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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.38.1
+sdk_version: 4.39.0
 app_file: run.py
 pinned: false
 hf_oauth: true

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\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}
+{"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", "\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}

run.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from gradio import ChatMessage
-from transformers import load_tool, ReactCodeAgent, HfEngine
+from transformers import load_tool, ReactCodeAgent, HfEngine  # type: ignore
 from utils import stream_from_transformers_agent
 
 # Import tool from Hub

utils.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from gradio import ChatMessage
 from transformers.agents import ReactCodeAgent, agent_types
 from typing import Generator
@@ -37,7 +39,8 @@ def stream_from_transformers_agent(
 
     class Output:
         output: agent_types.AgentType | str = None
-
+    
+    step_log = None
     for step_log in agent.run(prompt, stream=True):
         if isinstance(step_log, dict):
             for message in pull_message(step_log):
@@ -48,16 +51,16 @@ def stream_from_transformers_agent(
     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```")
+            role="assistant", content=f"**Final answer:**\n```\n{Output.output.to_string()}\n```")  # type: ignore
     elif isinstance(Output.output, agent_types.AgentImage):
         yield ChatMessage(
             role="assistant",
-            content={"path": Output.output.to_string(), "mime_type": "image/png"},
+            content={"path": Output.output.to_string(), "mime_type": "image/png"},  # type: ignore
         )
     elif isinstance(Output.output, agent_types.AgentAudio):
         yield ChatMessage(
             role="assistant",
-            content={"path": Output.output.to_string(), "mime_type": "audio/wav"},
+            content={"path": Output.output.to_string(), "mime_type": "audio/wav"},  # type: ignore
         )
     else:
         return ChatMessage(role="assistant", content=Output.output)