Merge pull request #93 from trycua/feature/agent/gradio-ui

[Agent] Add Gradio UI & OAI-Compatible Provider
This commit is contained in:
f-trycua
2025-04-06 11:40:31 -07:00
committed by GitHub
24 changed files with 1279 additions and 2194 deletions

13
.vscode/launch.json vendored
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@@ -26,6 +26,19 @@
"PYTHONPATH": "${workspaceFolder:cua-root}/libs/core:${workspaceFolder:cua-root}/libs/computer:${workspaceFolder:cua-root}/libs/agent:${workspaceFolder:cua-root}/libs/som:${workspaceFolder:cua-root}/libs/pylume"
}
},
{
"name": "Run Agent UI Examples",
"type": "debugpy",
"request": "launch",
"program": "examples/agent_ui_examples.py",
"console": "integratedTerminal",
"justMyCode": false,
"python": "${workspaceFolder:cua-root}/.venv/bin/python",
"cwd": "${workspaceFolder:cua-root}",
"env": {
"PYTHONPATH": "${workspaceFolder:cua-root}/libs/core:${workspaceFolder:cua-root}/libs/computer:${workspaceFolder:cua-root}/libs/agent:${workspaceFolder:cua-root}/libs/som:${workspaceFolder:cua-root}/libs/pylume"
}
},
{
"name": "Run PyLume Examples",
"type": "debugpy",

View File

@@ -35,6 +35,11 @@ async def run_agent_example():
# model=LLM(provider=LLMProvider.OPENAI, name="gpt-4.5-preview"),
# model=LLM(provider=LLMProvider.ANTHROPIC, name="claude-3-7-sonnet-20250219"),
model=LLM(provider=LLMProvider.OLLAMA, name="gemma3:4b-it-q4_K_M"),
# model=LLM(
# provider=LLMProvider.OAICOMPAT,
# name="qwen2.5-vl-7b-instruct",
# provider_base_url="http://localhost:1234/v1", # LM Studio local endpoint
# ),
save_trajectory=True,
only_n_most_recent_images=3,
verbosity=logging.DEBUG,

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@@ -0,0 +1,21 @@
#!/usr/bin/env python3
"""
Simple example script for the Computer-Use Agent Gradio UI.
This script launches the advanced Gradio UI for the Computer-Use Agent
with full model selection and configuration options.
It can be run directly from the command line.
"""
from utils import load_dotenv_files
load_dotenv_files()
# Import the create_gradio_ui function
from agent.ui.gradio.app import create_gradio_ui
if __name__ == "__main__":
print("Launching Computer-Use Agent Gradio UI with advanced features...")
app = create_gradio_ui()
app.launch(share=False)

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@@ -32,6 +32,7 @@ pip install "cua-agent[all]"
pip install "cua-agent[openai]" # OpenAI Cua Loop
pip install "cua-agent[anthropic]" # Anthropic Cua Loop
pip install "cua-agent[omni]" # Cua Loop based on OmniParser (includes Ollama for local models)
pip install "cua-agent[ui]" # Gradio UI for the agent
```
## Run
@@ -72,6 +73,44 @@ Refer to these notebooks for step-by-step guides on how to use the Computer-Use
- [Agent Notebook](../../notebooks/agent_nb.ipynb) - Complete examples and workflows
## Using the Gradio UI
The agent includes a Gradio-based user interface for easy interaction. To use it:
```bash
# Install with Gradio support
pip install "cua-agent[ui]"
# Create a simple launcher script
```python
from agent.ui.gradio.app import create_gradio_ui
app = create_gradio_ui()
app.launch(share=False)
```
The Gradio UI provides:
- Selection of different agent loops (OpenAI, Anthropic, OMNI)
- Model selection for each provider
- Configuration of agent parameters
- Chat interface for interacting with the agent
You can also embed the Gradio UI in your own application:
```python
# Import directly in your application
from agent.ui.gradio.app import create_gradio_ui
# Create the UI with advanced features
demo = create_gradio_ui()
demo.launch()
# Or for a simpler interface
from agent.ui.gradio import registry
demo = registry(name='cua:gpt-4o')
demo.launch()
```
## Agent Loops
The `cua-agent` package provides three agent loops variations, based on different CUA models providers and techniques:

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@@ -48,7 +48,7 @@ except Exception as e:
# Other issues with telemetry
logger.warning(f"Error initializing telemetry: {e}")
from .providers.omni.types import LLMProvider, LLM
from .core.types import LLMProvider, LLM
from .core.factory import AgentLoop
from .core.agent import ComputerAgent

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@@ -1,101 +0,0 @@
# Unified ComputerAgent
The `ComputerAgent` class provides a unified implementation that consolidates the previously separate agent implementations (AnthropicComputerAgent and OmniComputerAgent) into a single, configurable class.
## Features
- **Multiple Loop Types**: Switch between different agentic loop implementations using the `loop_type` parameter (Anthropic or Omni).
- **Provider Support**: Use different AI providers (OpenAI, Anthropic, etc.) with the appropriate loop.
- **Trajectory Saving**: Control whether to save screenshots and logs with the `save_trajectory` parameter.
- **Consistent Interface**: Maintains a consistent interface regardless of the underlying loop implementation.
## API Key Requirements
To use the ComputerAgent, you'll need API keys for the providers you want to use:
- For **OpenAI**: Set the `OPENAI_API_KEY` environment variable or pass it directly as `api_key`.
- For **Anthropic**: Set the `ANTHROPIC_API_KEY` environment variable or pass it directly as `api_key`.
- For **Groq**: Set the `GROQ_API_KEY` environment variable or pass it directly as `api_key`.
You can set environment variables in several ways:
```bash
# In your terminal before running the code
export OPENAI_API_KEY=your_api_key_here
# Or in a .env file
OPENAI_API_KEY=your_api_key_here
```
## Usage
Here's how to use the unified ComputerAgent:
```python
from agent.core.agent import ComputerAgent
from agent.types.base import AgenticLoop
from agent.providers.omni.types import LLMProvider
from computer import Computer
# Create a Computer instance
computer = Computer()
# Create an agent with the OMNI loop and OpenAI provider
agent = ComputerAgent(
computer=computer,
loop_type=AgenticLoop.OMNI,
provider=LLMProvider.OPENAI,
model="gpt-4o",
api_key="your_api_key_here", # Can also use OPENAI_API_KEY environment variable
save_trajectory=True,
only_n_most_recent_images=5
)
# Create an agent with the ANTHROPIC loop
agent = ComputerAgent(
computer=computer,
loop_type=AgenticLoop.ANTHROPIC,
model="claude-3-7-sonnet-20250219",
api_key="your_api_key_here", # Can also use ANTHROPIC_API_KEY environment variable
save_trajectory=True,
only_n_most_recent_images=5
)
# Use the agent
async with agent:
async for result in agent.run("Your task description here"):
# Process the result
title = result["metadata"].get("title", "Screen Analysis")
content = result["content"]
print(f"\n{title}")
print(content)
```
## Parameters
- `computer`: Computer instance to control
- `loop_type`: The type of loop to use (AgenticLoop.ANTHROPIC or AgenticLoop.OMNI)
- `provider`: AI provider to use (required for Omni loop)
- `api_key`: Optional API key (will use environment variable if not provided)
- `model`: Optional model name (will use provider default if not specified)
- `save_trajectory`: Whether to save screenshots and logs
- `only_n_most_recent_images`: Only keep N most recent images
- `max_retries`: Maximum number of retry attempts
## Directory Structure
When `save_trajectory` is enabled, the agent will create the following directory structure:
```
experiments/
├── screenshots/ # Screenshots captured during agent execution
└── logs/ # API call logs and other logging information
```
## Extending with New Loop Types
To add a new loop type:
1. Implement a new loop class
2. Add a new value to the `AgenticLoop` enum
3. Update the `_initialize_loop` method in `ComputerAgent` to handle the new loop type

