diff --git a/agents/agent_types.py b/agents/agent_types.py index 05bf6e3..531cb26 100644 --- a/agents/agent_types.py +++ b/agents/agent_types.py @@ -144,7 +144,7 @@ class AgentImage(AgentType, ImageType): if self._raw is not None: directory = tempfile.mkdtemp() self._path = os.path.join(directory, str(uuid.uuid4()) + ".png") - self._raw.save(self._path) + self._raw.save(self._path, format="png") return self._path if self._tensor is not None: @@ -155,12 +155,11 @@ class AgentImage(AgentType, ImageType): directory = tempfile.mkdtemp() self._path = os.path.join(directory, str(uuid.uuid4()) + ".png") - img.save(self._path, format="png") return self._path - def save(self, output_bytes, format = None, **params): + def save(self, output_bytes, format : str = None, **params): """ Saves the image to a file. Args: diff --git a/agents/agents.py b/agents/agents.py index 0dee846..709ea76 100644 --- a/agents/agents.py +++ b/agents/agents.py @@ -22,7 +22,7 @@ from rich.syntax import Syntax from transformers.utils import is_torch_available from .utils import console, parse_code_blob, parse_json_tool_call, truncate_content from .agent_types import AgentAudio, AgentImage -from .default_tools import BASE_PYTHON_TOOLS, FinalAnswerTool, setup_default_tools +from .default_tools import BASE_PYTHON_TOOLS, FinalAnswerTool from .llm_engine import HfApiEngine, MessageRole from .monitoring import Monitor from .prompts import ( @@ -42,13 +42,11 @@ from .tools import ( DEFAULT_TOOL_DESCRIPTION_TEMPLATE, Tool, get_tool_description_with_args, - load_tool, Toolbox, ) HUGGINGFACE_DEFAULT_TOOLS = {} -_tools_are_initialized = False class AgentError(Exception): """Base class for other agent-related exceptions""" @@ -101,9 +99,12 @@ class PlanningStep: @dataclass class TaskStep: - system_prompt: str task: str +@dataclass +class SystemPromptStep: + system_prompt: str + def format_prompt_with_tools(toolbox: Toolbox, prompt_template: str, tool_description_template: str) -> str: tool_descriptions = toolbox.show_tool_descriptions(tool_description_template) prompt = prompt_template.replace("{{tool_descriptions}}", tool_descriptions) @@ -189,7 +190,7 @@ class BaseAgent: self._toolbox, self.system_prompt_template, self.tool_description_template ) self.system_prompt = format_prompt_with_managed_agents_descriptions(self.system_prompt, self.managed_agents) - self.prompt = None + self.prompt_messages = None self.logs = [] self.task = None self.verbose = verbose @@ -208,8 +209,7 @@ class BaseAgent: """Get the toolbox currently available to the agent""" return self._toolbox - def initialize_for_run(self): - self.token_count = 0 + def initialize_system_prompt(self): self.system_prompt = format_prompt_with_tools( self._toolbox, self.system_prompt_template, @@ -220,27 +220,25 @@ class BaseAgent: self.system_prompt = format_prompt_with_imports( self.system_prompt, list(set(LIST_SAFE_MODULES) | set(self.authorized_imports)) ) - self.logs = [TaskStep(system_prompt=self.system_prompt, task=self.task)] - console.rule("[bold]New task", characters='=') - console.print(self.task) + + return self.system_prompt def write_inner_memory_from_logs(self, summary_mode: Optional[bool] = False) -> List[Dict[str, str]]: """ Reads past llm_outputs, actions, and observations or errors from the logs into a series of messages that can be used as input to the LLM. """ - prompt_message = {"role": MessageRole.SYSTEM, "content": self.logs[0].system_prompt} - task_message = { - "role": MessageRole.USER, - "content": "Task: " + self.logs[0].task, - } - if summary_mode: - memory = [task_message] - else: - memory = [prompt_message, task_message] - for i, step_log in enumerate(self.logs[1:]): + memory = [] + for i, step_log in enumerate(self.logs): + if isinstance(step_log, SystemPromptStep): + if not summary_mode: + thought_message = { + "role": MessageRole.SYSTEM, + "content": step_log.system_prompt.strip(), + } + memory.append(thought_message) - if isinstance(step_log, PlanningStep): + elif isinstance(step_log, PlanningStep): thought_message = { "role": MessageRole.ASSISTANT, "content": "[FACTS LIST]:\n" + step_log.facts.strip(), @@ -398,21 +396,21 @@ class ReactAgent(BaseAgent): """ This method provides a final answer to the task, based on the logs of the agent's interactions. """ - self.prompt = [ + self.prompt_messages = [ { "role": MessageRole.SYSTEM, "content": "An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:", } ] - self.prompt += self.write_inner_memory_from_logs()[1:] - self.prompt += [ + self.prompt_messages += self.write_inner_memory_from_logs()[1:] + self.prompt_messages += [ { "role": MessageRole.USER, "content": f"Based on the above, please provide an answer to