"""Illustrative simulation of the GPT-Live full-duplex decision loop.
Teaching model of the described architecture, NOT the real API."""
import random
random.seed(7) # reproducible output
class BackgroundModel: # stands in for GPT-5.5
def run(self, query):
return f"answer to '{query}'"
class GPTLive:
def __init__(self, background):
self.background = background
self.pending = None # a delegated task, if one is running
def decide(self, user_speaking, needs_deep_work):
# A real model makes this choice many times per second.
if self.pending is not None:
return "await_delegate"
if needs_deep_work:
return "delegate"
if user_speaking:
return random.choice(["listen", "backchannel"])
return "speak"
def step(self, frame):
action = self.decide(frame["user_speaking"], frame["needs_deep_work"])
if action == "delegate":
self.pending = frame["query"] # hand off, keep talking
return 'speak -> "one sec, still with you"'
if action == "await_delegate":
result = self.background.run(self.pending) # background result
self.pending = None
return f'speak -> "{result}"'
if action == "backchannel":
return 'backchannel-> "mhmm" (while user talks)'
if action == "listen":
return "listen -> (quiet, attending)"
return "speak -> (normal reply)"
# A short scripted stream of audio frames the loop consumes in order.
stream = [
{"user_speaking": True, "needs_deep_work": False, "query": None},
{"user_speaking": True, "needs_deep_work": False, "query": None},
{"user_speaking": False, "needs_deep_work": True, "query": "weather at 6pm"},
{"user_speaking": False, "needs_deep_work": False, "query": None}, # result returns
{"user_speaking": False, "needs_deep_work": False, "query": None},
]
live = GPTLive(BackgroundModel())
for i, frame in enumerate(stream):
print(f"frame {i}: {live.step(frame)}")Credit: Source link
