92 lines
3.9 KiB
Python
92 lines
3.9 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .agent_types import AgentAudio, AgentImage, AgentText
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from .agents import BaseAgent, AgentStep, ActionStep
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import gradio as gr
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def pull_messages_from_step(step_log: AgentStep, test_mode: bool = True):
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"""Extract ChatMessage objects from agent steps"""
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if isinstance(step_log, ActionStep):
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yield gr.ChatMessage(role="assistant", content=step_log.rationale)
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if step_log.tool_call is not None:
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used_code = step_log.tool_call["tool_name"] == "code interpreter"
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content = step_log.tool_call["tool_arguments"]
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if used_code:
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content = f"```py\n{content}\n```"
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yield gr.ChatMessage(
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role="assistant",
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metadata={"title": f"🛠️ Used tool {step_log.tool_call['tool_name']}"},
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content=str(content),
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)
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if step_log.observation is not None:
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yield gr.ChatMessage(role="assistant", content=f"```\n{step_log.observation}\n```")
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if step_log.error is not None:
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yield gr.ChatMessage(
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role="assistant",
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content=str(step_log.error),
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metadata={"title": "💥 Error"},
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)
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def stream_to_gradio(agent, task: str, test_mode: bool = False, reset_agent_memory: bool=False, **kwargs):
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"""Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
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for step_log in agent.run(task, stream=True, reset=reset_agent_memory, **kwargs):
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for message in pull_messages_from_step(step_log, test_mode=test_mode):
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yield message
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final_answer = step_log # Last log is the run's final_answer
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if isinstance(final_answer, AgentText):
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yield gr.ChatMessage(role="assistant", content=f"**Final answer:**\n```\n{final_answer.to_string()}\n```")
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elif isinstance(final_answer, AgentImage):
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yield gr.ChatMessage(
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role="assistant",
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content={"path": final_answer.to_string(), "mime_type": "image/png"},
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)
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elif isinstance(final_answer, AgentAudio):
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yield gr.ChatMessage(
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role="assistant",
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content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
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)
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else:
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yield gr.ChatMessage(role="assistant", content=str(final_answer))
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class GradioUI():
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"""A one-line interface to launch your agent in Gradio"""
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def __init__(self, agent: BaseAgent):
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self.agent = agent
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def interact_with_agent(self, prompt, messages):
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messages.append(gr.ChatMessage(role="user", content=prompt))
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yield messages
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for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
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messages.append(msg)
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yield messages
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yield messages
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def run(self):
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with gr.Blocks() as demo:
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stored_message = gr.State([])
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chatbot = gr.Chatbot(label="Agent",
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type="messages",
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avatar_images=(None, "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png"))
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text_input = gr.Textbox(lines=1, label="Chat Message")
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text_input.submit(lambda s: (s, ""), [text_input], [stored_message, text_input]).then(self.interact_with_agent, [stored_message, chatbot], [chatbot])
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demo.launch() |