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@@ -6,8 +6,7 @@ import os
from typing import AsyncGenerator, Optional
from computer import Computer
from ..providers.omni.types import LLM
from .. import AgentLoop
from .types import LLM, AgentLoop
from .types import AgentResponse
from .factory import LoopFactory
from .provider_config import DEFAULT_MODELS, ENV_VARS
@@ -75,6 +74,7 @@ class ComputerAgent:
# Use the provided LLM object
self.provider = model.provider
actual_model_name = model.name or DEFAULT_MODELS.get(self.provider, "")
self.provider_base_url = getattr(model, "provider_base_url", None)
# Ensure we have a valid model name
if not actual_model_name:
@@ -86,8 +86,12 @@ class ComputerAgent:
# Get API key from environment if not provided
actual_api_key = api_key or os.environ.get(ENV_VARS[self.provider], "")
# Ollama is local and doesn't require an API key
if not actual_api_key and str(self.provider) != "ollama":
# Ollama and OpenAI-compatible APIs typically don't require an API key
if (
not actual_api_key
and str(self.provider) not in ["ollama", "oaicompat"]
and ENV_VARS[self.provider] != "none"
):
raise ValueError(f"No API key provided for {self.provider}")
# Create the appropriate loop using the factory
@@ -102,6 +106,7 @@ class ComputerAgent:
save_trajectory=save_trajectory,
trajectory_dir=trajectory_dir,
only_n_most_recent_images=only_n_most_recent_images,
provider_base_url=self.provider_base_url,
)
except ValueError as e:
logger.error(f"Failed to create loop: {str(e)}")

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@@ -8,10 +8,6 @@ from computer import Computer
from .types import AgentLoop
from .base import BaseLoop
# For type checking only
if TYPE_CHECKING:
from ..providers.omni.types import LLMProvider
logger = logging.getLogger(__name__)
@@ -33,6 +29,7 @@ class LoopFactory:
trajectory_dir: str = "trajectories",
only_n_most_recent_images: Optional[int] = None,
acknowledge_safety_check_callback: Optional[Callable[[str], Awaitable[bool]]] = None,
provider_base_url: Optional[str] = None,
) -> BaseLoop:
"""Create and return an appropriate loop instance based on type."""
if loop_type == AgentLoop.ANTHROPIC:
@@ -77,7 +74,7 @@ class LoopFactory:
try:
from ..providers.omni.loop import OmniLoop
from ..providers.omni.parser import OmniParser
from ..providers.omni.types import LLMProvider
from .types import LLMProvider
except ImportError:
raise ImportError(
"The 'omni' provider is not installed. "
@@ -99,6 +96,7 @@ class LoopFactory:
base_dir=trajectory_dir,
only_n_most_recent_images=only_n_most_recent_images,
parser=OmniParser(),
provider_base_url=provider_base_url,
)
else:
raise ValueError(f"Unsupported loop type: {loop_type}")

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@@ -1,17 +1,19 @@
"""Provider-specific configurations and constants."""
from ..providers.omni.types import LLMProvider
from .types import LLMProvider
# Default models for different providers
DEFAULT_MODELS = {
LLMProvider.OPENAI: "gpt-4o",
LLMProvider.ANTHROPIC: "claude-3-7-sonnet-20250219",
LLMProvider.OLLAMA: "gemma3:4b-it-q4_K_M",
LLMProvider.OAICOMPAT: "Qwen2.5-VL-7B-Instruct",
}
# Map providers to their environment variable names
ENV_VARS = {
LLMProvider.OPENAI: "OPENAI_API_KEY",
LLMProvider.ANTHROPIC: "ANTHROPIC_API_KEY",
LLMProvider.OLLAMA: "OLLAMA_API_KEY",
LLMProvider.OLLAMA: "none",
LLMProvider.OAICOMPAT: "none", # OpenAI-compatible API typically doesn't require an API key
}

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@@ -1,7 +1,8 @@
"""Core type definitions."""
from typing import Any, Dict, List, Optional, TypedDict, Union
from enum import Enum, auto
from enum import Enum, StrEnum, auto
from dataclasses import dataclass
class AgentLoop(Enum):
@@ -14,6 +15,62 @@ class AgentLoop(Enum):
# Add more loop types as needed
class LLMProvider(StrEnum):
"""Supported LLM providers."""
ANTHROPIC = "anthropic"
OPENAI = "openai"
OLLAMA = "ollama"
OAICOMPAT = "oaicompat"
@dataclass
class LLM:
"""Configuration for LLM model and provider."""
provider: LLMProvider
name: Optional[str] = None
provider_base_url: Optional[str] = None
def __post_init__(self):
"""Set default model name if not provided."""
if self.name is None:
from .provider_config import DEFAULT_MODELS
self.name = DEFAULT_MODELS.get(self.provider)
# Set default provider URL if none provided
if self.provider_base_url is None and self.provider == LLMProvider.OAICOMPAT:
# Default for vLLM
self.provider_base_url = "http://localhost:8000/v1"
# Common alternatives:
# - LM Studio: "http://localhost:1234/v1"
# - LocalAI: "http://localhost:8080/v1"
# - Ollama with OpenAI compatible API: "http://localhost:11434/v1"
# For backward compatibility
LLMModel = LLM
Model = LLM
# Default models for each provider
PROVIDER_TO_DEFAULT_MODEL: Dict[LLMProvider, str] = {
LLMProvider.ANTHROPIC: "claude-3-7-sonnet-20250219",
LLMProvider.OPENAI: "gpt-4o",
LLMProvider.OLLAMA: "gemma3:4b-it-q4_K_M",
LLMProvider.OAICOMPAT: "Qwen2.5-VL-7B-Instruct",
}
# Environment variable names for each provider
PROVIDER_TO_ENV_VAR: Dict[LLMProvider, str] = {
LLMProvider.ANTHROPIC: "ANTHROPIC_API_KEY",
LLMProvider.OPENAI: "OPENAI_API_KEY",
LLMProvider.OLLAMA: "none",
LLMProvider.OAICOMPAT: "none",
}
class AgentResponse(TypedDict, total=False):
"""Agent response format."""

