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from __future__ import annotations |
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from gradio import ChatMessage |
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from transformers.agents import ReactCodeAgent, agent_types |
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from typing import Generator |
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def pull_message(step_log: dict): |
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if step_log.get("rationale"): |
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yield ChatMessage( |
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role="assistant", content=step_log["rationale"] |
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) |
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if step_log.get("tool_call"): |
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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 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=content, |
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) |
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if step_log.get("observation"): |
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yield ChatMessage( |
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role="assistant", content=f"```\n{step_log['observation']}\n```" |
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) |
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if step_log.get("error"): |
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yield 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_from_transformers_agent( |
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agent: ReactCodeAgent, prompt: str |
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) -> Generator[ChatMessage, None, ChatMessage | None]: |
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"""Runs an agent with the given prompt and streams the messages from the agent as ChatMessages.""" |
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class Output: |
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output: agent_types.AgentType | str = None |
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step_log = None |
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for step_log in agent.run(prompt, stream=True): |
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if isinstance(step_log, dict): |
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for message in pull_message(step_log): |
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print("message", message) |
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yield message |
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Output.output = step_log |
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if isinstance(Output.output, agent_types.AgentText): |
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yield ChatMessage( |
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role="assistant", content=f"**Final answer:**\n```\n{Output.output.to_string()}\n```") |
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elif isinstance(Output.output, agent_types.AgentImage): |
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yield ChatMessage( |
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role="assistant", |
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content={"path": Output.output.to_string(), "mime_type": "image/png"}, |
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) |
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elif isinstance(Output.output, agent_types.AgentAudio): |
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yield ChatMessage( |
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role="assistant", |
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content={"path": Output.output.to_string(), "mime_type": "audio/wav"}, |
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) |
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else: |
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return ChatMessage(role="assistant", content=Output.output) |
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