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import pdb
import logging
from dotenv import load_dotenv
load_dotenv()
import os
import glob
import asyncio
import argparse
import os
logger = logging.getLogger(__name__)
import gradio as gr
import inspect
from functools import wraps
from browser_use.agent.service import Agent
from playwright.async_api import async_playwright
from browser_use.browser.browser import Browser, BrowserConfig
from browser_use.browser.context import (
BrowserContextConfig,
BrowserContextWindowSize,
)
from langchain_ollama import ChatOllama
from playwright.async_api import async_playwright
from src.utils.agent_state import AgentState
from src.utils import utils
from src.agent.custom_agent import CustomAgent
from src.browser.custom_browser import CustomBrowser
from src.agent.custom_prompts import CustomSystemPrompt, CustomAgentMessagePrompt
from src.browser.custom_context import BrowserContextConfig, CustomBrowserContext
from src.controller.custom_controller import CustomController
from gradio.themes import Citrus, Default, Glass, Monochrome, Ocean, Origin, Soft, Base
from src.utils.utils import update_model_dropdown, get_latest_files, capture_screenshot, MissingAPIKeyError
from src.utils import utils
# Global variables for persistence
_global_browser = None
_global_browser_context = None
_global_agent = None
# Create the global agent state instance
_global_agent_state = AgentState()
# webui config
webui_config_manager = utils.ConfigManager()
def scan_and_register_components(blocks):
"""扫描一个 Blocks 对象并注册其中的所有交互式组件，但不包括按钮"""
global webui_config_manager
def traverse_blocks(block, prefix=""):
registered = 0
# 处理 Blocks 自身的组件
if hasattr(block, "children"):
for i, child in enumerate(block.children):
if isinstance(child, gr.components.Component):
# 排除按钮 (Button) 组件
if getattr(child, "interactive", False) and not isinstance(child, gr.Button):
name = f"{prefix}component_{i}"
if hasattr(child, "label") and child.label:
# 使用标签作为名称的一部分
label = child.label
name = f"{prefix}{label}"
logger.debug(f"Registering component: {name}")
webui_config_manager.register_component(name, child)
registered += 1
elif hasattr(child, "children"):
# 递归处理嵌套的 Blocks
new_prefix = f"{prefix}block_{i}_"
registered += traverse_blocks(child, new_prefix)
return registered
total = traverse_blocks(blocks)
logger.info(f"Total registered components: {total}")
def save_current_config():
return webui_config_manager.save_current_config()
def update_ui_from_config(config_file):
return webui_config_manager.update_ui_from_config(config_file)
def resolve_sensitive_env_variables(text):
"""
Replace environment variable placeholders ($SENSITIVE_*) with their values.
Only replaces variables that start with SENSITIVE_.
"""
if not text:
return text
import re
# Find all $SENSITIVE_* patterns
env_vars = re.findall(r'\$SENSITIVE_[A-Za-z0-9_]*', text)
result = text
for var in env_vars:
# Remove the $ prefix to get the actual environment variable name
env_name = var[1:] # removes the $
env_value = os.getenv(env_name)
if env_value is not None:
# Replace $SENSITIVE_VAR_NAME with its value
result = result.replace(var, env_value)
return result
async def stop_agent():
"""Request the agent to stop and update UI with enhanced feedback"""
global _global_agent
try:
if _global_agent is not None:
# Request stop
_global_agent.stop()
# Update UI immediately
message = "Stop requested - the agent will halt at the next safe point"
logger.info(f"🛑 {message}")
# Return UI updates
return (
gr.update(value="Stopping...", interactive=False), # stop_button
gr.update(interactive=False), # run_button
)
except Exception as e:
error_msg = f"Error during stop: {str(e)}"
logger.error(error_msg)
return (
gr.update(value="Stop", interactive=True),
gr.update(interactive=True)
)
async def stop_research_agent():
"""Request the agent to stop and update UI with enhanced feedback"""
global _global_agent_state
try:
# Request stop
_global_agent_state.request_stop()
# Update UI immediately
message = "Stop requested - the agent will halt at the next safe point"
logger.info(f"🛑 {message}")
# Return UI updates
return ( # errors_output
gr.update(value="Stopping...", interactive=False), # stop_button
gr.update(interactive=False), # run_button
)
except Exception as e:
error_msg = f"Error during stop: {str(e)}"
logger.error(error_msg)
return (
gr.update(value="Stop", interactive=True),
gr.update(interactive=True)
)
async def run_browser_agent(
agent_type,
llm_provider,
llm_model_name,
llm_num_ctx,
llm_temperature,
llm_base_url,
llm_api_key,
use_own_browser,
keep_browser_open,
headless,
disable_security,
window_w,
window_h,
save_recording_path,
save_agent_history_path,
save_trace_path,
enable_recording,
task,
add_infos,
max_steps,
use_vision,
max_actions_per_step,
tool_calling_method,
chrome_cdp,
max_input_tokens
):
try:
# Disable recording if the checkbox is unchecked
if not enable_recording:
save_recording_path = None
# Ensure the recording directory exists if recording is enabled
if save_recording_path:
os.makedirs(save_recording_path, exist_ok=True)
# Get the list of existing videos before the agent runs
existing_videos = set()
if save_recording_path:
existing_videos = set(
glob.glob(os.path.join(save_recording_path, "*.[mM][pP]4"))
+ glob.glob(os.path.join(save_recording_path, "*.[wW][eE][bB][mM]"))
)
task = resolve_sensitive_env_variables(task)
# Run the agent
llm = utils.get_llm_model(
provider=llm_provider,
model_name=llm_model_name,
num_ctx=llm_num_ctx,
temperature=llm_temperature,
base_url=llm_base_url,
api_key=llm_api_key,
)
if agent_type == "org":
final_result, errors, model_actions, model_thoughts, trace_file, history_file = await run_org_agent(
llm=llm,
use_own_browser=use_own_browser,
keep_browser_open=keep_browser_open,
headless=headless,
disable_security=disable_security,
window_w=window_w,
window_h=window_h,
save_recording_path=save_recording_path,
save_agent_history_path=save_agent_history_path,
save_trace_path=save_trace_path,
task=task,
max_steps=max_steps,
use_vision=use_vision,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
chrome_cdp=chrome_cdp,
max_input_tokens=max_input_tokens
)
elif agent_type == "custom":
final_result, errors, model_actions, model_thoughts, trace_file, history_file = await run_custom_agent(
llm=llm,
use_own_browser=use_own_browser,
keep_browser_open=keep_browser_open,
headless=headless,
disable_security=disable_security,
window_w=window_w,
window_h=window_h,
save_recording_path=save_recording_path,
save_agent_history_path=save_agent_history_path,
save_trace_path=save_trace_path,
task=task,
add_infos=add_infos,
max_steps=max_steps,
use_vision=use_vision,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
chrome_cdp=chrome_cdp,
max_input_tokens=max_input_tokens
)
else:
raise ValueError(f"Invalid agent type: {agent_type}")
# Get the list of videos after the agent runs (if recording is enabled)
# latest_video = None
# if save_recording_path:
# new_videos = set(
# glob.glob(os.path.join(save_recording_path, "*.[mM][pP]4"))
# + glob.glob(os.path.join(save_recording_path, "*.[wW][eE][bB][mM]"))
# )
# if new_videos - existing_videos:
# latest_video = list(new_videos - existing_videos)[0] # Get the first new video
gif_path = os.path.join(os.path.dirname(__file__), "agent_history.gif")
return (
final_result,
errors,
model_actions,
model_thoughts,
gif_path,
trace_file,
history_file,
gr.update(value="Stop", interactive=True), # Re-enable stop button
gr.update(interactive=True) # Re-enable run button
)
except MissingAPIKeyError as e:
logger.error(str(e))
raise gr.Error(str(e), print_exception=False)
except Exception as e:
import traceback
traceback.print_exc()
errors = str(e) + "\n" + traceback.format_exc()
return (
'', # final_result
errors, # errors
'', # model_actions
'', # model_thoughts
None, # latest_video
None, # history_file
None, # trace_file
gr.update(value="Stop", interactive=True), # Re-enable stop button
gr.update(interactive=True) # Re-enable run button
)
async def run_org_agent(
llm,
use_own_browser,
keep_browser_open,
headless,
disable_security,
window_w,
window_h,
save_recording_path,
save_agent_history_path,
save_trace_path,
task,
max_steps,
use_vision,
max_actions_per_step,
tool_calling_method,
chrome_cdp,
max_input_tokens
):
try:
global _global_browser, _global_browser_context, _global_agent
extra_chromium_args = ["--accept_downloads=True", f"--window-size={window_w},{window_h}"]
cdp_url = chrome_cdp
if use_own_browser:
cdp_url = os.getenv("CHROME_CDP", chrome_cdp)
chrome_path = os.getenv("CHROME_PATH", None)
if chrome_path == "":
chrome_path = None
chrome_user_data = os.getenv("CHROME_USER_DATA", None)
if chrome_user_data:
extra_chromium_args += [f"--user-data-dir={chrome_user_data}"]
else:
chrome_path = None
if _global_browser is None:
_global_browser = Browser(
config=BrowserConfig(
headless=headless,
cdp_url=cdp_url,
disable_security=disable_security,
chrome_instance_path=chrome_path,
extra_chromium_args=extra_chromium_args,
)
)
if _global_browser_context is None:
_global_browser_context = await _global_browser.new_context(
config=BrowserContextConfig(
trace_path=save_trace_path if save_trace_path else None,
save_recording_path=save_recording_path if save_recording_path else None,
save_downloads_path="./tmp/downloads",
no_viewport=False,
browser_window_size=BrowserContextWindowSize(
width=window_w, height=window_h
),
)
)
if _global_agent is None:
_global_agent = Agent(
task=task,
llm=llm,
use_vision=use_vision,
browser=_global_browser,
browser_context=_global_browser_context,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
max_input_tokens=max_input_tokens,
generate_gif=True
)
history = await _global_agent.run(max_steps=max_steps)
history_file = os.path.join(save_agent_history_path, f"{_global_agent.state.agent_id}.json")
_global_agent.save_history(history_file)
final_result = history.final_result()
errors = history.errors()
model_actions = history.model_actions()
model_thoughts = history.model_thoughts()
trace_file = get_latest_files(save_trace_path)
return final_result, errors, model_actions, model_thoughts, trace_file.get('.zip'), history_file
except Exception as e:
import traceback
traceback.print_exc()
errors = str(e) + "\n" + traceback.format_exc()
return '', errors, '', '', None, None
finally:
_global_agent = None
# Handle cleanup based on persistence configuration
if not keep_browser_open:
if _global_browser_context:
await _global_browser_context.close()
_global_browser_context = None
if _global_browser:
await _global_browser.close()
_global_browser = None
async def run_custom_agent(
llm,
use_own_browser,
keep_browser_open,
headless,
disable_security,
window_w,
window_h,
save_recording_path,
save_agent_history_path,
save_trace_path,
task,
add_infos,
max_steps,
use_vision,
max_actions_per_step,
tool_calling_method,
chrome_cdp,
max_input_tokens
):
try:
global _global_browser, _global_browser_context, _global_agent
extra_chromium_args = ["--accept_downloads=True", f"--window-size={window_w},{window_h}"]
cdp_url = chrome_cdp
if use_own_browser:
cdp_url = os.getenv("CHROME_CDP", chrome_cdp)
chrome_path = os.getenv("CHROME_PATH", None)
if chrome_path == "":
chrome_path = None
chrome_user_data = os.getenv("CHROME_USER_DATA", None)
if chrome_user_data:
extra_chromium_args += [f"--user-data-dir={chrome_user_data}"]
else:
chrome_path = None
controller = CustomController()
# Initialize global browser if needed
# if chrome_cdp not empty string nor None
if (_global_browser is None) or (cdp_url and cdp_url != "" and cdp_url != None):
_global_browser = CustomBrowser(
config=BrowserConfig(
headless=headless,
disable_security=disable_security,