the following user request:\n{task}", } ] try: - return self.llm_engine(self.prompt) + return self.llm_engine(self.prompt_messages) except Exception as e: error_msg = f"Error in generating final LLM output: {e}." console.print(f"[bold red]{error_msg}[/bold red]") @@ -423,7 +421,10 @@ class ReactAgent(BaseAgent): Runs the agent for the given task. Args: - task (`str`): The task to perform + task (`str`): The task to perform. + stream (`bool`): Wether to run in a streaming way. + reset (`bool`): Wether to reset the conversation or keep it going from previous run. + oneshot (`bool`): Should the agent run in one shot or multi-step fashion? Example: ```py @@ -436,10 +437,23 @@ class ReactAgent(BaseAgent): if len(kwargs) > 0: self.task += f"\nYou have been provided with these initial arguments: {str(kwargs)}." self.state = kwargs.copy() + + self.initialize_system_prompt() + system_prompt_step = SystemPromptStep(system_prompt=self.system_prompt) + if reset: - self.initialize_for_run() + self.token_count = 0 + self.logs = [] + self.logs.append(system_prompt_step) else: - self.logs.append(TaskStep(task=task)) + if len(self.logs) > 0: + self.logs[0] = system_prompt_step + else: + self.logs.append(system_prompt_step) + + console.rule("[bold]New task", characters='=') + console.print(self.task) + self.logs.append(TaskStep(task=task)) if oneshot: step_start_time = time.time() @@ -676,20 +690,20 @@ class JsonAgent(ReactAgent): """ agent_memory = self.write_inner_memory_from_logs() - self.prompt = agent_memory + self.prompt_messages = agent_memory # Add new step in logs log_entry.agent_memory = agent_memory.copy() if self.verbose: console.rule("[italic]Calling LLM engine with this last message:", align="left") - console.print(self.prompt[-1]) + console.print(self.prompt_messages[-1]) console.rule() try: additional_args = {"grammar": self.grammar} if self.grammar is not None else {} llm_output = self.llm_engine( - self.prompt, stop_sequences=["", "Observation:"], **additional_args + self.prompt_messages, stop_sequences=["", "Observation:"], **additional_args ) log_entry.llm_output = llm_output except Exception as e: @@ -796,20 +810,20 @@ class CodeAgent(ReactAgent): """ agent_memory = self.write_inner_memory_from_logs() - self.prompt = agent_memory.copy() + self.prompt_messages = agent_memory.copy() # Add new step in logs log_entry.agent_memory = agent_memory.copy() if self.verbose: console.rule("[italic]Calling LLM engine with these last messages:", align="left") - console.print(self.prompt[-2:]) + console.print(self.prompt_messages[-2:]) console.rule() try: additional_args = {"grammar": self.grammar} if self.grammar is not None else {} llm_output = self.llm_engine( - self.prompt, stop_sequences=["", "Observation:"], **additional_args + self.prompt_messages, stop_sequences=["", "Observation:"], **additional_args ) log_entry.llm_output = llm_output except Exception as e: @@ -893,7 +907,7 @@ You have been submitted this task by your manager. Task: {task} --- -You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible so that they have a clear understanding of the answer. +You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer. Your final_answer WILL HAVE to contain these parts: ### 1. Task outcome (short version): diff --git a/agents/default_tools.py b/agents/default_tools.py index d294a3e..d50380a 100644 --- a/agents/default_tools.py +++ b/agents/default_tools.py @@ -14,7 +14,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import importlib.util import json import math from dataclasses import dataclass @@ -25,7 +24,7 @@ from huggingface_hub import hf_hub_download, list_spaces from transformers.utils import is_offline_mode from .python_interpreter import LIST_SAFE_MODULES, evaluate_python_code -from .tools import TOOL_CONFIG_FILE, TOOL_MAPPING, Tool +from .tools import TOOL_CONFIG_FILE, Tool def custom_print(*args): @@ -97,12 +96,6 @@ class PreTool: repo_id: str -HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB = [ - "image-transformation", - "text-to-image", -] - - def get_remote_tools(logger, organization="huggingface-tools"): if is_offline_mode(): logger.info("You are in offline mode, so remote tools are not available.") @@ -128,26 +121,6 @@ def get_remote_tools(logger, organization="huggingface-tools"): return tools -def setup_default_tools(): - default_tools = {} - main_module = importlib.import_module("transformers") - tools_module = main_module.agents - - for task_name, tool_class_name in TOOL_MAPPING.items(): - tool_class = getattr(tools_module, tool_class_name) - tool_instance = tool_class() - default_tools[tool_class.name] = PreTool( - name=tool_instance.name, - inputs=tool_instance.inputs, - output_type=tool_instance.output_type, - task=task_name, - description=tool_instance.description, - repo_id=None, - ) - - return default_tools - - class PythonInterpreterTool(Tool): name = "python_interpreter" description = "This is a tool that evaluates python code. It can be used to perform calculations." diff --git a/agents/document_question_answering.py b/agents/document_question_answering.py deleted file mode 100644 index fd77e07..0000000 --- a/agents/document_question_answering.