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@@ -1,6 +1,6 @@
"""Omni provider implementation."""
from .types import LLMProvider
from ...core.types import LLMProvider
from .image_utils import (
decode_base64_image,
)

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@@ -0,0 +1,177 @@
"""OpenAI-compatible client implementation."""
import os
import logging
from typing import Dict, List, Optional, Any
import aiohttp
import re
from .base import BaseOmniClient
logger = logging.getLogger(__name__)
# OpenAI-compatible client for the OmniLoop
class OAICompatClient(BaseOmniClient):
"""OpenAI-compatible API client implementation.
This client can be used with any service that implements the OpenAI API protocol, including:
- vLLM
- LM Studio
- LocalAI
- Ollama (with OpenAI compatibility)
- Text Generation WebUI
- Any other service with OpenAI API compatibility
"""
def __init__(
self,
api_key: Optional[str] = None,
model: str = "Qwen2.5-VL-7B-Instruct",
provider_base_url: Optional[str] = "http://localhost:8000/v1",
max_tokens: int = 4096,
temperature: float = 0.0,
):
"""Initialize the OpenAI-compatible client.
Args:
api_key: Not used for local endpoints, usually set to "EMPTY"
model: Model name to use
provider_base_url: API base URL. Typically in the format "http://localhost:PORT/v1"
Examples:
- vLLM: "http://localhost:8000/v1"
- LM Studio: "http://localhost:1234/v1"
- LocalAI: "http://localhost:8080/v1"
- Ollama: "http://localhost:11434/v1"
max_tokens: Maximum tokens to generate
temperature: Generation temperature
"""
super().__init__(api_key="EMPTY", model=model)
self.api_key = "EMPTY" # Local endpoints typically don't require an API key
self.model = model
self.provider_base_url = (
provider_base_url or "http://localhost:8000/v1"
) # Use default if None
self.max_tokens = max_tokens
self.temperature = temperature
def _extract_base64_image(self, text: str) -> Optional[str]:
"""Extract base64 image data from an HTML img tag."""
pattern = r'data:image/[^;]+;base64,([^"]+)'
match = re.search(pattern, text)
return match.group(1) if match else None
def _get_loggable_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Create a loggable version of messages with image data truncated."""
loggable_messages = []
for msg in messages:
if isinstance(msg.get("content"), list):
new_content = []
for content in msg["content"]:
if content.get("type") == "image":
new_content.append(
{"type": "image", "image_url": {"url": "[BASE64_IMAGE_DATA]"}}
)
else:
new_content.append(content)
loggable_messages.append({"role": msg["role"], "content": new_content})
else:
loggable_messages.append(msg)
return loggable_messages
async def run_interleaved(
self, messages: List[Dict[str, Any]], system: str, max_tokens: Optional[int] = None
) -> Dict[str, Any]:
"""Run interleaved chat completion.
Args:
messages: List of message dicts
system: System prompt
max_tokens: Optional max tokens override
Returns:
Response dict
"""
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
final_messages = [{"role": "system", "content": system}]
# Process messages
for item in messages:
if isinstance(item, dict):
if isinstance(item["content"], list):
# Content is already in the correct format
final_messages.append(item)
else:
# Single string content, check for image
base64_img = self._extract_base64_image(item["content"])
if base64_img:
message = {
"role": item["role"],
"content": [
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_img}"},
}
],
}
else:
message = {
"role": item["role"],
"content": [{"type": "text", "text": item["content"]}],
}
final_messages.append(message)
else:
# String content, check for image
base64_img = self._extract_base64_image(item)
if base64_img:
message = {
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_img}"},
}
],
}
else:
message = {"role": "user", "content": [{"type": "text", "text": item}]}
final_messages.append(message)
payload = {"model": self.model, "messages": final_messages, "temperature": self.temperature}
payload["max_tokens"] = max_tokens or self.max_tokens
try:
async with aiohttp.ClientSession() as session:
# Use default base URL if none provided
base_url = self.provider_base_url or "http://localhost:8000/v1"
# Check if the base URL already includes the chat/completions endpoint
endpoint_url = base_url
if not endpoint_url.endswith("/chat/completions"):
# If the URL ends with /v1, append /chat/completions
if endpoint_url.endswith("/v1"):
endpoint_url = f"{endpoint_url}/chat/completions"
# If the URL doesn't end with /v1, make sure it has a proper structure
elif not endpoint_url.endswith("/"):
endpoint_url = f"{endpoint_url}/chat/completions"
else:
endpoint_url = f"{endpoint_url}chat/completions"
# Log the endpoint URL for debugging
logger.debug(f"Using endpoint URL: {endpoint_url}")
async with session.post(endpoint_url, headers=headers, json=payload) as response:
response_json = await response.json()
if response.status != 200:
error_msg = response_json.get("error", {}).get(
"message", str(response_json)
)
logger.error(f"Error in API call: {error_msg}")
raise Exception(f"API error: {error_msg}")
return response_json
except Exception as e:
logger.error(f"Error in API call: {str(e)}")
raise

