Use a subprocess to run ML worker

app.py

@@ -1,36 +1,14 @@
 import gradio as gr
 
-from giskard.ml_worker.ml_worker import MLWorker
-from pydantic import AnyHttpUrl
-from giskard.settings import settings
 from urllib.parse import urlparse
 
-import asyncio
+import subprocess
 import threading
 
 import sys
 
 LOG_FILE = "output.log"
 
-class Logger:
-    def __init__(self, filename):
-        self.terminal = sys.stdout
-        self.log = open(filename, "w")
-
-    def write(self, message):
-        self.terminal.write(message)
-        self.log.write(message)
-        
-    def flush(self):
-        self.terminal.flush()
-        self.log.flush()
-        
-    def isatty(self):
-        return False    
-
-sys.stdout = Logger(LOG_FILE)
-
-
 def read_logs():
     sys.stdout.flush()
     with open(LOG_FILE, "r") as f:
@@ -40,43 +18,36 @@ def read_logs():
 previous_url = ""
 ml_worker = None
 
+def run_ml_worker(url, api_key, hf_token):
+    global ml_worker, previous_url
+    previous_url = url
+    ml_worker = subprocess.run(
+        [
+            "giskard", "worker", "start",
+            "-u", f"{url}", "-k", f"{api_key}", "-t", f"{hf_token}"
+        ],
+        stdout=open(LOG_FILE, "w"), stderr=sys.stdout
+    )
 
-def run_ml_worker(ml_worker: MLWorker):
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-
-    loop.run_until_complete(ml_worker.start())
-    loop.close()
 
 def stop_ml_worker():
     global ml_worker, previous_url
     if ml_worker is not None:
         print(f"Stopping ML worker for {previous_url}")
-        ml_worker.stop()
+        ml_worker.terminate()
         print("ML worker stopped")
         return "ML worker stopped"
     return "ML worker not started"
 
 
 def start_ml_worker(url, api_key, hf_token):
-    global ml_worker, previous_url
     # Always run an external ML worker
     stop_ml_worker()
 
-    parsed_url = urlparse(url)
-    backend_url = AnyHttpUrl(
-        url=f"{parsed_url.scheme if parsed_url.scheme else 'http'}://{parsed_url.hostname}"
-            f"/{parsed_url.path if parsed_url.path and len(parsed_url.path) else settings.ws_path}",
-        scheme=parsed_url.scheme,
-        host=parsed_url.hostname,
-        path=parsed_url.path if parsed_url.path and len(parsed_url.path) else settings.ws_path,
-    )
-    print(f"Starting ML worker for {backend_url}")
-    ml_worker = MLWorker(False, backend_url, api_key, hf_token)
-    previous_url = backend_url
-    thread = threading.Thread(target=run_ml_worker, args=(ml_worker,))
+    print(f"Starting ML worker for {url}")
+    thread = threading.Thread(target=run_ml_worker, args=(url, api_key, hf_token))
     thread.start()
-    return f"ML worker running for {backend_url}"
+    return f"ML worker running for {url}"
 
 
 with gr.Blocks() as iface: