#!/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=str(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=str(step_log))