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@@ -16,10 +16,11 @@ from ...core.messages import StandardMessageManager, ImageRetentionConfig
from .utils import to_openai_agent_response_format
from ...core.types import AgentResponse
from computer import Computer
from .types import LLMProvider
from ...core.types import LLMProvider
from .clients.openai import OpenAIClient
from .clients.anthropic import AnthropicClient
from .clients.ollama import OllamaClient
from .clients.oaicompat import OAICompatClient
from .prompts import SYSTEM_PROMPT
from .api_handler import OmniAPIHandler
from .tools.manager import ToolManager
@@ -60,6 +61,7 @@ class OmniLoop(BaseLoop):
max_retries: int = 3,
retry_delay: float = 1.0,
save_trajectory: bool = True,
provider_base_url: Optional[str] = None,
**kwargs,
):
"""Initialize the loop.
@@ -75,10 +77,12 @@ class OmniLoop(BaseLoop):
max_retries: Maximum number of retries for API calls
retry_delay: Delay between retries in seconds
save_trajectory: Whether to save trajectory data
provider_base_url: Base URL for the API provider (used for OAICOMPAT)
"""
# Set parser and provider before initializing base class
self.parser = parser
self.provider = provider
self.provider_base_url = provider_base_url
# Initialize message manager with image retention config
self.message_manager = StandardMessageManager(
@@ -141,6 +145,12 @@ class OmniLoop(BaseLoop):
api_key=self.api_key,
model=self.model,
)
elif self.provider == LLMProvider.OAICOMPAT:
self.client = OAICompatClient(
api_key="EMPTY", # Local endpoints typically don't require an API key
model=self.model,
provider_base_url=self.provider_base_url,
)
else:
raise ValueError(f"Unsupported provider: {self.provider}")
@@ -171,6 +181,12 @@ class OmniLoop(BaseLoop):
api_key=self.api_key,
model=self.model,
)
elif self.provider == LLMProvider.OAICOMPAT:
self.client = OAICompatClient(
api_key="EMPTY", # Local endpoints typically don't require an API key
model=self.model,
provider_base_url=self.provider_base_url,
)
else:
raise ValueError(f"Unsupported provider: {self.provider}")
@@ -388,6 +404,14 @@ class OmniLoop(BaseLoop):
except (KeyError, TypeError, IndexError) as e:
logger.error(f"Invalid response format: {str(e)}")
return True, action_screenshot_saved
elif self.provider == LLMProvider.OAICOMPAT:
try:
# OpenAI-compatible response format
raw_text = response["choices"][0]["message"]["content"]
standard_content = [{"type": "text", "text": raw_text}]
except (KeyError, TypeError, IndexError) as e:
logger.error(f"Invalid response format: {str(e)}")
return True, action_screenshot_saved
else:
# Assume OpenAI or compatible format
try:

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@@ -7,7 +7,7 @@ from ....core.tools import BaseToolManager, ToolResult
from ....core.tools.collection import ToolCollection
from .computer import ComputerTool
from .bash import BashTool
from ..types import LLMProvider
from ....core.types import LLMProvider
class ToolManager(BaseToolManager):

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@@ -1,47 +0,0 @@
"""Type definitions for the Omni provider."""
from enum import StrEnum
from typing import Dict, Optional
from dataclasses import dataclass
class LLMProvider(StrEnum):
"""Supported LLM providers."""
ANTHROPIC = "anthropic"
OMNI = "omni"
OPENAI = "openai"
OLLAMA = "ollama"
@dataclass
class LLM:
"""Configuration for LLM model and provider."""
provider: LLMProvider
name: Optional[str] = None
def __post_init__(self):
"""Set default model name if not provided."""
if self.name is None:
self.name = PROVIDER_TO_DEFAULT_MODEL.get(self.provider)
# For backward compatibility
LLMModel = LLM
Model = LLM
# Default models for each provider
PROVIDER_TO_DEFAULT_MODEL: Dict[LLMProvider, str] = {
LLMProvider.ANTHROPIC: "claude-3-7-sonnet-20250219",
LLMProvider.OPENAI: "gpt-4o",
LLMProvider.OLLAMA: "gemma3:4b-it-q4_K_M",
}
# Environment variable names for each provider
PROVIDER_TO_ENV_VAR: Dict[LLMProvider, str] = {
LLMProvider.ANTHROPIC: "ANTHROPIC_API_KEY",
LLMProvider.OPENAI: "OPENAI_API_KEY",
LLMProvider.OLLAMA: "none",
}

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@@ -0,0 +1 @@
"""UI modules for the Computer-Use Agent."""

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@@ -0,0 +1,21 @@
"""Gradio UI for Computer-Use Agent."""
import gradio as gr
from typing import Optional
from .app import create_gradio_ui
def registry(name: str = "cua:gpt-4o") -> gr.Blocks:
"""Create and register a Gradio UI for the Computer-Use Agent.
Args:
name: The name to use for the Gradio app, in format 'provider:model'
Returns:
A Gradio Blocks application
"""
provider, model = name.split(":", 1) if ":" in name else ("openai", name)
# Create and return the Gradio UI
return create_gradio_ui(provider_name=provider, model_name=model)