cdp_url=cdp_url,
chrome_instance_path=chrome_path,
extra_chromium_args=extra_chromium_args,
)
)
if _global_browser_context is None or (chrome_cdp and cdp_url != "" and cdp_url != None):
_global_browser_context = await _global_browser.new_context(
config=BrowserContextConfig(
trace_path=save_trace_path if save_trace_path else None,
save_recording_path=save_recording_path if save_recording_path else None,
no_viewport=False,
save_downloads_path="./tmp/downloads",
browser_window_size=BrowserContextWindowSize(
width=window_w, height=window_h
),
)
)
# Create and run agent
if _global_agent is None:
_global_agent = CustomAgent(
task=task,
add_infos=add_infos,
use_vision=use_vision,
llm=llm,
browser=_global_browser,
browser_context=_global_browser_context,
controller=controller,
system_prompt_class=CustomSystemPrompt,
agent_prompt_class=CustomAgentMessagePrompt,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
max_input_tokens=max_input_tokens,
generate_gif=True
)
history = await _global_agent.run(max_steps=max_steps)
history_file = os.path.join(save_agent_history_path, f"{_global_agent.state.agent_id}.json")
_global_agent.save_history(history_file)
final_result = history.final_result()
errors = history.errors()
model_actions = history.model_actions()
model_thoughts = history.model_thoughts()
trace_file = get_latest_files(save_trace_path)
return final_result, errors, model_actions, model_thoughts, trace_file.get('.zip'), history_file
except Exception as e:
import traceback
traceback.print_exc()
errors = str(e) + "\n" + traceback.format_exc()
return '', errors, '', '', None, None
finally:
_global_agent = None
# Handle cleanup based on persistence configuration
if not keep_browser_open:
if _global_browser_context:
await _global_browser_context.close()
_global_browser_context = None
if _global_browser:
await _global_browser.close()
_global_browser = None
async def run_with_stream(
agent_type,
llm_provider,
llm_model_name,
llm_num_ctx,
llm_temperature,
llm_base_url,
llm_api_key,
use_own_browser,
keep_browser_open,
headless,
disable_security,
window_w,
window_h,
save_recording_path,
save_agent_history_path,
save_trace_path,
enable_recording,
task,
add_infos,
max_steps,
use_vision,
max_actions_per_step,
tool_calling_method,
chrome_cdp,
max_input_tokens
):
global _global_agent
stream_vw = 80
stream_vh = int(80 * window_h // window_w)
if not headless:
result = await run_browser_agent(
agent_type=agent_type,
llm_provider=llm_provider,
llm_model_name=llm_model_name,
llm_num_ctx=llm_num_ctx,
llm_temperature=llm_temperature,
llm_base_url=llm_base_url,
llm_api_key=llm_api_key,
use_own_browser=use_own_browser,
keep_browser_open=keep_browser_open,
headless=headless,
disable_security=disable_security,
window_w=window_w,
window_h=window_h,
save_recording_path=save_recording_path,
save_agent_history_path=save_agent_history_path,
save_trace_path=save_trace_path,
enable_recording=enable_recording,
task=task,
add_infos=add_infos,
max_steps=max_steps,
use_vision=use_vision,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
chrome_cdp=chrome_cdp,
max_input_tokens=max_input_tokens
)
# Add HTML content at the start of the result array
yield [gr.update(visible=False)] + list(result)
else:
try:
# Run the browser agent in the background
agent_task = asyncio.create_task(
run_browser_agent(
agent_type=agent_type,
llm_provider=llm_provider,
llm_model_name=llm_model_name,
llm_num_ctx=llm_num_ctx,
llm_temperature=llm_temperature,
llm_base_url=llm_base_url,
llm_api_key=llm_api_key,
use_own_browser=use_own_browser,
keep_browser_open=keep_browser_open,
headless=headless,
disable_security=disable_security,
window_w=window_w,
window_h=window_h,
save_recording_path=save_recording_path,
save_agent_history_path=save_agent_history_path,
save_trace_path=save_trace_path,
enable_recording=enable_recording,
task=task,
add_infos=add_infos,
max_steps=max_steps,
