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Add guides for msg format and llm agents (#8750)
* Add guides * add changeset * Add code * Add code * Add notebook * rename msg_format to type * Fix docs * notebooks * missing link * Update guides --------- Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com> Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
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"gradio": patch | ||
--- | ||
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feat:Add guides for msg format and llm agents |
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git+https://github.com/huggingface/transformers.git#egg=transformers[agents] |
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{"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\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", "\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", "\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", "\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", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |
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import gradio as gr | ||
from gradio import ChatMessage | ||
from transformers import load_tool, ReactCodeAgent, HfEngine | ||
from utils import stream_from_transformers_agent | ||
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# Import tool from Hub | ||
image_generation_tool = load_tool("m-ric/text-to-image") | ||
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llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct") | ||
# Initialize the agent with both tools | ||
agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine) | ||
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def interact_with_agent(prompt, messages): | ||
messages.append(ChatMessage(role="user", content=prompt)) | ||
yield messages | ||
for msg in stream_from_transformers_agent(agent, prompt): | ||
messages.append(msg) | ||
yield messages | ||
yield messages | ||
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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]) | ||
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if __name__ == "__main__": | ||
demo.launch() |
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from gradio import ChatMessage | ||
from transformers.agents import ReactCodeAgent, agent_types | ||
from typing import Generator | ||
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def pull_message(step_log: dict): | ||
if step_log.get("rationale"): | ||
yield ChatMessage( | ||
role="assistant", content=step_log["rationale"] | ||
) | ||
if step_log.get("tool_call"): | ||
used_code = step_log["tool_call"]["tool_name"] == "code interpreter" | ||
content = step_log["tool_call"]["tool_arguments"] | ||
if used_code: | ||
content = f"```py\n{content}\n```" | ||
yield ChatMessage( | ||
role="assistant", | ||
metadata={"title": f"🛠️ Used tool {step_log['tool_call']['tool_name']}"}, | ||
content=content, | ||
) | ||
if step_log.get("observation"): | ||
yield ChatMessage( | ||
role="assistant", content=f"```\n{step_log['observation']}\n```" | ||
) | ||
if step_log.get("error"): | ||
yield ChatMessage( | ||
role="assistant", | ||
content=str(step_log["error"]), | ||
metadata={"title": "💥 Error"}, | ||
) | ||
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def stream_from_transformers_agent( | ||
agent: ReactCodeAgent, prompt: str | ||
) -> Generator[ChatMessage, None, ChatMessage | None]: | ||
"""Runs an agent with the given prompt and streams the messages from the agent as ChatMessages.""" | ||
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class Output: | ||
output: agent_types.AgentType | str = None | ||
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for step_log in agent.run(prompt, stream=True): | ||
if isinstance(step_log, dict): | ||
for message in pull_message(step_log): | ||
print("message", message) | ||
yield message | ||
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Output.output = step_log | ||
if isinstance(Output.output, agent_types.AgentText): | ||
yield ChatMessage( | ||
role="assistant", content=f"**Final answer:**\n```\n{Output.output.to_string()}\n```") | ||
elif isinstance(Output.output, agent_types.AgentImage): | ||
yield ChatMessage( | ||
role="assistant", | ||
content={"path": Output.output.to_string(), "mime_type": "image/png"}, | ||
) | ||
elif isinstance(Output.output, agent_types.AgentAudio): | ||
yield ChatMessage( | ||
role="assistant", | ||
content={"path": Output.output.to_string(), "mime_type": "audio/wav"}, | ||
) | ||
else: | ||
return ChatMessage(role="assistant", content=Output.output) |