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -# Copyright 2023 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re - -import numpy as np -import torch - -from transformers import AutoProcessor, VisionEncoderDecoderModel -from transformers.utils import is_vision_available -from .tools import PipelineTool - - -if is_vision_available(): - from PIL import Image - - -class DocumentQuestionAnsweringTool(PipelineTool): - default_checkpoint = "naver-clova-ix/donut-base-finetuned-docvqa" - description = "This is a tool that answers a question about an document (pdf). It returns a string that contains the answer to the question." - name = "document_qa" - pre_processor_class = AutoProcessor - model_class = VisionEncoderDecoderModel - - inputs = { - "document": { - "type": "image", - "description": "The image containing the information. Can be a PIL Image or a string path to the image.", - }, - "question": {"type": "string", "description": "The question in English"}, - } - output_type = "string" - - def __init__(self, *args, **kwargs): - if not is_vision_available(): - raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.") - - super().__init__(*args, **kwargs) - - def encode(self, document: "Image", question: str): - task_prompt = "{user_input}" - prompt = task_prompt.replace("{user_input}", question) - decoder_input_ids = self.pre_processor.tokenizer( - prompt, add_special_tokens=False, return_tensors="pt" - ).input_ids - if isinstance(document, str): - img = Image.open(document).convert("RGB") - img_array = np.array(img).transpose(2, 0, 1) - document = torch.from_numpy(img_array) - pixel_values = self.pre_processor(document, return_tensors="pt").pixel_values - - return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values} - - def forward(self, inputs): - return self.model.generate( - inputs["pixel_values"].to(self.device), - decoder_input_ids=inputs["decoder_input_ids"].to(self.device), - max_length=self.model.decoder.config.max_position_embeddings, - early_stopping=True, - pad_token_id=self.pre_processor.tokenizer.pad_token_id, - eos_token_id=self.pre_processor.tokenizer.eos_token_id, - use_cache=True, - num_beams=1, - bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]], - return_dict_in_generate=True, - ).sequences - - def decode(self, outputs): - sequence = self.pre_processor.batch_decode(outputs)[0] - sequence = sequence.replace(self.pre_processor.tokenizer.eos_token, "") - sequence = sequence.replace(self.pre_processor.tokenizer.pad_token, "") - sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token - sequence = self.pre_processor.token2json(sequence) - - return sequence["answer"] diff --git a/agents/image_question_answering.py b/agents/image_question_answering.py deleted file mode 100644 index 5101684..0000000 --- a/agents/image_question_answering.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -# Copyright 2023 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -from PIL import Image - -from transformers import AutoModelForVisualQuestionAnswering, AutoProcessor -from transformers.utils import requires_backends -from .tools import PipelineTool - - -class ImageQuestionAnsweringTool(PipelineTool): - default_checkpoint = "dandelin/vilt-b32-finetuned-vqa" - description = ( - "This is a tool that answers a question about an image. It " - "returns a text that is the answer to the question." - ) - name = "image_qa" - pre_processor_class = AutoProcessor - model_class = AutoModelForVisualQuestionAnswering - - inputs = { - "image": { - "type": "image", - "description": "The image containing the information. Can be a PIL Image or a string path to the image.", - }, - "question": {"type": "string", "description": "The question in English"}, - } - output_type = "string" - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - super().