use_vision=use_vision,
max_actions_per_step=max_actions_per_step,
tool_calling_method=tool_calling_method,
chrome_cdp=chrome_cdp,
max_input_tokens=max_input_tokens
)
)
# Initialize values for streaming
html_content = f"<h1 style='width:{stream_vw}vw; height:{stream_vh}vh'>Using browser...</h1>"
final_result = errors = model_actions = model_thoughts = ""
recording_gif = trace = history_file = None
# Periodically update the stream while the agent task is running
while not agent_task.done():
try:
encoded_screenshot = await capture_screenshot(_global_browser_context)
if encoded_screenshot is not None:
html_content = f'<img src="data:image/jpeg;base64,{encoded_screenshot}" style="width:{stream_vw}vw; height:{stream_vh}vh ; border:1px solid #ccc;">'
else:
html_content = f"<h1 style='width:{stream_vw}vw; height:{stream_vh}vh'>Waiting for browser session...</h1>"
except Exception as e:
html_content = f"<h1 style='width:{stream_vw}vw; height:{stream_vh}vh'>Waiting for browser session...</h1>"
if _global_agent and _global_agent.state.stopped:
yield [
gr.HTML(value=html_content, visible=True),
final_result,
errors,
model_actions,
model_thoughts,
recording_gif,
trace,
history_file,
gr.update(value="Stopping...", interactive=False), # stop_button
gr.update(interactive=False), # run_button
]
break
else:
yield [
gr.HTML(value=html_content, visible=True),
final_result,
errors,
model_actions,
model_thoughts,
recording_gif,
trace,
history_file,
gr.update(), # Re-enable stop button
gr.update() # Re-enable run button
]
await asyncio.sleep(0.1)
# Once the agent task completes, get the results
try:
result = await agent_task
final_result, errors, model_actions, model_thoughts, recording_gif, trace, history_file, stop_button, run_button = result
except gr.Error:
final_result = ""
model_actions = ""
model_thoughts = ""
recording_gif = trace = history_file = None
except Exception as e:
errors = f"Agent error: {str(e)}"
yield [
gr.HTML(value=html_content, visible=True),
final_result,
errors,
model_actions,
model_thoughts,
recording_gif,
trace,
history_file,
stop_button,
run_button
]
except Exception as e:
import traceback
yield [
gr.HTML(
value=f"<h1 style='width:{stream_vw}vw; height:{stream_vh}vh'>Waiting for browser session...</h1>",
visible=True),
"",
f"Error: {str(e)}\n{traceback.format_exc()}",
"",
"",
None,
None,
None,
gr.update(value="Stop", interactive=True), # Re-enable stop button
gr.update(interactive=True) # Re-enable run button
]
# Define the theme map globally
theme_map = {
"Default": Default(),
"Soft": Soft(),
"Monochrome": Monochrome(),
"Glass": Glass(),
"Origin": Origin(),
"Citrus": Citrus(),
"Ocean": Ocean(),
"Base": Base()
}
async def close_global_browser():
global _global_browser, _global_browser_context
if _global_browser_context:
await _global_browser_context.close()
_global_browser_context = None
if _global_browser:
await _global_browser.close()
_global_browser = None
async def run_deep_search(research_task, max_search_iteration_input, max_query_per_iter_input, llm_provider,
llm_model_name, llm_num_ctx, llm_temperature, llm_base_url, llm_api_key, use_vision,
use_own_browser, headless, chrome_cdp):
from src.utils.deep_research import deep_research
global _global_agent_state
# Clear any previous stop request
_global_agent_state.clear_stop()
llm = utils.get_llm_model(
provider=llm_provider,
model_name=llm_model_name,
num_ctx=llm_num_ctx,
temperature=llm_temperature,
base_url=llm_base_url,
api_key=llm_api_key,
)
markdown_content, file_path = await deep_research(research_task, llm, _global_agent_state,
max_search_iterations=max_search_iteration_input,
max_query_num=max_query_per_iter_input,
use_vision=use_vision,
headless=headless,
use_own_browser=use_own_browser,
chrome_cdp=chrome_cdp
)
return markdown_content, file_path, gr.update(value="Stop", interactive=True), gr.update(interactive=True)
def create_ui(theme_name="Ocean"):
