On this tutorial, we construct a sophisticated computer-use agent from scratch that may cause, plan, and carry out digital actions utilizing a neighborhood open-weight mannequin. We create a miniature simulated desktop, equip it with a software interface, and design an clever agent that may analyze its atmosphere, determine on actions like clicking or typing, and execute them step-by-step. By the tip, we see how the agent interprets objectives reminiscent of opening emails or taking notes, demonstrating how a neighborhood language mannequin can mimic interactive reasoning and process execution. Take a look at the FULL CODES right here.
!pip set up -q transformers speed up sentencepiece nest_asyncio
import torch, asyncio, uuid
from transformers import pipeline
import nest_asyncio
nest_asyncio.apply()We arrange the environment by putting in important libraries reminiscent of Transformers, Speed up, and Nest Asyncio, which allow us to run native fashions and asynchronous duties seamlessly in Colab. We put together the runtime in order that the upcoming elements of our agent can work effectively with out exterior dependencies. Take a look at the FULL CODES right here.
class LocalLLM:
def __init__(self, model_name="google/flan-t5-small", max_new_tokens=128):
self.pipe = pipeline("text2text-generation", mannequin=model_name, machine=0 if torch.cuda.is_available() else -1)
self.max_new_tokens = max_new_tokens
def generate(self, immediate: str) -> str:
out = self.pipe(immediate, max_new_tokens=self.max_new_tokens, temperature=0.0)[0]["generated_text"]
return out.strip()
class VirtualComputer:
def __init__(self):
self.apps = {"browser": "https://instance.com", "notes": "", "mail": ["Welcome to CUA", "Invoice #221", "Weekly Report"]}
self.focus = "browser"
self.display = "Browser open at https://instance.comnSearch bar targeted."
self.action_log = []
def screenshot(self):
return f"FOCUS:{self.focus}nSCREEN:n{self.display}nAPPS:{checklist(self.apps.keys())}"
def click on(self, goal:str):
if goal in self.apps:
self.focus = goal
if goal=="browser":
self.display = f"Browser tab: {self.apps['browser']}nAddress bar targeted."
elif goal=="notes":
self.display = f"Notes AppnCurrent notes:n{self.apps['notes']}"
elif goal=="mail":
inbox = "n".be part of(f"- {s}" for s in self.apps['mail'])
self.display = f"Mail App Inbox:n{inbox}n(Learn-only preview)"
else:
self.display += f"nClicked '{goal}'."
self.action_log.append({"sort":"click on","goal":goal})
def sort(self, textual content:str):
if self.focus=="browser":
self.apps["browser"] = textual content
self.display = f"Browser tab now at {textual content}nPage headline: Instance Area"
elif self.focus=="notes":
self.apps["notes"] += ("n"+textual content)
self.display = f"Notes AppnCurrent notes:n{self.apps['notes']}"
else:
self.display += f"nTyped '{textual content}' however no editable area."
self.action_log.append({"sort":"sort","textual content":textual content})We outline the core elements, a light-weight native mannequin, and a digital laptop. We use Flan-T5 as our reasoning engine and create a simulated desktop that may open apps, show screens, and reply to typing and clicking actions. Take a look at the FULL CODES right here.
class ComputerTool:
def __init__(self, laptop:VirtualComputer):
self.laptop = laptop
def run(self, command:str, argument:str=""):
if command=="click on":
self.laptop.click on(argument)
return {"standing":"accomplished","consequence":f"clicked {argument}"}
if command=="sort":
self.laptop.sort(argument)
return {"standing":"accomplished","consequence":f"typed {argument}"}
if command=="screenshot":
snap = self.laptop.screenshot()
return {"standing":"accomplished","consequence":snap}
return {"standing":"error","consequence":f"unknown command {command}"}We introduce the ComputerTool interface, which acts because the communication bridge between the agent’s reasoning and the digital desktop. We outline high-level operations reminiscent of click on, sort, and screenshot, enabling the agent to work together with the atmosphere in a structured approach. Take a look at the FULL CODES right here.
