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| from __future__ import annotations | |
| import gradio as gr | |
| from gradio import ChatMessage | |
| from transformers.agents import ReactCodeAgent, agent_types | |
| from typing import Generator | |
| from termcolor import colored | |
| from pygments import highlight | |
| from pygments.lexers import PythonLexer | |
| from pygments.formatters import HtmlFormatter | |
| from pygments.formatters import TerminalFormatter | |
| def highlight_code_terminal(text): | |
| return highlight(text, PythonLexer(), TerminalFormatter()) | |
| def highlight_code_html(code): | |
| return highlight(code, PythonLexer(), HtmlFormatter()) | |
| def pull_message(step_log: dict): | |
| if step_log.get("rationale"): | |
| yield "π§ Thinking...", str(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"] | |
| yield f"π οΈ Using tool {step_log['tool_call']['tool_name']}...", content | |
| if step_log.get("observation"): | |
| yield "π Observing...", step_log['observation'] | |
| if step_log.get("error"): | |
| yield "π₯ Coping with an Error...", step_log['error'].message | |
| def stream_from_transformers_agent( | |
| agent: ReactCodeAgent, prompt: str | |
| ) -> Generator[ChatMessage, None, ChatMessage | None]: | |
| """Runs an agent with the given prompt and streams the messages from the agent as ChatMessages.""" | |
| class Output: | |
| output: agent_types.AgentType | str = None | |
| inner_monologue = ChatMessage( | |
| role="assistant", | |
| metadata={"title": "π§ Thinking..."}, | |
| content="", | |
| ) | |
| step_log = None | |
| for step_log in agent.run(prompt, stream=True, reset=len(agent.logs) == 0): # Reset=False misbehaves if the agent has not yet been run | |
| if isinstance(step_log, dict): | |
| for title, message in pull_message(step_log): | |
| terminal_message = message | |
| if ("Using tool" in title) or ("Error" in title): | |
| terminal_message = highlight_code_terminal(message) | |
| message = highlight_code_html(message) | |
| if "Observing" in title: | |
| message = "<div style='border:1px solid black; background-color: var(--code-background-fill); padding: 10px;'>{}</div>".format(message.replace('\n', '<br/>')) | |
| print(colored("=== Inner Monologue Message:\n", "blue", attrs=["bold"]), f"{title}\n{terminal_message}") | |
| inner_monologue.content += f"<h2>{title}</h2><p>{message}</p>" | |
| yield title | |
| if inner_monologue is not None: | |
| inner_monologue.metadata = {"title": "Inner Monologue (click to expand)"} | |
| yield inner_monologue | |
| Output.output = step_log | |
| if isinstance(Output.output, agent_types.AgentText): | |
| yield ChatMessage( | |
| role="assistant", content=f"{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"}, # type: ignore | |
| ) | |
| elif isinstance(Output.output, agent_types.AgentAudio): | |
| yield ChatMessage( | |
| role="assistant", | |
| content={"path": Output.output.to_string(), "mime_type": "audio/wav"}, # type: ignore | |
| ) | |
| else: | |
| return ChatMessage(role="assistant", content=Output.output) | |