css = """
.gradio-container {
width: 60vw !important;
max-width: 60% !important;
margin-left: auto !important;
margin-right: auto !important;
padding-top: 20px !important;
}
.header-text {
text-align: center;
margin-bottom: 30px;
}
.theme-section {
margin-bottom: 20px;
padding: 15px;
border-radius: 10px;
}
"""
with gr.Blocks(
title="Browser Use WebUI", theme=theme_map[theme_name], css=css
) as demo:
with gr.Row():
gr.Markdown(
"""
# 🌐 Browser Use WebUI
### Control your browser with AI assistance
""",
elem_classes=["header-text"],
)
with gr.Tabs() as tabs:
with gr.TabItem("⚙️ Agent Settings", id=1):
with gr.Group():
agent_type = gr.Radio(
["org", "custom"],
label="Agent Type",
value="custom",
info="Select the type of agent to use",
interactive=True
)
with gr.Column():
max_steps = gr.Slider(
minimum=1,
maximum=200,
value=100,
step=1,
label="Max Run Steps",
info="Maximum number of steps the agent will take",
interactive=True
)
max_actions_per_step = gr.Slider(
minimum=1,
maximum=100,
value=10,
step=1,
label="Max Actions per Step",
info="Maximum number of actions the agent will take per step",
interactive=True
)
with gr.Column():
use_vision = gr.Checkbox(
label="Use Vision",
value=True,
info="Enable visual processing capabilities",
interactive=True
)
max_input_tokens = gr.Number(
label="Max Input Tokens",
value=128000,
precision=0,
interactive=True
)
tool_calling_method = gr.Dropdown(
label="Tool Calling Method",
value="auto",
interactive=True,
allow_custom_value=True, # Allow users to input custom model names
choices=["auto", "json_schema", "function_calling"],
info="Tool Calls Funtion Name",
visible=False
)
with gr.TabItem("🔧 LLM Settings", id=2):
with gr.Group():
llm_provider = gr.Dropdown(
choices=[provider for provider, model in utils.model_names.items()],
label="LLM Provider",
value="openai",
info="Select your preferred language model provider",
interactive=True
)
llm_model_name = gr.Dropdown(
label="Model Name",
choices=utils.model_names['openai'],
value="gpt-4o",
interactive=True,
allow_custom_value=True, # Allow users to input custom model names
info="Select a model in the dropdown options or directly type a custom model name"
)
ollama_num_ctx = gr.Slider(
minimum=2 ** 8,
maximum=2 ** 16,
value=16000,
step=1,
label="Ollama Context Length",
info="Controls max context length model needs to handle (less = faster)",
visible=False,
interactive=True
)
llm_temperature = gr.Slider(
minimum=0.0,
maximum=2.0,
value=0.6,
step=0.1,
label="Temperature",
info="Controls randomness in model outputs",
interactive=True
)
with gr.Row():
llm_base_url = gr.Textbox(
label="Base URL",
value="",
info="API endpoint URL (if required)"
)
llm_api_key = gr.Textbox(
label="API Key",
type="password",
value="",
info="Your API key (leave blank to use .env)"
)
# Change event to update context length slider
def update_llm_num_ctx_visibility(llm_provider):
return gr.update(visible=llm_provider == "ollama")
# Bind the change event of llm_provider to update the visibility of context length slider
llm_provider.change(
fn=update_llm_num_ctx_visibility,
inputs=llm_provider,
outputs=ollama_num_ctx
)
with gr.TabItem("🌐 Browser Settings", id=3):
with gr.Group():
with gr.Row():
use_own_browser = gr.Checkbox(
label="Use Own Browser",
value=False,
info="Use your existing browser instance",
interactive=True
)
keep_browser_open = gr.Checkbox(
label="Keep Browser Open",
value=False,
info="Keep Browser Open between Tasks",
interactive=True
)
headless = gr.Checkbox(
label="Headless Mode",
value=False,
info="Run browser without GUI",
interactive=True
)
disable_security = gr.Checkbox(
label="Disable Security",
value=True,
info="Disable browser security features",
interactive=True
)
enable_recording = gr.Checkbox(
label="Enable Recording",
value=True,
info="Enable saving browser recordings",
interactive=True
)
with gr.Row():
window_w = gr.Number(
