Add stream messages from agent run for gradio chatbot (#32142)

* Add stream_to_gradio method for running agent in gradio demo

docs/source/en/agents.md

@@ -509,3 +509,54 @@ agent = ReactCodeAgent(tools=[search_tool])
 
 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?")
 ```
+
+## Gradio interface
+
+You can leverage `gradio.Chatbot`to display your agent's thoughts using `stream_to_gradio`, here is an example:
+
+```py
+import gradio as gr
+from transformers import (
+    load_tool,
+    ReactCodeAgent,
+    HfEngine,
+    stream_to_gradio,
+)
+
+# Import tool from Hub
+image_generation_tool = load_tool("m-ric/text-to-image")
+
+llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct")
+
+# Initialize the agent with the image generation tool
+agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
+
+
+def interact_with_agent(task):
+    messages = []
+    messages.append(gr.ChatMessage(role="user", content=task))
+    yield messages
+    for msg in stream_to_gradio(agent, task):
+        messages.append(msg)
+        yield messages + [
+            gr.ChatMessage(role="assistant", content="⏳ Task not finished yet!")
+        ]
+    yield messages
+
+
+with gr.Blocks() as demo:
+    text_input = gr.Textbox(lines=1, label="Chat Message", value="Make me a picture of the Statue of Liberty.")
+    submit = gr.Button("Run illustrator agent!")
+    chatbot = gr.Chatbot(
+        label="Agent",
+        type="messages",
+        avatar_images=(
+            None,
+            "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
+        ),
+    )
+    submit.click(interact_with_agent, [text_input], [chatbot])
+
+if __name__ == "__main__":
+    demo.launch()
+```

docs/source/en/main_classes/agent.md

@@ -72,6 +72,10 @@ We provide two types of agents, based on the main [`Agent`] class:
 
 [[autodoc]] launch_gradio_demo
 
+### stream_to_gradio
+
+[[autodoc]] stream_to_gradio
+
 ### ToolCollection
 
 [[autodoc]] ToolCollection

src/transformers/__init__.py

@@ -67,6 +67,7 @@
         "ToolCollection",
         "launch_gradio_demo",
         "load_tool",
+        "stream_to_gradio",
     ],
     "audio_utils": [],
     "benchmark": [],
@@ -4733,6 +4734,7 @@
         ToolCollection,
         launch_gradio_demo,
         load_tool,
+        stream_to_gradio,
     )
     from .configuration_utils import PretrainedConfig
 

src/transformers/agents/__init__.py

@@ -26,6 +26,7 @@
 _import_structure = {
     "agents": ["Agent", "CodeAgent", "ReactAgent", "ReactCodeAgent", "ReactJsonAgent", "Toolbox"],
     "llm_engine": ["HfEngine"],
+    "monitoring": ["stream_to_gradio"],
     "tools": ["PipelineTool", "Tool", "ToolCollection", "launch_gradio_demo", "load_tool"],
 }
 
@@ -45,6 +46,7 @@
 if TYPE_CHECKING:
     from .agents import Agent, CodeAgent, ReactAgent, ReactCodeAgent, ReactJsonAgent, Toolbox
     from .llm_engine import HfEngine
+    from .monitoring import stream_to_gradio
     from .tools import PipelineTool, Tool, ToolCollection, launch_gradio_demo, load_tool
 
     try:

src/transformers/agents/monitoring.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+# coding=utf-8
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .agent_types import AgentAudio, AgentImage, AgentText
+from .agents import ReactAgent
+
+
+def pull_message(step_log: dict):
+    try:
+        from gradio import ChatMessage
+    except ImportError:
+        raise ImportError("Gradio should be installed in order to launch a gradio demo.")
+
+    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"},
+        )
+
+
+def stream_to_gradio(agent: ReactAgent, task: str, **kwargs):
+    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+
+    try:
+        from gradio import ChatMessage
+    except ImportError:
+        raise ImportError("Gradio should be installed in order to launch a gradio demo.")
+
+    for step_log in agent.run(task, stream=True, **kwargs):
+        if isinstance(step_log, dict):
+            for message in pull_message(step_log):
+                yield message
+
+    if isinstance(step_log, AgentText):
+        yield ChatMessage(role="assistant", content=f"**Final answer:**\n```\n{step_log.to_string()}\n```")
+    elif isinstance(step_log, AgentImage):
+        yield ChatMessage(
+            role="assistant",
+            content={"path": step_log.to_string(), "mime_type": "image/png"},
+        )
+    elif isinstance(step_log, AgentAudio):
+        yield ChatMessage(
+            role="assistant",
+            content={"path": step_log.to_string(), "mime_type": "audio/wav"},
+        )
+    else:
+        yield ChatMessage(role="assistant", content=step_log)