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@@ -0,0 +1,872 @@
"""
Advanced Gradio UI for Computer-Use Agent
This is a Gradio interface for the Computer-Use Agent
with an advanced UI for model selection and configuration.
Supported Agent Loops and Models:
- AgentLoop.OPENAI: Uses OpenAI Operator CUA model
• computer_use_preview
- AgentLoop.ANTHROPIC: Uses Anthropic Computer-Use models
• claude-3-5-sonnet-20240620
• claude-3-7-sonnet-20250219
- AgentLoop.OMNI (experimental): Uses OmniParser for element pixel-detection
• claude-3-5-sonnet-20240620
• claude-3-7-sonnet-20250219
• gpt-4.5-preview
• gpt-4o
• gpt-4
Requirements:
- Mac with Apple Silicon (M1/M2/M3/M4)
- macOS 14 (Sonoma) or newer
- Python 3.10+
- Lume CLI installed (https://github.com/trycua/cua)
- OpenAI or Anthropic API key
"""
import os
import asyncio
import logging
from typing import Dict, List, Optional, AsyncGenerator, Any, Tuple, Union
import gradio as gr
# Import from agent package
from agent.core.types import AgentResponse
from computer import Computer
from agent import ComputerAgent, AgentLoop, LLM, LLMProvider
# Global variables
global_agent = None
global_computer = None
# Map model names to specific provider model names
MODEL_MAPPINGS = {
"openai": {
# Default to operator CUA model
"default": "computer_use_preview",
# Map standard OpenAI model names to CUA-specific model names
"gpt-4-turbo": "computer_use_preview",
"gpt-4o": "computer_use_preview",
"gpt-4": "computer_use_preview",
"gpt-4.5-preview": "computer_use_preview",
},
"anthropic": {
# Default to newest model
"default": "claude-3-7-sonnet-20250219",
# Specific Claude models for CUA
"claude-3-5-sonnet-20240620": "claude-3-5-sonnet-20240620",
"claude-3-7-sonnet-20250219": "claude-3-7-sonnet-20250219",
# Map standard model names to CUA-specific model names
"claude-3-opus": "claude-3-7-sonnet-20250219",
"claude-3-sonnet": "claude-3-5-sonnet-20240620",
"claude-3-5-sonnet": "claude-3-5-sonnet-20240620",
"claude-3-7-sonnet": "claude-3-7-sonnet-20250219",
},
"omni": {
# OMNI works with any of these models
"default": "gpt-4o",
"gpt-4o": "gpt-4o",
"gpt-4": "gpt-4",
"gpt-4.5-preview": "gpt-4.5-preview",
"claude-3-5-sonnet-20240620": "claude-3-5-sonnet-20240620",
"claude-3-7-sonnet-20250219": "claude-3-7-sonnet-20250219",
},
"ollama": {
# For Ollama models, we keep the original name
"default": "llama3", # A common default model
# Don't map other models - we'll use the original name
},
"oaicompat": {
# Default for OpenAI-compatible providers like VLLM
"default": "Qwen2.5-VL-7B-Instruct",
},
}
def get_provider_and_model(model_name: str, loop_provider: str) -> tuple:
"""
Determine the provider and actual model name to use based on the input.
Args:
model_name: The requested model name
loop_provider: The requested agent loop provider
Returns:
tuple: (provider, model_name_to_use, agent_loop)
"""
# Get the agent loop
loop_provider_map = {
"OPENAI": AgentLoop.OPENAI,
"ANTHROPIC": AgentLoop.ANTHROPIC,
"OMNI": AgentLoop.OMNI,
"OMNI-OLLAMA": AgentLoop.OMNI, # Special case for Ollama models with OMNI parser
}
agent_loop = loop_provider_map.get(loop_provider, AgentLoop.OPENAI)
# Set up the provider and model based on the loop and model_name
if agent_loop == AgentLoop.OPENAI:
provider = LLMProvider.OPENAI
model_name_to_use = MODEL_MAPPINGS["openai"].get(
model_name.lower(), MODEL_MAPPINGS["openai"]["default"]
)
elif agent_loop == AgentLoop.ANTHROPIC:
provider = LLMProvider.ANTHROPIC
model_name_to_use = MODEL_MAPPINGS["anthropic"].get(
model_name.lower(), MODEL_MAPPINGS["anthropic"]["default"]
)
elif agent_loop == AgentLoop.OMNI:
# For OMNI, select provider based on model name or loop_provider
if loop_provider == "OMNI-OLLAMA":
provider = LLMProvider.OLLAMA
# For Ollama models from the UI dropdown, we use the model name as is
# No need to parse it - it's already the correct Ollama model name
model_name_to_use = model_name
elif "claude" in model_name.lower():
provider = LLMProvider.ANTHROPIC
model_name_to_use = MODEL_MAPPINGS["omni"].get(
model_name.lower(), MODEL_MAPPINGS["omni"]["default"]
)
elif "gpt" in model_name.lower():
provider = LLMProvider.OPENAI
model_name_to_use = MODEL_MAPPINGS["omni"].get(
model_name.lower(), MODEL_MAPPINGS["omni"]["default"]
)
else:
# Handle custom model names - use the OAICOMPAT provider
provider = LLMProvider.OAICOMPAT
# Use the model name as is without mapping, or use default if empty
model_name_to_use = (
model_name if model_name.strip() else MODEL_MAPPINGS["oaicompat"]["default"]
)
else:
# Default to OpenAI if unrecognized loop
provider = LLMProvider.OPENAI
model_name_to_use = MODEL_MAPPINGS["openai"]["default"]
agent_loop = AgentLoop.OPENAI
return provider, model_name_to_use, agent_loop
def get_ollama_models() -> List[str]:
"""Get available models from Ollama if installed."""
try:
import subprocess
result = subprocess.run(["ollama", "list"], capture_output=True, text=True)
if result.returncode == 0:
lines = result.stdout.strip().split("\n")
if len(lines) < 2: # No models or just header
return []
models = []
# Skip header line
for line in lines[1:]:
parts = line.split()
if parts:
model_name = parts[0]
models.append(f"OMNI: Ollama {model_name}")
return models
return []
except Exception as e:
logging.error(f"Error getting Ollama models: {e}")
return []
def extract_synthesized_text(result: Union[AgentResponse, Dict[str, Any]]) -> str:
"""Extract synthesized text from the agent result."""
synthesized_text = ""
if "output" in result and result["output"]:
for output in result["output"]:
if output.get("type") == "reasoning":
content = output.get("content", "")
if content:
synthesized_text += f"{content}\n"
elif output.get("type") == "message":
# Handle message type outputs - can contain rich content
content = output.get("content", [])
# Content is usually an array of content blocks
if isinstance(content, list):
for block in content:
if isinstance(block, dict) and block.get("type") == "output_text":
text_value = block.get("text", "")
if text_value:
synthesized_text += f"{text_value}\n"
elif output.get("type") == "computer_call":
action = output.get("action", {})
action_type = action.get("type", "")
# Create a descriptive text about the action
if action_type == "click":
button = action.get("button", "")
x = action.get("x", "")
y = action.get("y", "")
synthesized_text += f"Clicked {button} at position ({x}, {y}).\n"
elif action_type == "type":
text = action.get("text", "")
synthesized_text += f"Typed: {text}.\n"
elif action_type == "keypress":
# Extract key correctly from either keys array or key field
if isinstance(action.get("keys"), list):
key = ", ".join(action.get("keys"))
else:
key = action.get("key", "")
synthesized_text += f"Pressed key: {key}\n"
else:
synthesized_text += f"Performed {action_type} action.\n"
return synthesized_text.strip()
def create_computer_instance(verbosity: int = logging.INFO) -> Computer:
"""Create or get the global Computer instance."""
global global_computer
if global_computer is None:
global_computer = Computer(verbosity=verbosity)
return global_computer
def create_agent(
provider: LLMProvider,
agent_loop: AgentLoop,
model_name: str,
api_key: Optional[str] = None,
save_trajectory: bool = True,
only_n_most_recent_images: int = 3,
verbosity: int = logging.INFO,
use_ollama: bool = False,
use_oaicompat: bool = False,
) -> ComputerAgent:
"""Create or update the global agent with the specified parameters."""
global global_agent
# Create the computer if not already done
computer = create_computer_instance(verbosity=verbosity)
# Extra configuration to pass to the agent
extra_config = {}
# For Ollama models, we'll pass use_ollama and the model_name directly
if use_ollama:
extra_config["use_ollama"] = True
extra_config["ollama_model"] = model_name
print(f"DEBUG - Using Ollama with model: {model_name}")
# Get API key from environment if not provided
if api_key is None:
if provider == LLMProvider.OPENAI:
api_key = os.environ.get("OPENAI_API_KEY", "")
elif provider == LLMProvider.ANTHROPIC:
api_key = os.environ.get("ANTHROPIC_API_KEY", "")
# Create LLM model object with appropriate parameters
provider_base_url = "http://localhost:8000/v1" if use_oaicompat else None
if use_oaicompat:
# Special handling for OAICOMPAT - use OPENAI provider with custom base URL
print(f"DEBUG - Creating OAICOMPAT agent with model: {model_name}")