label="Window Width",
value=1280,
info="Browser window width",
interactive=True
)
window_h = gr.Number(
label="Window Height",
value=1100,
info="Browser window height",
interactive=True
)
chrome_cdp = gr.Textbox(
label="CDP URL",
placeholder="http://localhost:9222",
value="",
info="CDP for google remote debugging",
interactive=True, # Allow editing only if recording is enabled
)
save_recording_path = gr.Textbox(
label="Recording Path",
placeholder="e.g. ./tmp/record_videos",
value="./tmp/record_videos",
info="Path to save browser recordings",
interactive=True, # Allow editing only if recording is enabled
)
save_trace_path = gr.Textbox(
label="Trace Path",
placeholder="e.g. ./tmp/traces",
value="./tmp/traces",
info="Path to save Agent traces",
interactive=True,
)
save_agent_history_path = gr.Textbox(
label="Agent History Save Path",
placeholder="e.g., ./tmp/agent_history",
value="./tmp/agent_history",
info="Specify the directory where agent history should be saved.",
interactive=True,
)
with gr.TabItem("🤖 Run Agent", id=4):
task = gr.Textbox(
label="Task Description",
lines=4,
placeholder="Enter your task here...",
value="go to google.com and type 'OpenAI' click search and give me the first url",
info="Describe what you want the agent to do",
interactive=True
)
add_infos = gr.Textbox(
label="Additional Information",
lines=3,
placeholder="Add any helpful context or instructions...",
info="Optional hints to help the LLM complete the task",
value="",
interactive=True
)
with gr.Row():
run_button = gr.Button("▶️ Run Agent", variant="primary", scale=2)
stop_button = gr.Button("⏹️ Stop", variant="stop", scale=1)
with gr.Row():
browser_view = gr.HTML(
value="<h1 style='width:80vw; height:50vh'>Waiting for browser session...</h1>",
label="Live Browser View",
visible=False
)
gr.Markdown("### Results")
with gr.Row():
with gr.Column():
final_result_output = gr.Textbox(
label="Final Result", lines=3, show_label=True
)
with gr.Column():
errors_output = gr.Textbox(
label="Errors", lines=3, show_label=True
)
with gr.Row():
with gr.Column():
model_actions_output = gr.Textbox(
label="Model Actions", lines=3, show_label=True, visible=False
)
with gr.Column():
model_thoughts_output = gr.Textbox(
label="Model Thoughts", lines=3, show_label=True, visible=False
)
recording_gif = gr.Image(label="Result GIF", format="gif")
trace_file = gr.File(label="Trace File")
agent_history_file = gr.File(label="Agent History")
with gr.TabItem("🧐 Deep Research", id=5):
research_task_input = gr.Textbox(label="Research Task", lines=5,
value="Compose a report on the use of Reinforcement Learning for training Large Language Models, encompassing its origins, current advancements, and future prospects, substantiated with examples of relevant models and techniques. The report should reflect original insights and analysis, moving beyond mere summarization of existing literature.",
interactive=True)
with gr.Row():
max_search_iteration_input = gr.Number(label="Max Search Iteration", value=3,
precision=0,
interactive=True) # precision=0 确保是整数
max_query_per_iter_input = gr.Number(label="Max Query per Iteration", value=1,
precision=0,
interactive=True) # precision=0 确保是整数
with gr.Row():
research_button = gr.Button("▶️ Run Deep Research", variant="primary", scale=2)
stop_research_button = gr.Button("⏹ Stop", variant="stop", scale=1)
markdown_output_display = gr.Markdown(label="Research Report")
markdown_download = gr.File(label="Download Research Report")
# Bind the stop button click event after errors_output is defined
stop_button.click(
fn=stop_agent,
inputs=[],
outputs=[stop_button, run_button],
)
# Run button click handler
run_button.click(
fn=run_with_stream,
inputs=[
agent_type, llm_provider, llm_model_name, ollama_num_ctx, llm_temperature, llm_base_url,
llm_api_key,
use_own_browser, keep_browser_open, headless, disable_security, window_w, window_h,
