Integration Helpers

integration-helpers

Instructions

This skill provides production-ready integration templates for deploying machine learning models into full-stack applications. It covers FastAPI inference endpoints, Next.js prediction interfaces, and Supabase schemas for ML metadata storage.

1. FastAPI Inference Endpoints

Create production-ready ML inference APIs with proper error handling and validation:

# Generate FastAPI ML router
bash ./skills/integration-helpers/scripts/add-fastapi-endpoint.sh <model-type> <endpoint-name>

# Model types: classification, regression, text-generation, image-classification, embeddings

What This Creates:

• Pydantic models for request/response validation
• Inference endpoint with proper error handling
• Model loading and caching logic
• Health check endpoint
• Batch prediction support
• Async request handling

Router Structure:

from fastapi import APIRouter, HTTPException, UploadFile
from pydantic import BaseModel, Field
import numpy as np

router = APIRouter(
    prefix="/ml",
    tags=["machine-learning"],
    responses={500: {"description": "Model inference error"}},
)

Example Usage:

# Create text classification endpoint
bash ./skills/integration-helpers/scripts/add-fastapi-endpoint.sh classification sentiment-analysis

# Creates: app/routers/ml_sentiment_analysis.py

2. Request/Response Models

Define type-safe ML inference contracts:

Classification Model:

class ClassificationRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=10000)
    model_version: str | None = None
    return_probabilities: bool = False

class ClassificationResponse(BaseModel):
    prediction: str
    confidence: float = Field(..., ge=0.0, le=1.0)
    probabilities: dict[str, float] | None = None
    model_version: str
    inference_time_ms: float

Regression Model:

class RegressionRequest(BaseModel):
    features: list[float] = Field(..., min_items=1)
    feature_names: list[str] | None = None

class RegressionResponse(BaseModel):
    prediction: float
    feature_importance: dict[str, float] | None = None
    model_version: str

Image Classification:

class ImageClassificationResponse(BaseModel):
    predictions: list[dict[str, Any]]
    top_prediction: str
    confidence: float
    processing_time_ms: float

3. Model Loading and Caching

Implement efficient model loading with caching:

from functools import lru_cache
import joblib
import torch

# Singleton model loader
class ModelLoader:
    _instance = None
    _model = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def load_model(self, model_path: str):
        if self._model is None:
            # Load based on framework
            if model_path.endswith('.pkl'):
                self._model = joblib.load(model_path)
            elif model_path.endswith('.pt'):
                self._model = torch.load(model_path)
            # Add TensorFlow, ONNX, etc.
        return self._model

# Dependency for endpoints
async def get_model():
    loader = ModelLoader()
    return loader.load_model("models/latest.pkl")

4. Inference Endpoints with Error Handling

Implement robust inference with proper error handling:

@router.post("/predict", response_model=ClassificationResponse)
async def predict(
    request: ClassificationRequest,
    model = Depends(get_model)
):
    try:
        start_time = time.time()

        # Preprocess input
        processed_input = preprocess_text(request.text)

        # Run inference
        prediction = model.predict([processed_input])[0]
        probabilities = None

        if request.return_probabilities:
            probs = model.predict_proba([processed_input])[0]
            probabilities = {
                label: float(prob)
                for label, prob in zip(model.classes_, probs)
            }

        inference_time = (time.time() - start_time) * 1000

        return ClassificationResponse(
            prediction=str(prediction),
            confidence=float(max(probs)) if probabilities else 0.0,
            probabilities=probabilities,
            model_version=MODEL_VERSION,
            inference_time_ms=inference_time
        )

    except ValueError as e:
        raise HTTPException(
            status_code=400,
            detail=f"Invalid input: {str(e)}"
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Model inference failed: {str(e)}"
        )

5. Batch Prediction Support

Enable efficient batch inference:

class BatchClassificationRequest(BaseModel):
    texts: list[str] = Field(..., min_items=1, max_items=100)
    model_version: str | None = None

class BatchClassificationResponse(BaseModel):
    predictions: list[ClassificationResponse]
    total_inference_time_ms: float

@router.post("/predict/batch", response_model=BatchClassificationResponse)
async def predict_batch(
    request: BatchClassificationRequest,
    model = Depends(get_model)
):
    start_time = time.time()
    predictions = []

    # Process in batches for efficiency
    for text in request.texts:
        pred = await predict(
            ClassificationRequest(text=text),
            model=model
        )
        predictions.append(pred)

    total_time = (time.time() - start_time) * 1000

    return BatchClassificationResponse(
        predictions=predictions,
        total_inference_time_ms=total_time
    )