class ComputerAgent:
def __init__(self, llm:LocalLLM, software:ComputerTool, max_trajectory_budget:float=5.0):
self.llm = llm
self.software = software
self.max_trajectory_budget = max_trajectory_budget
async def run(self, messages):
user_goal = messages[-1]["content"]
steps_remaining = int(self.max_trajectory_budget)
output_events = []
total_prompt_tokens = 0
total_completion_tokens = 0
whereas steps_remaining>0:
display = self.software.laptop.screenshot()
immediate = (
"You're a computer-use agent.n"
f"Consumer objective: {user_goal}n"
f"Present display:n{display}nn"
"Assume step-by-step.n"
"Reply with: ACTION ARG THEN .n"
)
thought = self.llm.generate(immediate)
total_prompt_tokens += len(immediate.break up())
total_completion_tokens += len(thought.break up())
motion="screenshot"; arg=""; assistant_msg="Working..."
for line in thought.splitlines():
if line.strip().startswith("ACTION "):
after = line.break up("ACTION ",1)[1]
motion = after.break up()[0].strip()
if "ARG " in line:
half = line.break up("ARG ",1)[1]
if " THEN " partly:
arg = half.break up(" THEN ")[0].strip()
else:
arg = half.strip()
if "THEN " in line:
assistant_msg = line.break up("THEN ",1)[1].strip()
output_events.append({"abstract":[{"text":assistant_msg,"type":"summary_text"}],"sort":"reasoning"})
call_id = "call_"+uuid.uuid4().hex[:16]
tool_res = self.software.run(motion, arg)
output_events.append({"motion":{"sort":motion,"textual content":arg},"call_id":call_id,"standing":tool_res["status"],"sort":"computer_call"})
snap = self.software.laptop.screenshot()
output_events.append({"sort":"computer_call_output","call_id":call_id,"output":{"sort":"input_image","image_url":snap}})
output_events.append({"sort":"message","position":"assistant","content material":[{"type":"output_text","text":assistant_msg}]})
if "executed" in assistant_msg.decrease() or "right here is" in assistant_msg.decrease():
break
steps_remaining -= 1
utilization = {"prompt_tokens": total_prompt_tokens,"completion_tokens": total_completion_tokens,"total_tokens": total_prompt_tokens + total_completion_tokens,"response_cost": 0.0}
yield {"output": output_events, "utilization": utilization} We assemble the ComputerAgent, which serves because the system’s clever controller. We program it to cause about objectives, determine which actions to take, execute these by means of the software interface, and document every interplay as a step in its decision-making course of. Take a look at the FULL CODES right here.
async def main_demo():
laptop = VirtualComputer()
software = ComputerTool(laptop)
llm = LocalLLM()
agent = ComputerAgent(llm, software, max_trajectory_budget=4)
messages=[{"role":"user","content":"Open mail, read inbox subjects, and summarize."}]
async for lead to agent.run(messages):
print("==== STREAM RESULT ====")
for occasion in consequence["output"]:
if occasion["type"]=="computer_call":
a = occasion.get("motion",{})
print(f"[TOOL CALL] {a.get('sort')} -> {a.get('textual content')} [{event.get('status')}]")
if occasion["type"]=="computer_call_output":
snap = occasion["output"]["image_url"]
print("SCREEN AFTER ACTION:n", snap[:400],"...n")
if occasion["type"]=="message":
print("ASSISTANT:", occasion["content"][0]["text"], "n")
print("USAGE:", consequence["usage"])
loop = asyncio.get_event_loop()
loop.run_until_complete(main_demo())We carry all the things collectively by operating the demo, the place the agent interprets a consumer’s request and performs duties on the digital laptop. We observe it producing reasoning, executing instructions, updating the digital display, and attaining its objective in a transparent, step-by-step method.
In conclusion, we carried out the essence of a computer-use agent able to autonomous reasoning and interplay. We witness how native language fashions like Flan-T5 can powerfully simulate desktop-level automation inside a protected, text-based sandbox. This undertaking helps us perceive the structure behind clever brokers reminiscent of these in computer-use brokers, bridging pure language reasoning with digital software management. It lays a robust basis for extending these capabilities towards real-world, multimodal, and safe automation techniques.
Take a look at the FULL CODES right here. Be at liberty to take a look at our GitHub Web page for Tutorials, Codes and Notebooks. Additionally, be at liberty to comply with us on Twitter and don’t overlook to affix our 100k+ ML SubReddit and Subscribe to our E-newsletter. Wait! are you on telegram? now you possibly can be part of us on telegram as properly.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.
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