__init__(*args, **kwargs) - - def encode(self, image: "Image", question: str): - return self.pre_processor(image, question, return_tensors="pt") - - def forward(self, inputs): - with torch.no_grad(): - return self.model(**inputs).logits - - def decode(self, outputs): - idx = outputs.argmax(-1).item() - return self.model.config.id2label[idx] diff --git a/agents/monitoring.py b/agents/monitoring.py index 9e579e8..72fc1b6 100644 --- a/agents/monitoring.py +++ b/agents/monitoring.py @@ -17,20 +17,8 @@ from .agent_types import AgentAudio, AgentImage, AgentText from .utils import console - def pull_message(step_log: dict, test_mode: bool = True): - try: - from gradio import ChatMessage - except ImportError: - if test_mode: - - class ChatMessage: - def __init__(self, role, content, metadata=None): - self.role = role - self.content = content - self.metadata = metadata - else: - raise ImportError("Gradio should be installed in order to launch a gradio demo.") + from gradio import ChatMessage if step_log.get("rationale"): yield ChatMessage(role="assistant", content=step_log["rationale"]) @@ -54,23 +42,11 @@ def pull_message(step_log: dict, test_mode: bool = True): ) -def stream_to_gradio(agent, task: str, test_mode: bool = False, **kwargs): +def stream_to_gradio(agent, task: str, test_mode: bool = False, reset_agent_memory: bool=False, **kwargs): """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages.""" + from gradio import ChatMessage - try: - from gradio import ChatMessage - except ImportError: - if test_mode: - - class ChatMessage: - def __init__(self, role, content, metadata=None): - self.role = role - self.content = content - self.metadata = metadata - else: - raise ImportError("Gradio should be installed in order to launch a gradio demo.") - - for step_log in agent.run(task, stream=True, **kwargs): + for step_log in agent.run(task, stream=True, reset=reset_agent_memory, **kwargs): if isinstance(step_log, dict): for message in pull_message(step_log, test_mode=test_mode): yield message diff --git a/agents/prompts.py b/agents/prompts.py index 03a6e87..c6ea7ba 100644 --- a/agents/prompts.py +++ b/agents/prompts.py @@ -395,7 +395,7 @@ Do not add anything else.""" SYSTEM_PROMPT_PLAN = """You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools. Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts. -This plan should involve individual tasks based on the avilable tools, that if executed correctly will yield the correct answer. +This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer. Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS. After writing the final step of the plan, write the '\n' tag and stop there.""" @@ -466,7 +466,7 @@ Here is the up to date list of facts that you know: ``` Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts. -This plan should involve individual tasks based on the avilable tools, that if executed correctly will yield the correct answer. +This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer. Beware that you have {remaining_steps} steps remaining. Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS. After writing the final step of the plan, write the '\n' tag and stop there. diff --git a/agents/speech_to_text.py b/agents/speech_to_text.py deleted file mode 100644 index bfa8aeb..0000000 --- a/agents/speech_to_text.py +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from transformers import WhisperForConditionalGeneration, WhisperProcessor -from .tools import PipelineTool - - -class SpeechToTextTool(PipelineTool): - default_checkpoint = "distil-whisper/distil-large-v3" - description = "This is a tool that transcribes an audio into text. It returns the transcribed text." - name = "transcriber" - pre_processor_class = WhisperProcessor - model_class = WhisperForConditionalGeneration - - inputs = {"audio": {"type": "audio", "description": "The audio to transcribe"}} - output_type = "string" - - def encode(self, audio): - return self.pre_processor(audio, return_tensors="pt") - - def forward(self, inputs): - return self.model.generate(inputs["input_features"]) - - def decode(self, outputs): - return self.pre_processor.batch_decode(outputs, skip_special_tokens=True)[0] diff --git a/agents/text_to_speech.py b/agents/text_to_speech.py deleted file mode 100644 index 415f5df..0000000 --- a/agents/text_to_speech.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch - -from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor -from transformers.utils import is_datasets_available -from .tools import PipelineTool - - -if is_datasets_available(): - from datasets import load_dataset - - -class TextToSpeechTool(PipelineTool): - default_checkpoint = "microsoft/speecht5_tts" - description = ( - "This is a tool that reads an English text out loud. It returns a waveform object containing the sound." - ) - name = "text_to_speech" - pre_processor_class = SpeechT5Processor - model_class = SpeechT5ForTextToSpeech - post_processor_class = SpeechT5HifiGan - - inputs = {"text": {"type": "string", "description": "The text to read out