6. Next.js Prediction Forms

Create React components for ML model interaction:

# Generate Next.js prediction form
bash ./skills/integration-helpers/scripts/add-nextjs-component.sh <component-type> <component-name>

# Component types: classification-form, regression-form, image-upload, chat-interface

What This Creates:

• TypeScript React component with shadcn/ui
• Form validation with react-hook-form and zod
• Loading states and error handling
• Result visualization components
• API integration with fetch/axios

Example Component:

// components/ml/sentiment-form.tsx
'use client'

import { useState } from 'react'
import { useForm } from 'react-hook-form'
import { zodResolver } from '@hookform/resolvers/zod'
import * as z from 'zod'
import { Button } from '@/components/ui/button'
import { Textarea } from '@/components/ui/textarea'
import { Card } from '@/components/ui/card'

const formSchema = z.object({
  text: z.string().min(1).max(10000),
})

export function SentimentForm() {
  const [result, setResult] = useState<any>(null)
  const [loading, setLoading] = useState(false)

  const form = useForm<z.infer<typeof formSchema>>({
    resolver: zodResolver(formSchema),
  })

  async function onSubmit(values: z.infer<typeof formSchema>) {
    setLoading(true)
    try {
      const response = await fetch('/api/ml/predict', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify(values),
      })
      const data = await response.json()
      setResult(data)
    } catch (error) {
      console.error(error)
    } finally {
      setLoading(false)
    }
  }

  return (
    <Card className="p-6">
      <form
        <Textarea {...form.register('text')} />
        <Button type="submit" disabled={loading}>
          {loading ? 'Analyzing...' : 'Analyze Sentiment'}
        </Button>
      </form>
      {result && <ResultDisplay result={result} />}
    </Card>
  )
}

7. Result Visualization Components

Display ML predictions with visual feedback:

// Classification result with confidence
function ClassificationResult({ prediction, confidence, probabilities }) {
  return (
    <div className="space-y-4">
      <div className="text-2xl font-bold">{prediction}</div>
      <div className="text-muted-foreground">
        Confidence: {(confidence * 100).toFixed(1)}%
      </div>
      {probabilities && (
        <div className="space-y-2">
          {Object.entries(probabilities).map(([label, prob]) => (
            <div key={label} className="flex items-center gap-2">
              <span className="w-24">{label}</span>
              <div className="flex-1 bg-secondary rounded-full h-2">
                <div
                  className="bg-primary h-2 rounded-full"
                  style={{ width: `${prob * 100}%` }}
                />
              </div>
              <span className="w-12 text-right">{(prob * 100).toFixed(1)}%</span>
            </div>
          ))}
        </div>
      )}
    </div>
  )
}

8. Supabase ML Metadata Schemas

Create database schemas for ML model tracking and results:

# Generate Supabase schema for ML metadata
bash ./skills/integration-helpers/scripts/create-supabase-schema.sh <schema-type>

# Schema types: ml-models, predictions, training-runs, model-versions

ML Models Table:

create table ml_models (
  id uuid default gen_random_uuid() primary key,
  name text not null,
  model_type text not null, -- classification, regression, etc.
  framework text not null, -- scikit-learn, pytorch, tensorflow
  version text not null,
  artifact_url text, -- Cloud storage URL
  metrics jsonb, -- Accuracy, F1, RMSE, etc.
  hyperparameters jsonb,
  feature_names text[],
  target_classes text[],
  is_active boolean default true,
  created_at timestamptz default now(),
  updated_at timestamptz default now(),
  created_by uuid references auth.users(id)
);

create index idx_ml_models_active on ml_models(is_active, created_at desc);
create index idx_ml_models_type on ml_models(model_type);

Predictions Log Table:

create table predictions (
  id uuid default gen_random_uuid() primary key,
  model_id uuid references ml_models(id),
  model_version text not null,
  input_data jsonb not null,
  prediction jsonb not null,
  confidence float,
  inference_time_ms float,
  user_id uuid references auth.users(id),
  session_id text,
  created_at timestamptz default now()
);

create index idx_predictions_model on predictions(model_id, created_at desc);
create index idx_predictions_user on predictions(user_id, created_at desc);
create index idx_predictions_session on predictions(session_id);

