diff --git a/.vscode/launch.json b/.vscode/launch.json index dfec828c..3ea4279e 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -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", diff --git a/examples/agent_examples.py b/examples/agent_examples.py index 8416552d..6d7aa796 100644 --- a/examples/agent_examples.py +++ b/examples/agent_examples.py @@ -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, diff --git a/examples/agent_ui_examples.py b/examples/agent_ui_examples.py new file mode 100644 index 00000000..17130f5c --- /dev/null +++ b/examples/agent_ui_examples.py @@ -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) diff --git a/libs/agent/README.md b/libs/agent/README.md index b0fc41e9..29974d5c 100644 --- a/libs/agent/README.md +++ b/libs/agent/README.md @@ -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: diff --git a/libs/agent/agent/__init__.py b/libs/agent/agent/__init__.py index a6ac1216..230eb91a 100644 --- a/libs/agent/agent/__init__.py +++ b/libs/agent/agent/__init__.py @@ -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 diff --git a/libs/agent/agent/core/README.md b/libs/agent/agent/core/README.md deleted file mode 100644 index 9916f7b5..00000000 --- a/libs/agent/agent/core/README.md +++ /dev/null @@ -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 \ No newline at end of file diff --git a/libs/agent/agent/core/agent.py b/libs/agent/agent/core/agent.py index 7b223447..6f2c6278 100644 --- a/libs/agent/agent/core/agent.py +++ b/libs/agent/agent/core/agent.py @@ -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)}") diff --git a/libs/agent/agent/core/factory.py b/libs/agent/agent/core/factory.py index 5b77cdc7..091f6c34 100644 --- a/libs/agent/agent/core/factory.py +++ b/libs/agent/agent/core/factory.py @@ -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}") diff --git a/libs/agent/agent/core/provider_config.py b/libs/agent/agent/core/provider_config.py index 68b69e2e..21a5d283 100644 --- a/libs/agent/agent/core/provider_config.py +++ b/libs/agent/agent/core/provider_config.py @@ -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 } diff --git a/libs/agent/agent/core/types.py b/libs/agent/agent/core/types.py index b9dad859..c1da69b1 100644 --- a/libs/agent/agent/core/types.py +++ b/libs/agent/agent/core/types.py @@ -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.""" diff --git a/libs/agent/agent/providers/omni/__init__.py b/libs/agent/agent/providers/omni/__init__.py index c09fb386..a93b8577 100644 --- a/libs/agent/agent/providers/omni/__init__.py +++ b/libs/agent/agent/providers/omni/__init__.py @@ -1,6 +1,6 @@ """Omni provider implementation.""" -from .types import LLMProvider +from ...core.types import LLMProvider from .image_utils import ( decode_base64_image, ) diff --git a/libs/agent/agent/providers/omni/clients/oaicompat.py b/libs/agent/agent/providers/omni/clients/oaicompat.py new file mode 100644 index 00000000..bddc95e2 --- /dev/null +++ b/libs/agent/agent/providers/omni/clients/oaicompat.py @@ -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 diff --git a/libs/agent/agent/providers/omni/loop.py b/libs/agent/agent/providers/omni/loop.py index d5fa930f..33124b25 100644 --- a/libs/agent/agent/providers/omni/loop.py +++ b/libs/agent/agent/providers/omni/loop.py @@ -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: diff --git a/libs/agent/agent/providers/omni/tools/manager.py b/libs/agent/agent/providers/omni/tools/manager.py index 9e8deafe..bce3a4ad 100644 --- a/libs/agent/agent/providers/omni/tools/manager.py +++ b/libs/agent/agent/providers/omni/tools/manager.py @@ -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): diff --git a/libs/agent/agent/providers/omni/types.py b/libs/agent/agent/providers/omni/types.py deleted file mode 100644 index c0d9837b..00000000 --- a/libs/agent/agent/providers/omni/types.py +++ /dev/null @@ -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", -} diff --git a/libs/agent/agent/ui/__init__.py b/libs/agent/agent/ui/__init__.py new file mode 100644 index 00000000..9ca1a249 --- /dev/null +++ b/libs/agent/agent/ui/__init__.py @@ -0,0 +1 @@ +"""UI modules for the Computer-Use Agent.""" diff --git a/libs/agent/agent/ui/gradio/__init__.py b/libs/agent/agent/ui/gradio/__init__.py new file mode 100644 index 00000000..42fc3c27 --- /dev/null +++ b/libs/agent/agent/ui/gradio/__init__.py @@ -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) diff --git a/libs/agent/agent/ui/gradio/app.py b/libs/agent/agent/ui/gradio/app.py new file mode 100644 index 00000000..4fb4b4fd --- /dev/null +++ b/libs/agent/agent/ui/gradio/app.py @@ -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( + """ + +
+ + +
+ """ + ) + + # 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.
Built with github.com/trycua/cua." + ) + + 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() diff --git a/libs/agent/pdm.lock b/libs/agent/pdm.lock deleted file mode 100644 index 730dc2f2..00000000 --- a/libs/agent/pdm.lock +++ /dev/null @@ -1,910 +0,0 @@ -# This file is @generated by PDM. -# It is not intended for manual editing. - 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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": { diff --git a/notebooks/gradio_agent_nb.ipynb b/notebooks/gradio_agent_nb.ipynb deleted file mode 100644 index 034861b1..00000000 --- a/notebooks/gradio_agent_nb.ipynb +++ /dev/null @@ -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": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "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 -}