Update Transformers agent demo

demo/agent_chatbot/requirements.txt

@@ -1 +1 @@
-git+https://github.com/huggingface/transformers.git#egg=transformers[agents]
+transformers>=4.47.0

demo/agent_chatbot/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: agent_chatbot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers.git#egg=transformers[agents]"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/agent_chatbot/utils.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio import ChatMessage\n", "from transformers import load_tool, ReactCodeAgent, HfEngine  # type: ignore\n", "from utils import stream_from_transformers_agent\n", "\n", "# Import tool from Hub\n", "image_generation_tool = load_tool(\"m-ric/text-to-image\")\n", "\n", "llm_engine = HfEngine(\"meta-llama/Meta-Llama-3-70B-Instruct\")\n", "# Initialize the agent with both tools\n", "agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)\n", "\n", "def interact_with_agent(prompt, messages):\n", "    messages.append(ChatMessage(role=\"user\", content=prompt))\n", "    yield messages\n", "    for msg in stream_from_transformers_agent(agent, prompt):\n", "        messages.append(msg)\n", "        yield messages\n", "    yield messages\n", "\n", "with gr.Blocks() as demo:\n", "    stored_message = gr.State([])\n", "    chatbot = gr.Chatbot(label=\"Agent\",\n", "                         type=\"messages\",\n", "                         avatar_images=(None, \"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png\"))\n", "    text_input = gr.Textbox(lines=1, label=\"Chat Message\")\n", "    text_input.submit(lambda s: (s, \"\"), [text_input], [stored_message, text_input]).then(interact_with_agent, [stored_message, chatbot], [chatbot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: agent_chatbot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio transformers>=4.47.0"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio import ChatMessage\n", "from transformers import Tool, ReactCodeAgent  # type: ignore\n", "from transformers.agents import stream_to_gradio, HfApiEngine  # type: ignore\n", "\n", "# Import tool from Hub\n", "image_generation_tool = Tool.from_space(\n", "    space_id=\"black-forest-labs/FLUX.1-schnell\",\n", "    name=\"image_generator\",\n", "    description=\"Generates an image following your prompt. Returns a PIL Image.\",\n", "    api_name=\"/infer\",\n", ")\n", "\n", "llm_engine = HfApiEngine(\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n", "# Initialize the agent with both tools and engine\n", "agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)\n", "\n", "\n", "def interact_with_agent(prompt, messages):\n", "    messages.append(ChatMessage(role=\"user\", content=prompt))\n", "    yield messages\n", "    for msg in stream_to_gradio(agent, prompt):\n", "        messages.append(msg)\n", "        yield messages\n", "    yield messages\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    stored_message = gr.State([])\n", "    chatbot = gr.Chatbot(\n", "        label=\"Agent\",\n", "        type=\"messages\",\n", "        avatar_images=(\n", "            None,\n", "            \"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png\",\n", "        ),\n", "    )\n", "    text_input = gr.Textbox(lines=1, label=\"Chat Message\")\n", "    text_input.submit(\n", "        lambda s: (s, \"\"), [text_input], [stored_message, text_input]\n", "    ).then(interact_with_agent, [stored_message, chatbot], [chatbot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/agent_chatbot/run.py

@@ -1,30 +1,44 @@
 import gradio as gr
 from gradio import ChatMessage
-from transformers import load_tool, ReactCodeAgent, HfEngine  # type: ignore
-from utils import stream_from_transformers_agent
+from transformers import Tool, ReactCodeAgent  # type: ignore
+from transformers.agents import stream_to_gradio, HfApiEngine  # type: ignore
 
 # Import tool from Hub
-image_generation_tool = load_tool("m-ric/text-to-image")
+image_generation_tool = Tool.from_space(
+    space_id="black-forest-labs/FLUX.1-schnell",
+    name="image_generator",
+    description="Generates an image following your prompt. Returns a PIL Image.",
+    api_name="/infer",
+)
 
-llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct")
-# Initialize the agent with both tools
+llm_engine = HfApiEngine("Qwen/Qwen2.5-Coder-32B-Instruct")
+# Initialize the agent with both tools and engine
 agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
 
+
 def interact_with_agent(prompt, messages):
     messages.append(ChatMessage(role="user", content=prompt))
     yield messages
-    for msg in stream_from_transformers_agent(agent, prompt):
+    for msg in stream_to_gradio(agent, prompt):
         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"))
+    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])
+    text_input.submit(
+        lambda s: (s, ""), [text_input], [stored_message, text_input]
+    ).then(interact_with_agent, [stored_message, chatbot], [chatbot])
 
 if __name__ == "__main__":
     demo.launch()

guides/05_chatbots/03_agents-and-tool-usage.md

@@ -35,25 +35,24 @@ We'll start by importing the necessary classes from transformers and gradio.
 ```python
 import gradio as gr
 from gradio import ChatMessage
-from transformers import load_tool, ReactCodeAgent, HfEngine
-from utils import stream_from_transformers_agent
+from transformers import load_tool, ReactCodeAgent # type: ignore
+from transformers.agents import stream_to_gradio, HfApiEngine # type: ignore
 
 # Import tool from Hub
-image_generation_tool = load_tool("m-ric/text-to-image")
+image_generation_tool = load_tool("huggingface-tools/text-to-image")
 
-
-llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct")
-# Initialize the agent with both tools
+llm_engine = HfApiEngine("meta-llama/Meta-Llama-3-70B-Instruct")
+# Initialize the agent with both tools and engine
 agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
 ```
 
-Then we'll build the UI. The bulk of the logic is handled by `stream_from_transformers_agent`. We won't cover it in this guide because it will soon be merged to transformers but you can see its source code [here](https://huggingface.co/spaces/gradio/agent_chatbot/blob/main/utils.py).
+Then we'll build the UI:
 
 ```python
 def interact_with_agent(prompt, messages):
     messages.append(ChatMessage(role="user", content=prompt))
     yield messages
-    for msg in stream_from_transformers_agent(agent, prompt):
+    for msg in stream_to_gradio(agent, prompt):
         messages.append(msg)
         yield messages
     yield messages