llm = LLM(
provider=provider, # Already set to OPENAI
name=model_name,
provider_base_url=provider_base_url,
)
# Note: Don't pass use_oaicompat to the agent, as it doesn't accept this parameter
elif provider == LLMProvider.OAICOMPAT:
# This path is unlikely to be taken with our current approach
llm = LLM(provider=provider, name=model_name, provider_base_url=provider_base_url)
else:
# For other providers, just use standard parameters
llm = LLM(provider=provider, name=model_name)
# Create or update the agent
if global_agent is None:
global_agent = ComputerAgent(
computer=computer,
loop=agent_loop,
model=llm,
api_key=api_key,
save_trajectory=save_trajectory,
only_n_most_recent_images=only_n_most_recent_images,
verbosity=verbosity,
**extra_config,
)
else:
# Update the existing agent's parameters
global_agent._loop = None # Force recreation of the loop
global_agent.provider = provider
global_agent.loop = agent_loop
global_agent.model = llm
global_agent.api_key = api_key
# Explicitly update these settings to ensure they take effect
global_agent.save_trajectory = save_trajectory
global_agent.only_n_most_recent_images = only_n_most_recent_images
# Update Ollama settings if applicable
if use_ollama:
global_agent.use_ollama = True
global_agent.ollama_model = model_name
else:
global_agent.use_ollama = False
global_agent.ollama_model = None
# Log the updated settings
logging.info(
f"Updated agent settings: save_trajectory={save_trajectory}, recent_images={only_n_most_recent_images}"
)
return global_agent
def process_agent_result(result: Union[AgentResponse, Dict[str, Any]]) -> str:
"""Process agent results for the Gradio UI."""
# Extract text content
text_obj = result.get("text", {})
# For OpenAI's Computer-Use Agent, text field is an object with format property
if (
text_obj
and isinstance(text_obj, dict)
and "format" in text_obj
and not text_obj.get("value", "")
):
content = extract_synthesized_text(result)
else:
# For other types of results, try to get text directly
if isinstance(text_obj, dict):
if "value" in text_obj:
content = text_obj["value"]
elif "text" in text_obj:
content = text_obj["text"]
elif "content" in text_obj:
content = text_obj["content"]
else:
content = ""
else:
content = str(text_obj) if text_obj else ""
# If still no content but we have outputs, create a summary
if not content and "output" in result and result["output"]:
output = result["output"]
for out in output:
if out.get("type") == "reasoning":
content = out.get("content", "")
if content:
break
elif out.get("type") == "computer_call":
action = out.get("action", {})
action_type = action.get("type", "")
if action_type:
content = f"Performing action: {action_type}"
break
# Clean up the text - ensure content is a string
if not isinstance(content, str):
content = str(content) if content else ""
return content
def respond(
message: str,
history: List[Tuple[str, str]],
model_choice, # Accept Gradio Dropdown component
agent_loop, # Accept Gradio Dropdown component
save_trajectory, # Accept Gradio Checkbox component
recent_images, # Accept Gradio Slider component
openai_api_key: Optional[str] = None,
anthropic_api_key: Optional[str] = None,
) -> str:
"""Process a message with the Computer-Use Agent and return the response."""
import asyncio
# Get actual values from Gradio components
model_choice_value = model_choice.value if hasattr(model_choice, "value") else model_choice
agent_loop_value = agent_loop.value if hasattr(agent_loop, "value") else agent_loop
save_trajectory_value = (
save_trajectory.value if hasattr(save_trajectory, "value") else save_trajectory
)
recent_images_value = int(
recent_images.value if hasattr(recent_images, "value") else recent_images
)
# Debug logging
print(f"DEBUG - Model choice object: {type(model_choice)}")
print(f"DEBUG - Model choice value: {model_choice_value}")
print(f"DEBUG - Agent loop value: {agent_loop_value}")
# Create a new event loop for this function call
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
async def _async_respond():
# Extract the loop type and model from the selection
loop_provider = "OPENAI"
if isinstance(model_choice_value, str):
# This is the case for a custom text input from textbox
if agent_loop_value == "OMNI":
loop_provider = "OMNI"
# Use the custom model name as is
model_id = model_choice_value
print(f"DEBUG - Using custom model: {model_id}")
else:
# Handle regular dropdown value as string
if model_choice_value.startswith("OpenAI:"):
loop_provider = "OPENAI"
model_id = model_choice_value.replace("OpenAI: ", "").lower()
elif model_choice_value.startswith("Anthropic:"):
loop_provider = "ANTHROPIC"
model_id = model_choice_value.replace("Anthropic: ", "").lower()
elif model_choice_value.startswith("OMNI:"):
loop_provider = "OMNI"
if "GPT" in model_choice_value:
model_id = model_choice_value.replace("OMNI: OpenAI ", "").lower()
elif "Claude" in model_choice_value:
model_id = model_choice_value.replace("OMNI: ", "").lower()
elif "Ollama" in model_choice_value:
loop_provider = "OMNI-OLLAMA"
# Extract everything after "OMNI: Ollama " which is the full model name (e.g., phi3:latest)
model_id = model_choice_value.replace("OMNI: Ollama ", "")
print(f"DEBUG - Ollama model ID: {model_id}")
else:
model_id = "default"
else:
# Default case
loop_provider = agent_loop_value
model_id = "default"
else:
# Model choice is not a string (shouldn't happen, but handle anyway)
loop_provider = agent_loop_value
model_id = "default"
print(f"DEBUG - Using loop provider: {loop_provider}, model_id: {model_id}")
# Use the mapping function to get provider, model name and agent loop
provider, model_name, agent_loop_type = get_provider_and_model(model_id, loop_provider)
print(
f"DEBUG - After mapping: provider={provider}, model_name={model_name}, agent_loop={agent_loop_type}"
)
# Special handling for OAICOMPAT to bypass provider-specific errors
# Creates the agent with OPENAI provider but using custom model name and provider base URL
is_oaicompat = str(provider) == "oaicompat"
if is_oaicompat:
provider = LLMProvider.OPENAI
# Get API key based on provider
if provider == LLMProvider.OPENAI:
api_key = openai_api_key or os.environ.get("OPENAI_API_KEY", "")
elif provider == LLMProvider.ANTHROPIC:
api_key = anthropic_api_key or os.environ.get("ANTHROPIC_API_KEY", "")
else:
api_key = ""
# Check for settings changes if agent already exists
settings_changed = False
settings_message = ""
if global_agent is not None:
# Safely check if save_trajectory setting changed
current_save_traj = getattr(global_agent, "save_trajectory", None)
if current_save_traj is not None and current_save_traj != save_trajectory_value:
settings_changed = True
settings_message += f"Save trajectory set to: {save_trajectory_value}. "
# Safely check if recent_images setting changed
current_recent_images = getattr(global_agent, "only_n_most_recent_images", None)
if current_recent_images is not None and current_recent_images != recent_images_value:
settings_changed = True
settings_message += f"Recent images set to: {recent_images_value}. "
# Create or update the agent
try:
create_agent(
provider=provider,
agent_loop=agent_loop_type,
model_name=model_name,
api_key=api_key,
save_trajectory=save_trajectory_value,
only_n_most_recent_images=recent_images_value,
use_ollama=loop_provider == "OMNI-OLLAMA",
use_oaicompat=is_oaicompat,
)
if global_agent is None:
return "Failed to create agent. Check API keys and configuration."
except Exception as e:
return f"Error creating agent: {str(e)}"
# Notify about settings changes if needed
if settings_changed:
return f"Settings updated: {settings_message}"
# Collect all responses
response_text = []
# Run the agent
try:
async for result in global_agent.run(message):
# Process result
content = process_agent_result(result)
# Skip empty content
if not content:
continue
# Add content to response list
response_text.append(content)
# Return the full response as a single string
return "\n".join(response_text) if response_text else "Task completed."
except Exception as e:
import traceback
traceback.print_exc()
return f"Error: {str(e)}"
# Run the async function and get the result
try:
result = loop.run_until_complete(_async_respond())
loop.close()
return result
except Exception as e:
loop.close()
import traceback
traceback.print_exc()
return f"Error executing async operation: {str(e)}"
def create_gradio_ui(
