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import gradio as gr
from urllib.parse import urlparse
import subprocess
import threading
import sys
LOG_FILE = "output.log"
def read_logs():
sys.stdout.flush()
with open(LOG_FILE, "r") as f:
return f.read()
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.Popen(
[
"giskard", "worker", "start",
"-u", f"{url}", "-k", f"{api_key}", "-t", f"{hf_token}"
],
stdout=open(LOG_FILE, "w"), stderr=subprocess.STDOUT
)
args = ml_worker.args
print(f"Process {args} exited with {ml_worker.wait()}")
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.terminate()
print("ML worker stopped")
return "ML worker stopped"
return "ML worker not started"
def start_ml_worker(url, api_key, hf_token):
# Always run an external ML worker
stop_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 {url}"
with gr.Blocks() as iface:
with gr.Row():
with gr.Column():
url_input = gr.Textbox(label="Giskard Hub URL")
api_key_input = gr.Textbox(label="Giskard Hub API Key", placeholder="gsk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx")
hf_token_input = gr.Textbox(label="Hugging Face Spaces Token")
output = gr.Textbox(label="Status")
with gr.Row():
run_btn = gr.Button("Run")
run_btn.click(start_ml_worker, [url_input, api_key_input, hf_token_input], output)
stop_btn = gr.Button("Stop")
stop_btn.click(stop_ml_worker, None, output)
logs = gr.Textbox(label="Giskard ML worker log:")
iface.load(read_logs, None, logs, every=0.5)
iface.queue()
iface.launch()
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