SYNAPSE
————

-- Schema, pipeline, and logic in one file

schema ImageInput {
  path:   str,
  label:  str?,
  width:  int,
  height: int
}

pipeline classify_images {
  source: ImageInput[]
  steps:  [preprocess -> embed -> infer]
  output: ClassResult[]
}

fn preprocess(img: ImageInput) -> Tensor {
  load_image(img.path)
    |> resize(224, 224)
    |> normalize(mean=[0.485, 0.456])
}

async fn infer(t: Tensor) -> ClassResult {
  let model = load_model("resnet50.synmodel")
  model.forward(t)
}

# schema.json + config.yaml + pipeline.py + run.sh

# --- schema.json ---
{ "type": "object",
  "properties": {
    "path": {"type": "string"},
    "label": {"type": "string"} }}

# --- pipeline.py ---
from torchvision import transforms
from PIL import Image
import torch, yaml, json, subprocess

def preprocess(img_path):
    t = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456])
    ])
    return t(Image.open(img_path))

async def infer(tensor):
    model = torch.load("resnet50.pt")
    model.eval()
    with torch.no_grad():
        return model(tensor.unsqueeze(0))