provider_name: str = "openai",
model_name: str = "gpt-4o",
) -> gr.Blocks:
"""Create a Gradio UI for the Computer-Use Agent.
Args:
provider_name: The provider to use (e.g., "openai", "anthropic")
model_name: The model to use (e.g., "gpt-4o", "claude-3-7-sonnet")
Returns:
A Gradio Blocks application
"""
# Check for API keys
openai_api_key = os.environ.get("OPENAI_API_KEY", "")
anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY", "")
# Prepare model choices based on available API keys
openai_models = []
anthropic_models = []
omni_models = []
if openai_api_key:
openai_models = ["OpenAI: Computer-Use Preview"]
omni_models += [
"OMNI: OpenAI GPT-4o",
"OMNI: OpenAI GPT-4.5-preview",
]
if anthropic_api_key:
anthropic_models = [
"Anthropic: Claude 3.7 Sonnet (20250219)",
"Anthropic: Claude 3.5 Sonnet (20240620)",
]
omni_models += ["OMNI: Claude 3.7 Sonnet (20250219)", "OMNI: Claude 3.5 Sonnet (20240620)"]
# Get Ollama models for OMNI
ollama_models = get_ollama_models()
if ollama_models:
omni_models += ollama_models
# Format model choices
provider_to_models = {
"OPENAI": openai_models,
"ANTHROPIC": anthropic_models,
"OMNI": omni_models + ["Custom model..."], # Add custom model option
}
# Get initial agent loop and model based on provided parameters
if provider_name.lower() == "openai":
initial_loop = "OPENAI"
initial_model = "OpenAI: Computer-Use Preview" if openai_models else "No models available"
elif provider_name.lower() == "anthropic":
initial_loop = "ANTHROPIC"
initial_model = anthropic_models[0] if anthropic_models else "No models available"
else:
initial_loop = "OMNI"
if model_name == "gpt-4o" and "OMNI: OpenAI GPT-4o" in omni_models:
initial_model = "OMNI: OpenAI GPT-4o"
elif "claude" in model_name.lower() and omni_models:
initial_model = next((m for m in omni_models if "Claude" in m), omni_models[0])
else:
initial_model = omni_models[0] if omni_models else "No models available"
# Example prompts
example_messages = [
"Create a Python virtual environment, install pandas and matplotlib, then plot stock data",
"Open a PDF in Preview, add annotations, and save it as a compressed version",
"Open Safari, search for 'macOS automation tools', and save the first three results as bookmarks",
"Configure SSH keys and set up a connection to a remote server",
]
# Function to update model choices based on agent loop selection
def update_model_choices(loop):
models = provider_to_models.get(loop, [])
if loop == "OMNI":
# For OMNI, include the custom model option
if not models:
models = ["Custom model..."]
elif "Custom model..." not in models:
models.append("Custom model...")
return gr.update(
choices=models, value=models[0] if models else "Custom model...", interactive=True
)
else:
# For other providers, use standard dropdown without custom option
if not models:
return gr.update(
choices=["No models available"], value="No models available", interactive=True
)
return gr.update(choices=models, value=models[0] if models else None, interactive=True)
# Create the Gradio interface with advanced UI
with gr.Blocks(title="Computer-Use Agent") as demo:
with gr.Row():
# Left column for settings
with gr.Column(scale=1):
# Logo with theme-aware styling
gr.HTML(
"""
<style>
.light-logo, .dark-logo {
display: block;
margin: 0 auto;
width: 80px;
}
/* Hide dark logo in light mode */
.dark-logo {
display: none;
}
/* In dark mode, hide light logo and show dark logo */
.dark .light-logo {
display: none;
}
.dark .dark-logo {
display: block;
}
</style>
<div style="display: flex; justify-content: center; margin-bottom: 0.5em">
<img class="light-logo" alt="CUA Logo"
src="https://github.com/trycua/cua/blob/main/img/logo_black.png?raw=true" />
<img class="dark-logo" alt="CUA Logo"
src="https://github.com/trycua/cua/blob/main/img/logo_white.png?raw=true" />
</div>
"""
)
# Add installation prerequisites as a collapsible section
with gr.Accordion("Prerequisites & Installation", open=False):
gr.Markdown(
"""
## Prerequisites
Before using the Computer-Use Agent, you need to set up the Lume daemon and pull the macOS VM image.
### 1. Install Lume daemon
While a lume binary is included with Computer, we recommend installing the standalone version with brew, and starting the lume daemon service:
```bash
sudo /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
```
### 2. Start the Lume daemon service
In a separate terminal:
```bash
lume serve
```
### 3. Pull the pre-built macOS image
```bash
lume pull macos-sequoia-cua:latest --no-cache
```
Initial download requires 80GB storage, but reduces to ~30GB after first run due to macOS's sparse file system.
VMs are stored in `~/.lume`, and locally cached images are stored in `~/.lume/cache`.
### 4. Test the sandbox
```bash
lume run macos-sequoia-cua:latest
```
For more detailed instructions, visit the [CUA GitHub repository](https://github.com/trycua/cua).
"""
)
# Configuration options
agent_loop = gr.Dropdown(
choices=["OPENAI", "ANTHROPIC", "OMNI"],
label="Agent Loop",
value=initial_loop,
info="Select the agent loop provider",
)
# Create model selection dropdown with custom value support for OMNI
model_choice = gr.Dropdown(
choices=provider_to_models.get(initial_loop, ["No models available"]),
label="LLM Provider and Model",
value=initial_model,
info="Select model or choose 'Custom model...' to enter a custom name",
interactive=True,
)
# Add custom model textbox (only visible when "Custom model..." is selected)
custom_model = gr.Textbox(
label="Custom Model Name",
placeholder="Enter custom model name (e.g., Qwen2.5-VL-7B-Instruct)",
value="Qwen2.5-VL-7B-Instruct", # Default value
visible=False, # Initially hidden
interactive=True,
)
save_trajectory = gr.Checkbox(
label="Save Trajectory",
value=True,
info="Save the agent's trajectory for debugging",
interactive=True,
)
recent_images = gr.Slider(
label="Recent Images",
minimum=1,
maximum=10,
value=3,
step=1,
info="Number of recent images to keep in context",
interactive=True,
)
# Right column for chat interface
with gr.Column(scale=2):
# Add instruction text before the chat interface
gr.Markdown(
"Ask me to perform tasks in a virtual macOS environment.<br>Built with <a href='https://github.com/trycua/cua' target='_blank'>github.com/trycua/cua</a>."
)
chatbot = gr.Chatbot()
msg = gr.Textbox(
placeholder="Ask me to perform tasks in a virtual macOS environment"
)
clear = gr.Button("Clear")
# Add examples
example_group = gr.Examples(examples=example_messages, inputs=msg)
# Function to handle chat submission
def chat_submit(message, history):
# Add user message to history
history = history + [(message, None)]
return "", history
# Function to process agent response after user input
def process_response(
history,
model_choice_value,
custom_model_value,
agent_loop_choice,
save_traj,
recent_imgs,
):
if not history:
return history
# Get the last user message
last_user_message = history[-1][0]
# Use custom model value if "Custom model..." is selected
model_to_use = (
custom_model_value
if model_choice_value == "Custom model..."
else model_choice_value
)
# Process with agent
response = respond(
last_user_message,
history[:-1], # History without the last message
model_to_use,
agent_loop_choice,
save_traj,
recent_imgs,
openai_api_key,
anthropic_api_key,
)
# Update the last assistant message
history[-1] = (last_user_message, response)
return history
# Connect the components
msg.submit(chat_submit, [msg, chatbot], [msg, chatbot]).then(
process_response,
[
chatbot,
model_choice,
custom_model,
agent_loop,
save_trajectory,
recent_images,
],
[chatbot],
)
# Clear button functionality
clear.click(lambda: None, None, chatbot, queue=False)
# Connect agent_loop changes to model selection
agent_loop.change(
fn=update_model_choices,
inputs=[agent_loop],
outputs=[model_choice],
queue=False, # Process immediately without queueing
)
# Show/hide custom model textbox based on dropdown selection
def update_custom_model_visibility(model_value):
return gr.update(visible=model_value == "Custom model...")
model_choice.change(
fn=update_custom_model_visibility,
inputs=[model_choice],
outputs=[custom_model],
queue=False, # Process immediately without queueing
)
return demo
def test_cua():
"""Standalone function to launch the Gradio app."""
demo = create_gradio_ui()
demo.launch(share=False) # Don't create a public link
if __name__ == "__main__":
test_cua()

