import re from smolagents.agent_types import AgentAudio, AgentImage, AgentText from smolagents.agents import PlanningStep from smolagents.gradio_ui import get_step_footnote_content from smolagents.memory import ActionStep, FinalAnswerStep, MemoryStep from smolagents.models import ChatMessageStreamDelta from smolagents.utils import _is_package_available def pull_messages_from_step(step_log: MemoryStep, skip_model_outputs: bool = False): """Extract ChatMessage objects from agent steps with proper nesting. Args: step_log: The step log to display as gr.ChatMessage objects. skip_model_outputs: If True, skip the model outputs when creating the gr.ChatMessage objects: This is used for instance when streaming model outputs have already been displayed. """ if not _is_package_available("gradio"): raise ModuleNotFoundError( "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`" ) import gradio as gr if isinstance(step_log, ActionStep): # Output the step number step_number = ( f"Step {step_log.step_number}" if step_log.step_number is not None else "Step" ) if not skip_model_outputs: yield gr.ChatMessage( role="assistant", content=f"**{step_number}**", metadata={"status": "done"}, ) # First yield the thought/reasoning from the LLM if ( not skip_model_outputs and hasattr(step_log, "model_output") and step_log.model_output is not None ): model_output = step_log.model_output.strip() # Remove any trailing and extra backticks, handling multiple possible formats model_output = re.sub( r"```\s*", "```", model_output ) # handles ``` model_output = re.sub( r"\s*```", "```", model_output ) # handles ``` model_output = re.sub( r"```\s*\n\s*", "```", model_output ) # handles ```\n model_output = model_output.strip() yield gr.ChatMessage( role="assistant", content=model_output, metadata={"status": "done"} ) # For tool calls, create a parent message if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None: first_tool_call = step_log.tool_calls[0] used_code = first_tool_call.name == "python_interpreter" # Tool call becomes the parent message with timing info # First we will handle arguments based on type args = first_tool_call.arguments if isinstance(args, dict): content = str(args.get("answer", str(args))) else: content = str(args).strip() if used_code: # Clean up the content by removing any end code tags content = re.sub( r"```.*?\n", "", content ) # Remove existing code blocks content = re.sub( r"\s*\s*", "", content ) # Remove end_code tags content = content.strip() if not content.startswith("```python"): content = f"```python\n{content}\n```" parent_message_tool = gr.ChatMessage( role="assistant", content=content, metadata={ "title": f"🛠️ Used tool {first_tool_call.name}", "status": "done", }, ) yield parent_message_tool # Display execution logs if they exist if hasattr(step_log, "observations") and ( step_log.observations is not None and step_log.observations.strip() ): # Only yield execution logs if there's actual content log_content = step_log.observations.strip() if log_content: log_content = re.sub(r"^Execution logs:\s*", "", log_content) yield gr.ChatMessage( role="assistant", content=f"```bash\n{log_content}\n", metadata={"title": "📝 Execution Logs", "status": "done"}, ) # Display any errors if hasattr(step_log, "error") and step_log.error is not None: yield gr.ChatMessage( role="assistant", content=str(step_log.error), metadata={"title": "💥 Error", "status": "done"}, ) # Update parent message metadata to done status without yielding a new message if getattr(step_log, "observations_images", []): for image in step_log.observations_images: path_image = AgentImage(image).to_string() yield gr.ChatMessage( role="assistant", content={ "path": path_image, "mime_type": f"image/{path_image.split('.')[-1]}", }, metadata={"title": "🖼️ Output Image", "status": "done"}, ) # Handle standalone errors but not from tool calls if hasattr(step_log, "error") and step_log.error is not None: yield gr.ChatMessage( role="assistant", content=str(step_log.error), metadata={"title": "💥 Error", "status": "done"}, ) yield gr.ChatMessage( role="assistant", content=get_step_footnote_content(step_log, step_number), metadata={"status": "done"}, ) yield gr.ChatMessage( role="assistant", content="-----", metadata={"status": "done"} ) elif isinstance(step_log, PlanningStep): yield gr.ChatMessage( role="assistant", content="**Planning step**", metadata={"status": "done"} ) yield gr.ChatMessage( role="assistant", content=step_log.plan, metadata={"status": "done"} ) yield gr.ChatMessage( role="assistant", content=get_step_footnote_content(step_log, "Planning step"), metadata={"status": "done"}, ) yield gr.ChatMessage( role="assistant", content="-----", metadata={"status": "done"} ) elif isinstance(step_log, FinalAnswerStep): final_answer = step_log.final_answer if isinstance(final_answer, AgentText): yield gr.ChatMessage( role="assistant", content=f"**Final answer:**\n{final_answer.to_string()}\n", metadata={"status": "done"}, ) elif isinstance(final_answer, AgentImage): yield gr.ChatMessage( role="assistant", content={"path": final_answer.to_string(), "mime_type": "image/png"}, metadata={"status": "done"}, ) elif isinstance(final_answer, AgentAudio): yield gr.ChatMessage( role="assistant", content={"path": final_answer.to_string(), "mime_type": "audio/wav"}, metadata={"status": "done"}, ) else: yield gr.ChatMessage( role="assistant", content=f"**Final answer:** {str(final_answer)}", metadata={"status": "done"}, ) else: raise ValueError(f"Unsupported step type: {type(step_log)}") def stream_to_gradio( agent, task: str, task_images: list | None = None, reset_agent_memory: bool = False, additional_args: dict | None = None, ): """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages.""" total_input_tokens = 0 total_output_tokens = 0 if not _is_package_available("gradio"): raise ModuleNotFoundError( "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`" ) intermediate_text = "" for step_log in agent.run( task, images=task_images, stream=True, reset=reset_agent_memory, additional_args=additional_args, ): # Track tokens if model provides them if getattr(agent.model, "last_input_token_count", None) is not None: total_input_tokens += agent.model.last_input_token_count total_output_tokens += agent.model.last_output_token_count if isinstance(step_log, (ActionStep, PlanningStep)): step_log.input_token_count = agent.model.last_input_token_count step_log.output_token_count = agent.model.last_output_token_count if isinstance(step_log, MemoryStep): intermediate_text = "" for message in pull_messages_from_step( step_log, # If we're streaming model outputs, no need to display them twice skip_model_outputs=getattr(agent, "stream_outputs", False), ): yield message elif isinstance(step_log, ChatMessageStreamDelta): intermediate_text += step_log.content or "" yield intermediate_text