save_recording_path, save_agent_history_path, save_trace_path, # Include the new path
enable_recording, task, add_infos, max_steps, use_vision, max_actions_per_step,
tool_calling_method, chrome_cdp, max_input_tokens
],
outputs=[
browser_view, # Browser view
final_result_output, # Final result
errors_output, # Errors
model_actions_output, # Model actions
model_thoughts_output, # Model thoughts
recording_gif, # Latest recording
trace_file, # Trace file
agent_history_file, # Agent history file
stop_button, # Stop button
run_button # Run button
],
)
# Run Deep Research
research_button.click(
fn=run_deep_search,
inputs=[research_task_input, max_search_iteration_input, max_query_per_iter_input, llm_provider,
llm_model_name, ollama_num_ctx, llm_temperature, llm_base_url, llm_api_key, use_vision,
use_own_browser, headless, chrome_cdp],
outputs=[markdown_output_display, markdown_download, stop_research_button, research_button]
)
# Bind the stop button click event after errors_output is defined
stop_research_button.click(
fn=stop_research_agent,
inputs=[],
outputs=[stop_research_button, research_button],
)
with gr.TabItem("🎥 Recordings", id=7, visible=True):
def list_recordings(save_recording_path):
if not os.path.exists(save_recording_path):
return []
# Get all video files
recordings = glob.glob(os.path.join(save_recording_path, "*.[mM][pP]4")) + glob.glob(
os.path.join(save_recording_path, "*.[wW][eE][bB][mM]"))
# Sort recordings by creation time (oldest first)
recordings.sort(key=os.path.getctime)
# Add numbering to the recordings
numbered_recordings = []
for idx, recording in enumerate(recordings, start=1):
filename = os.path.basename(recording)
numbered_recordings.append((recording, f"{idx}. {filename}"))
return numbered_recordings
recordings_gallery = gr.Gallery(
label="Recordings",
columns=3,
height="auto",
object_fit="contain"
)
refresh_button = gr.Button("🔄 Refresh Recordings", variant="secondary")
refresh_button.click(
fn=list_recordings,
inputs=save_recording_path,
outputs=recordings_gallery
)
with gr.TabItem("📁 UI Configuration", id=8):
config_file_input = gr.File(
label="Load UI Settings from Config File",
file_types=[".json"],
interactive=True
)
with gr.Row():
load_config_button = gr.Button("Load Config", variant="primary")
save_config_button = gr.Button("Save UI Settings", variant="primary")
config_status = gr.Textbox(
label="Status",
lines=2,
interactive=False
)
save_config_button.click(
fn=save_current_config,
inputs=[], # 不需要输入参数
outputs=[config_status]
)
# Attach the callback to the LLM provider dropdown
llm_provider.change(
lambda provider, api_key, base_url: update_model_dropdown(provider, api_key, base_url),
inputs=[llm_provider, llm_api_key, llm_base_url],
outputs=llm_model_name
)
# Add this after defining the components
enable_recording.change(
lambda enabled: gr.update(interactive=enabled),
inputs=enable_recording,
outputs=save_recording_path
)
use_own_browser.change(fn=close_global_browser)
keep_browser_open.change(fn=close_global_browser)
scan_and_register_components(demo)
global webui_config_manager
all_components = webui_config_manager.get_all_components()
load_config_button.click(
fn=update_ui_from_config,
inputs=[config_file_input],
outputs=all_components + [config_status]
)
return demo
def main():
parser = argparse.ArgumentParser(description="Gradio UI for Browser Agent")
parser.add_argument("--ip", type=str, default="127.0.0.1", help="IP address to bind to")
parser.add_argument("--port", type=int, default=7788, help="Port to listen on")
parser.add_argument("--theme", type=str, default="Ocean", choices=theme_map.keys(), help="Theme to use for the UI")
args = parser.parse_args()
demo = create_ui(theme_name=args.theme)
demo.launch(server_name="0.0.0.0",share=True, server_port=int(os.environ.get("PORT", 80)))
if __name__ == '__main__':
main()
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