{"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}