", "model_v2"])
    async validateModel(model: Model): ValidationReport {
        return ModelValidator.builder()
            .testSet(dataset.testSplit())
            .metrics(["accuracy", "precision", "recall", "f1"])
            .fairnessTests(["demographic_parity", "equal_opportunity"])
            .explainabilityTests(true)
            .build()
            .validate(model)
    }
}
```

## Natural Language Processing
```nova
@NLPPipeline
class DocumentAnalysisService {
    
    @HuggingFaceModel("bert-base-uncased")
    private bertModel: TransformerModel
    
    @NamedEntityRecognition
    private nerModel: NERModel
    
    @SentimentAnalysis
    private sentimentModel: SentimentModel
    
    async analyzeDocument(@Input document: Document): DocumentAnalysis {
        
        // Parallel NLP tasks
        let [entities, sentiment, embeddings] = await Promise.all([
            extractEntities(document.text),
            analyzeSentiment(document.text),
            generateEmbeddings(document.text)
        ])
        
        return DocumentAnalysis.builder()
            .entities(entities)
            .sentiment(sentiment)
            .embeddings(embeddings)
            .topics(await extractTopics(document.text))
            .summary(await summarizeText(document.text))
            .build()
    }
    
    @TextGeneration
    async generateResponse(
        @Input prompt: string,
        @Parameter maxLength: int = 100,
        @Parameter temperature: float = 0.7
    ): string {
        return await bertModel.generate(prompt, {
            maxLength: maxLength,
            temperature: temperature,
            doSample: true
        })
    }
}
```

## Computer Vision
```nova
@ComputerVision
class ImageProcessingService {
    
    @YOLOModel("yolov5s.pt")
    private objectDetector: ObjectDetectionModel
    
    @SegmentationModel("mask_rcnn.pth")
    private segmentationModel: SegmentationModel
    
    @FaceRecognition
    private faceModel: FaceRecognitionModel
    
    async processImage(@Input image: Image): ImageAnalysis {
        
        // Object detection
        let objects = await objectDetector.detect(image)
        
        // Image segmentation
        let segments = await segmentationModel.segment(image)
        
        // Face detection and recognition
        let faces = await faceModel.detectAndRecognize(image)
        
        // OCR for text extraction
        let text = await extractText(image)
        
        return ImageAnalysis.builder()
            .objects(objects)
            .segments(segments)
            .faces(faces)
            .extractedText(text)
            .imageMetadata(image.getMetadata())
            .build()
    }
    
    @RealTimeProcessing
    @GPU(enabled: true)
    streamVideoAnalysis(@Input videoStream: VideoStream): Publisher<VideoFrame> {
        return videoStream
            .frames()
            .map(frame -> processImage(frame))
            .buffer(10) // Process 10 frames at once
    }
}
```

## Reinforcement Learning
```nova
@ReinforcementLearning
class TradingAgent {
    
    @Environment
    class TradingEnvironment implements RLEnvironment<TradingState, TradingAction> {
        
        @State
        getCurrentState(): TradingState {
            return TradingState.builder()
                .marketData(marketDataService.getCurrentData())
                .portfolio(portfolioService.getCurrentPortfolio())
                .technicalIndicators(technicalAnalysis.getIndicators())
                .build()
        }
        
        @Action
        takeAction(action: TradingAction): Reward {
            let profit = portfolioService.executeAction(action)
            let risk = riskAnalyzer.calculateRisk(action)
            
            return Reward.of(profit - risk * 0.1)
        }
    }
    
    @PPOAgent(
        learningRate: 0.001,
        gamma: 0.99,
        clipRatio: 0.2
    )
    private agent: PPOAgent
    
    @Training(episodes: 10000)
    async trainAgent(): TrainingResult {
        return await agent.train(TradingEnvironment())
    }
    
    @InferenceMode
    async makeDecision(marketState: MarketState): TradingDecision {
        let action = await agent.act(marketState)
        return TradingDecision.from(action)
    }
}
```

## AI-Assisted Development
```nova
@CodeAssistant
class SmartIDE {
    
    @CodeCompletion
    @ContextAware
    async suggestCode(@Context currentCode: CodeContext): List<CodeSuggestion> {
        
        let codeEmbedding = await codeEncoder.encode(currentCode)
        let similarPatterns = await patternMatcher.findSimilar(codeEmbedding)
        
        return aiModel.generateSuggestions({
            context: currentCode,
            patterns: similarPatterns,
            userPreferences: getUserPreferences()
        })
    }
    
    @BugDetection
    @RealTime
    async analyzeCode(@Input code: SourceCode): List<PotentialBug> {
        
        let staticAnalysis = staticAnalyzer.analyze(code)
        let aiAnalysis = await bugDetectionModel.predict(code)
        
        return BugReport.builder()
            .staticIssues(staticAnalysis)
            .aiPredictedBugs(aiAnalysis)
            .severity(calculateSeverity(staticAnalysis, aiAnalysis))
            .suggestions(generateFixSuggestions(aiAnalysis))
            .build()
    }
    
    @CodeGeneration
    async generateFromNaturalLanguage(@Input description: string): GeneratedCode {
        
        let intent = await intentClassifier.classify(description)
        let codeTemplate = templateEngine.getTemplate(intent)
        
        return await codeGenerator.generate({
            description: description,
            intent: intent,
            template: codeTemplate,
            codeStyle: getUserCodeStyle()
        })
    }
    
    @PerformanceOptimization
    async optimizeCode(@Input code: SourceCode): OptimizedCode {
        
        let performanceProfile = profiler.analyze(code)
        let optimizations = await optimizationModel.suggest(code, performanceProfile)
        
        return CodeOptimizer.builder()
            .originalCode(code)
            .optimizations(optimizations)
            .expectedSpeedup(calculateSpeedup(optimizations))
            .build()
            .apply()
    }
}
```

## Federated Learning
```nova
@FederatedLearning
class DistributedModelTraining {
    
    @FederatedServer(port: 8080)
    class ModelServer {
        
        @GlobalModel("recommendation_model.pt")
        private globalModel: PyTorchModel
        
        @FederatedRound
        async coordinateTraining(): FederationResult {
            
            // Select clients for this round
            let selectedClients = clientSelector.selectClients(
                criteria: ClientSelectionCriteria.random(fraction: 0.1)
            )
            
            // Distribute current model
            await distributeModel(selectedClients, globalModel)
            
            // Collect local updates
            let localUpdates = await collectUpdates(selectedClients)
            
            // Aggregate updates (FedAvg algorithm)
            let aggregatedUpdate = federatedAveraging(localUpdates)
            
            // Update global model
            globalModel.updateWeights(aggregatedUpdate)
            
            return FederationResult.builder()
                .round(getCurrentRound())
                .participatingClients(selectedClients.size())
                .globalAccuracy(evaluateGlobalModel())
                .build()
        }
    }
    
    @FederatedClient
    class ModelClient {
        
        @LocalData(private: true)
        private localDataset: Dataset
        
        @LocalTraining(epochs: 5)
        async trainLocally(globalModel: Model): ModelUpdate {
            
            let localModel = globalModel.copy()
            
            // Train on local data (data never leaves device)
            let trainingResult = await localModel.fit(
                dataset: localDataset,
                epochs: 5,
                privacy: DifferentialPrivacy(epsilon: 1.0)
            )
            
            // Return only model updates, not data
            return ModelUpdate.builder()
                .weights(localModel.getWeights() - globalModel.getWeights())
                .dataSize(localDataset.size())
                .localAccuracy(trainingResult.accuracy)
                .build()
        }
    }
}
```
