File size: 1,540 Bytes
c1b8df7 bea0634 c1b8df7 7cb9779 bea0634 e9ec27a bea0634 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import json
import traceback
import gradio as gr
import spaces
import threading
_generate_lock = threading.Lock()
@spaces.GPU
def generate_stream_gpu(text: str, mode: str, speed_mode: str):
if not _generate_lock.acquire(blocking=False):
yield json.dumps({"type": "status", "content": "Server busy"}) + "\n"
yield json.dumps({"type": "done"}) + "\n"
return
try:
if mode == "literacy":
from masteries.literacy.inference.v4_orchestrator import literacy_pipeline as active_pipeline
elif mode == "research":
from masteries.research.inference.v4_orchestrator import research_pipeline as active_pipeline
else:
from masteries.coding.inference.v4_orchestrator import v4_pipeline as active_pipeline
for event in active_pipeline(text, speed_mode=speed_mode):
yield json.dumps(event) + "\n"
yield json.dumps({"type": "done"}) + "\n"
except Exception as e:
traceback.print_exc()
yield json.dumps({"type": "error", "content": str(e)}) + "\n"
finally:
_generate_lock.release()
demo = gr.Interface(
fn=generate_stream_gpu,
inputs=[
gr.Textbox(label="text"),
gr.Textbox(label="mode", value="coding"),
gr.Textbox(label="speed_mode", value="pro")
],
outputs=gr.Textbox(label="output"),
title="PACE AI Inference Service",
description="Backend AI API for PACE. Used via Gradio Client."
)
if __name__ == "__main__":
demo.launch()
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