| import json |
| from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer |
| from predict import Predictor |
|
|
| MAX_BODY = 4 * 1024 * 1024 |
| P = Predictor() |
| STATE = {'status': 'SETUP'} |
| SCHEMA = {'outputKind': 'json', 'inputs': [ |
| {'name': 'dataset', 'type': 'string', 'required': True, 'order': 0, |
| 'choices': ['adult', 'bank-marketing', 'credit-g', 'diabetes', 'phoneme'], |
| 'description': 'Model to use (OpenML dataset schema)'}, |
| {'name': 'rows', 'type': 'string', 'required': True, 'order': 1, |
| 'description': 'JSON object or list of up to 1000 objects with the dataset columns'}]} |
|
|
|
|
| class H(BaseHTTPRequestHandler): |
| def send(self, code, obj): |
| b = json.dumps(obj).encode() |
| self.send_response(code) |
| self.send_header('content-type', 'application/json') |
| self.send_header('content-length', str(len(b))) |
| self.end_headers() |
| self.wfile.write(b) |
|
|
| def do_GET(self): |
| if self.path == '/health-check': |
| self.send(200, {'status': STATE['status']}) |
| elif self.path == '/openapi.json': |
| self.send(200, SCHEMA) |
| else: |
| self.send(404, {'error': 'not found'}) |
|
|
| def do_POST(self): |
| if self.path != '/predictions': |
| return self.send(404, {'error': 'not found'}) |
| n = int(self.headers.get('content-length') or 0) |
| if n > MAX_BODY: |
| return self.send(413, {'status': 'failed', 'error': 'body too large'}) |
| try: |
| inp = json.loads(self.rfile.read(n))['input'] |
| out = P.predict(inp['dataset'], inp['rows']) |
| self.send(200, {'status': 'succeeded', 'output': json.loads(out)}) |
| except Exception as e: |
| self.send(200, {'status': 'failed', 'error': f'{type(e).__name__}: {e}'[:300]}) |
|
|
| def log_message(self, *a): |
| pass |
|
|
|
|
| if __name__ == '__main__': |
| P.setup() |
| STATE['status'] = 'READY' |
| ThreadingHTTPServer(('0.0.0.0', 5000), H).serve_forever() |
|
|
