Agents: turn any Space into a Tool with Tool.from_space() (#34561)
* Agents: you can now load a Space as a tool
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@@ -123,6 +123,54 @@ from transformers import load_tool, CodeAgent
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model_download_tool = load_tool("m-ric/hf-model-downloads")
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```
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### Import a Space as a tool 🚀
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You can directly import a Space from the Hub as a tool using the [`Tool.from_space`] method!
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You only need to provide the id of the Space on the Hub, its name, and a description that will help you agent understand what the tool does. Under the hood, this will use [`gradio-client`](https://pypi.org/project/gradio-client/) library to call the Space.
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For instance, let's import the [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) Space from the Hub and use it to generate an image.
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```
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from transformers import Tool
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image_generation_tool = Tool.from_space(
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"black-forest-labs/FLUX.1-dev",
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name="image_generator",
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description="Generate an image from a prompt")
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image_generation_tool("A sunny beach")
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```
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And voilà, here's your image! 🏖️
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/sunny_beach.webp">
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Then you can use this tool just like any other tool. For example, let's improve the prompt `a rabbit wearing a space suit` and generate an image of it.
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```python
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from transformers import ReactCodeAgent
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agent = ReactCodeAgent(tools=[image_generation_tool])
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agent.run(
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"Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
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)
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```
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```text
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=== Agent thoughts:
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improved_prompt could be "A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background"
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Now that I have improved the prompt, I can use the image generator tool to generate an image based on this prompt.
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>>> Agent is executing the code below:
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image = image_generator(prompt="A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background")
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final_answer(image)
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```
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit_spacesuit_flux.webp">
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How cool is this? 🤩
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### Use gradio-tools
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[gradio-tools](https://github.com/freddyaboulton/gradio-tools) is a powerful library that allows using Hugging
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@@ -140,36 +188,6 @@ gradio_prompt_generator_tool = StableDiffusionPromptGeneratorTool()
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prompt_generator_tool = Tool.from_gradio(gradio_prompt_generator_tool)
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```
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Now you can use it just like any other tool. For example, let's improve the prompt `a rabbit wearing a space suit`.
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```python
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image_generation_tool = load_tool('huggingface-tools/text-to-image')
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agent = CodeAgent(tools=[prompt_generator_tool, image_generation_tool], llm_engine=llm_engine)
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agent.run(
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"Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
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)
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```
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The model adequately leverages the tool:
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```text
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======== New task ========
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Improve this prompt, then generate an image of it.
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You have been provided with these initial arguments: {'prompt': 'A rabbit wearing a space suit'}.
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==== Agent is executing the code below:
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improved_prompt = StableDiffusionPromptGenerator(query=prompt)
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while improved_prompt == "QUEUE_FULL":
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improved_prompt = StableDiffusionPromptGenerator(query=prompt)
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print(f"The improved prompt is {improved_prompt}.")
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image = image_generator(prompt=improved_prompt)
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====
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```
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Before finally generating the image:
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png">
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> [!WARNING]
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> gradio-tools require *textual* inputs and outputs even when working with different modalities like image and audio objects. Image and audio inputs and outputs are currently incompatible.
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@@ -179,7 +197,7 @@ We love Langchain and think it has a very compelling suite of tools.
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To import a tool from LangChain, use the `from_langchain()` method.
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Here is how you can use it to recreate the intro's search result using a LangChain web search tool.
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This tool will need `pip install google-search-results` to work properly.
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```python
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from langchain.agents import load_tools
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from transformers import Tool, ReactCodeAgent
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@@ -188,7 +206,7 @@ search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])
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agent = ReactCodeAgent(tools=[search_tool])
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agent.run("How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?")
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agent.run("How many more blocks (also denoted as layers) are in BERT base encoder compared to the encoder from the architecture proposed in Attention is All You Need?")
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```
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## Display your agent run in a cool Gradio interface
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