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", " return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", " return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks(fill_width=True) as demo:\n", " gr.Markdown(\n", " \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", " )\n", " gr.DuplicateButton()\n", " disease = gr.CheckboxGroup(\n", " info=\"Select the diseases you want to scan for.\",\n", " choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", " )\n", " slider = gr.Slider(0, 100)\n", "\n", " with gr.Tab(\"X-ray\") as x_tab:\n", " with gr.Row():\n", " xray_scan = gr.Image()\n", " xray_results = gr.JSON()\n", " xray_run = gr.Button(\"Run\")\n", " xray_run.click(\n", " xray_model,\n", " inputs=[disease, xray_scan],\n", " outputs=xray_results,\n", " api_name=\"xray_model\"\n", " )\n", "\n", " with gr.Tab(\"CT Scan\"):\n", " with gr.Row():\n", " ct_scan = gr.Image()\n", " ct_results = gr.JSON()\n", " ct_run = gr.Button(\"Run\")\n", " ct_run.click(\n", " ct_model,\n", " inputs=[disease, ct_scan],\n", " outputs=ct_results,\n", " api_name=\"ct_model\"\n", " )\n", "\n", " upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", " upload_btn.click(\n", " lambda ct, xr: None,\n", " inputs=[ct_results, xray_results],\n", " outputs=[],\n", " )\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: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", " return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", " return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", " )\n", " gr.DuplicateButton()\n", " disease = gr.CheckboxGroup(\n", " info=\"Select the diseases you want to scan for.\",\n", " choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", " )\n", " slider = gr.Slider(0, 100)\n", "\n", " with gr.Tab(\"X-ray\") as x_tab:\n", " with gr.Row():\n", " xray_scan = gr.Image()\n", " xray_results = gr.JSON()\n", " xray_run = gr.Button(\"Run\")\n", " xray_run.click(\n", " xray_model,\n", " inputs=[disease, xray_scan],\n", " outputs=xray_results,\n", " api_name=\"xray_model\"\n", " )\n", "\n", " with gr.Tab(\"CT Scan\"):\n", " with gr.Row():\n", " ct_scan = gr.Image()\n", " ct_results = gr.JSON()\n", " ct_run = gr.Button(\"Run\")\n", " ct_run.click(\n", " ct_model,\n", " inputs=[disease, ct_scan],\n", " outputs=ct_results,\n", " api_name=\"ct_model\"\n", " )\n", "\n", " upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", " upload_btn.click(\n", " lambda ct, xr: None,\n", " inputs=[ct_results, xray_results],\n", " outputs=[],\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_with_tools"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio import ChatMessage\n", "import time\n", "\n", "def generate_response(history):\n", " history.append(ChatMessage(role=\"user\", content=\"What is the weather in San Francisco right now?\"))\n", " yield history\n", " time.sleep(0.25)\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"In order to find the current weather in San Francisco, I will need to use my weather tool.\")\n", " )\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"API Error when connecting to weather service.\",\n", " metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"})\n", " )\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"I will try again\",\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"Weather 72 degrees Fahrenheit with 20% chance of rain.\",\n", " metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"}\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"Now that the API succeeded I can complete my task.\",\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"It's a sunny day in San Francisco with a current temperature of 72 degrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\",\n", " ))\n", " yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(msg_format=\"messages\")\n", " button = gr.Button(\"Get San Francisco Weather\")\n", " button.click(generate_response, 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: chatbot_with_tools"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio import ChatMessage\n", "import time\n", "\n", "def generate_response(history):\n", " history.append(ChatMessage(role=\"user\", content=\"What is the weather in San Francisco right now?\"))\n", " yield history\n", " time.sleep(0.25)\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"In order to find the current weather in San Francisco, I will need to use my weather tool.\")\n", " )\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"API Error when connecting to weather service.\",\n", " metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"})\n", " )\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"I will try again\",\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"Weather 72 degrees Fahrenheit with 20% chance of rain.\",\n", " metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"}\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"Now that the API succeeded I can complete my task.\",\n", " ))\n", " yield history\n", " time.sleep(0.25)\n", "\n", " history.append(ChatMessage(role=\"assistant\",\n", " content=\"It's a sunny day in San Francisco with a current temperature of 72 degrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\",\n", " ))\n", " yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(type=\"messages\")\n", " button = gr.Button(\"Get San Francisco Weather\")\n", " button.click(generate_response, chatbot, chatbot)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |
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