loud (in English)"}} - output_type = "audio" - - def setup(self): - if self.post_processor is None: - self.post_processor = "microsoft/speecht5_hifigan" - super().setup() - - def encode(self, text, speaker_embeddings=None): - inputs = self.pre_processor(text=text, return_tensors="pt", truncation=True) - - if speaker_embeddings is None: - if not is_datasets_available(): - raise ImportError("Datasets needs to be installed if not passing speaker embeddings.") - - embeddings_dataset = load_dataset( - "Matthijs/cmu-arctic-xvectors", split="validation", trust_remote_code=True - ) - speaker_embeddings = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0) - - return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} - - def forward(self, inputs): - with torch.no_grad(): - return self.model.generate_speech(**inputs) - - def decode(self, outputs): - with torch.no_grad(): - return self.post_processor(outputs).cpu().detach() diff --git a/agents/tools.py b/agents/tools.py index bacb2b7..2c962b9 100644 --- a/agents/tools.py +++ b/agents/tools.py @@ -80,6 +80,19 @@ def get_repo_type(repo_id, repo_type=None, **hub_kwargs): return "space" +def setup_default_tools(): + default_tools = {} + main_module = importlib.import_module("transformers") + tools_module = main_module.agents + + for task_name, tool_class_name in TOOL_MAPPING.items(): + tool_class = getattr(tools_module, tool_class_name) + tool_instance = tool_class() + default_tools[tool_class.name] = tool_instance + + return default_tools + + # docstyle-ignore APP_FILE_TEMPLATE = """from transformers import launch_gradio_demo from {module_name} import {class_name} @@ -811,11 +824,6 @@ def launch_gradio_demo(tool_class: Tool): TOOL_MAPPING = { - "document_question_answering": "DocumentQuestionAnsweringTool", - "image_question_answering": "ImageQuestionAnsweringTool", - "speech_to_text": "SpeechToTextTool", - "text_to_speech": "TextToSpeechTool", - "translation": "TranslationTool", "python_interpreter": "PythonInterpreterTool", "web_search": "DuckDuckGoSearchTool", } @@ -1018,18 +1026,14 @@ class Toolbox: self._tools = {tool.name: tool for tool in tools} if add_base_tools: self.add_base_tools() - # self._load_tools_if_needed() def add_base_tools(self, add_python_interpreter: bool = False): - global _tools_are_initialized global HUGGINGFACE_DEFAULT_TOOLS - if not _tools_are_initialized: + if len(HUGGINGFACE_DEFAULT_TOOLS.keys()) == 0: HUGGINGFACE_DEFAULT_TOOLS = setup_default_tools() - _tools_are_initialized = True for tool in HUGGINGFACE_DEFAULT_TOOLS.values(): if tool.name != "python_interpreter" or add_python_interpreter: self.add_tool(tool) - # self._load_tools_if_needed() @property def tools(self) -> Dict[str, Tool]: diff --git a/examples/agent_with_tools.py b/examples/agent_with_tools.py index dbb7a24..8b179c9 100644 --- a/examples/agent_with_tools.py +++ b/examples/agent_with_tools.py @@ -1,4 +1,4 @@ -from agents import load_tool, ReactCodeAgent, HfApiEngine +from agents import load_tool, CodeAgent, HfApiEngine # Import tool from Hub image_generation_tool = load_tool("m-ric/text-to-image", cache=False) @@ -10,7 +10,7 @@ search_tool = DuckDuckGoSearchTool() llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct") # Initialize the agent with both tools -agent = ReactCodeAgent(tools=[image_generation_tool, search_tool], llm_engine=llm_engine) +agent = CodeAgent(tools=[image_generation_tool, search_tool], llm_engine=llm_engine) # Run it! result = agent.run( diff --git a/examples/gradio_example.py b/examples/gradio_example.py new file mode 100644 index 0000000..757de81 --- /dev/null +++ b/examples/gradio_example.py @@ -0,0 +1,29 @@ +from agents import stream_to_gradio, HfApiEngine, load_tool, CodeAgent + +image_generation_tool = load_tool("m-ric/text-to-image") + +llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct") + +agent = CodeAgent(tools=[image_generation_tool], llm_engine=llm_engine) + +import gradio as gr + + +def interact_with_agent(prompt, messages): + messages.append(gr.ChatMessage(role="user", content=prompt)) + yield messages + for msg in stream_to_gradio(agent, task=prompt, reset_agent_memory=False): + messages.append(msg) + yield messages + yield messages + + +with gr.Blocks() as demo: + stored_message = gr.State([]) + chatbot = gr.Chatbot(label="Agent", + type="messages", + avatar_images=(None, "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png")) + text_input = gr.Textbox(lines=1, label="Chat Message") + text_input.submit(lambda s: (s, ""), [text_input], [stored_message, text_input]).then(interact_with_agent, [stored_message, chatbot], [chatbot]) + +demo.launch() \ No newline 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