910
libs/agent/pdm.lock generated
View File

@@ -1,910 +0,0 @@
# This file is @generated by PDM.
# It is not intended for manual editing.
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View File

@@ -34,6 +34,10 @@ openai = [
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[tool.pdm]

817
libs/computer/pdm.lock generated
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{file = "typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f"},
{file = "typing_inspection-0.4.0.tar.gz", hash = "sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122"},
]

View File

@@ -172,8 +172,9 @@
"agent = ComputerAgent(\n",
" computer=computer,\n",
" loop=AgentLoop.OMNI,\n",
" model=LLM(provider=LLMProvider.ANTHROPIC, name=\"claude-3-7-sonnet-20250219\"),\n",
" # model=LLM(provider=LLMProvider.ANTHROPIC, name=\"claude-3-7-sonnet-20250219\"),\n",
" # model=LLM(provider=LLMProvider.OPENAI, name=\"gpt-4.5-preview\"),\n",
" model=LLM(provider=LLMProvider.OLLAMA, name=\"gemma3:12b-it-q4_K_M\"),\n",
" save_trajectory=True,\n",
" trajectory_dir=str(Path(\"trajectories\")),\n",
" only_n_most_recent_images=3,\n",
@@ -197,6 +198,25 @@
"\n",
" print(f\"\\n✅ Task {i+1}/{len(tasks)} completed: {task}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the Gradio UI\n",
"\n",
"The agent includes a Gradio-based user interface for easy interaction. To use it:\n",
"\n",
"```python\n",
"from agent.ui.gradio.app import create_gradio_ui\n",
"\n",
"app = create_gradio_ui()\n",
"app.launch(share=False)\n",
"```\n",
"\n",
"The Gradio UI provides:\n",
"- Selection of different agent loops (OpenAI, Anthropic, OMNI)"
]
}
],
"metadata": {

View File

@@ -1,136 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradio UI for Agent\n",
"\n",
"This notebook demonstrates how to use Cua's Agent using a Gradio frontend.\n",
"\n",
"Credits to AK391 for [github.com/AK391/ai-gradio](https://github.com/AK391/ai-gradio)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install 'ai-gradio[cua]'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start the Gradio App"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Get API keys from environment or prompt user\n",
"openai_key = os.getenv(\"OPENAI_API_KEY\") or input(\"Enter your OpenAI API key: \")\n",
"os.environ[\"OPENAI_API_KEY\"] = openai_key"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"UserWarning: The 'tuples' format for chatbot messages is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style 'role' and 'content' keys.\n",
"INFO:httpx:HTTP Request: GET http://127.0.0.1:7862/gradio_api/startup-events \"HTTP/1.1 200 OK\"\n",
"INFO:httpx:HTTP Request: HEAD http://127.0.0.1:7862/ \"HTTP/1.1 200 OK\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7862\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"import ai_gradio\n",
"\n",
"gr.load(\n",
" name='cua:gpt-4', # currently ignored\n",
" src=ai_gradio.registry,\n",
" title=\"Cua - AI Agent\"\n",
").launch()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Available Agent Loop & LLM Providers\n",
"\n",
"Refer to: https://github.com/trycua/cua/tree/main/libs/agent#agent-loops"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cua",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}