Training Runs Table:

create table training_runs (
  id uuid default gen_random_uuid() primary key,
  model_id uuid references ml_models(id),
  dataset_name text not null,
  dataset_size integer,
  train_test_split jsonb, -- {train: 0.8, test: 0.2}
  hyperparameters jsonb,
  training_metrics jsonb, -- Loss curves, accuracy per epoch
  validation_metrics jsonb,
  test_metrics jsonb,
  training_duration_seconds integer,
  status text default 'running', -- running, completed, failed
  error_message text,
  artifact_url text,
  created_at timestamptz default now(),
  completed_at timestamptz,
  created_by uuid references auth.users(id)
);

create index idx_training_runs_model on training_runs(model_id, created_at desc);
create index idx_training_runs_status on training_runs(status);

Model Versions Table:

create table model_versions (
  id uuid default gen_random_uuid() primary key,
  model_id uuid references ml_models(id),
  version text not null,
  changelog text,
  metrics_comparison jsonb, -- Compare with previous version
  is_deployed boolean default false,
  deployment_url text,
  created_at timestamptz default now(),
  deployed_at timestamptz,
  created_by uuid references auth.users(id),
  unique(model_id, version)
);

create index idx_model_versions_deployed on model_versions(model_id, is_deployed);

9. API Route Handlers for Next.js

Create Next.js API routes that call FastAPI backend:

// app/api/ml/predict/route.ts
import { NextRequest, NextResponse } from 'next/server'

const FASTAPI_URL = process.env.FASTAPI_URL || 'http://localhost:8000'

export async function POST(request: NextRequest) {
  try {
    const body = await request.json()

    const response = await fetch(`${FASTAPI_URL}/ml/predict`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(body),
    })

    if (!response.ok) {
      const error = await response.json()
      return NextResponse.json(
        { error: error.detail },
        { status: response.status }
      )
    }

    const data = await response.json()
    return NextResponse.json(data)

  } catch (error) {
    return NextResponse.json(
      { error: 'Failed to connect to ML service' },
      { status: 500 }
    )
  }
}

Examples

Example 1: Complete Sentiment Analysis Integration

# 1. Create FastAPI endpoint
cd /path/to/fastapi-backend
bash plugins/ml-training/skills/integration-helpers/scripts/add-fastapi-endpoint.sh classification sentiment-analysis

# 2. Create Next.js form component
cd /path/to/nextjs-frontend
bash plugins/ml-training/skills/integration-helpers/scripts/add-nextjs-component.sh classification-form sentiment-form

# 3. Create Supabase schema for logging
bash plugins/ml-training/skills/integration-helpers/scripts/create-supabase-schema.sh ml-models
bash plugins/ml-training/skills/integration-helpers/scripts/create-supabase-schema.sh predictions

Result: End-to-end sentiment analysis system with API, UI, and data logging

Example 2: Image Classification Service

# 1. Create image classification endpoint
bash plugins/ml-training/skills/integration-helpers/scripts/add-fastapi-endpoint.sh image-classification image-classifier

# 2. Create image upload component
bash plugins/ml-training/skills/integration-helpers/scripts/add-nextjs-component.sh image-upload image-classifier-form

# 3. Setup model versioning schema
bash plugins/ml-training/skills/integration-helpers/scripts/create-supabase-schema.sh model-versions

Result: Complete image classification service with upload UI and version tracking

Requirements

FastAPI Dependencies:

• FastAPI 0.100+
• Pydantic 2.0+
• scikit-learn, PyTorch, or TensorFlow (based on your model)
• python-multipart (for file uploads)
• joblib or pickle (for model serialization)

Next.js Dependencies:

• Next.js 14+
• React 18+
• shadcn/ui components
• react-hook-form
• zod
• TypeScript

Supabase:

• PostgreSQL 15+
• Row Level Security enabled
• UUID extension enabled

Best Practices

API Design:

• Use proper HTTP status codes (200, 400, 500)
• Implement request validation with Pydantic
• Add rate limiting for production
• Log all predictions for monitoring
• Version your models in URLs or headers

Performance:

• Cache models in memory (singleton pattern)
• Use async endpoints for I/O operations
• Implement batch prediction for efficiency
• Consider model quantization for speed
• Use background tasks for heavy operations

Security:

• Validate all inputs strictly
• Implement authentication for API endpoints
• Use CORS properly in production
• Don't expose model internals in errors
• Rate limit inference endpoints

Monitoring:

• Log inference times
• Track prediction distributions
• Monitor error rates
• Store predictions for retraining
• Set up alerts for degraded performance

Error Handling:

• Return structured error responses
• Differentiate input errors from model errors
• Provide helpful error messages
• Log errors for debugging
• Implement graceful degradation

Plugin: ml-training Version: 1.0.0 Category: ML Integration Skill Type: Integration Templates