#!/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. import os import re from typing import Optional import tempfile from PIL import Image as PILImage from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types from smolagents.agents import ActionStep, MultiStepAgent from smolagents.memory import MemoryStep from smolagents.utils import _is_package_available import gradio as gr def pull_messages_from_step_dict(step_log: MemoryStep): """Extract messages as dicts for Gradio type='messages' Chatbot""" if isinstance(step_log, ActionStep): step_number_str = f"Step {step_log.step_number}" if step_log.step_number is not None else "Processing" yield {"role": "assistant", "content": f"**{step_number_str}**"} if hasattr(step_log, "model_output") and step_log.model_output is not None: model_output = step_log.model_output.strip() model_output = re.sub(r"```\s*[\s\S]*|[\s\S]*\s*```", "```", model_output, flags=re.DOTALL) model_output = re.sub(r"", "", model_output) model_output = model_output.strip() yield {"role": "assistant", "content": model_output} if hasattr(step_log, "tool_calls") and step_log.tool_calls: tc = step_log.tool_calls[0] tool_info_md = f"🛠️ **Tool Used: {tc.name}**\n" args = tc.arguments if isinstance(args, dict): args_str = str(args.get("answer", str(args))) else: args_str = str(args).strip() if tc.name == "python_interpreter": code_content = args_str code_content = re.sub(r"^```python\s*\n?", "", code_content) code_content = re.sub(r"\n?```\s*$", "", code_content) code_content = re.sub(r"^\s*\s*", "", code_content) code_content = re.sub(r"\s*\s*$", "", code_content) code_content = code_content.strip() tool_info_md += f"Executing Code:\n```python\n{code_content}\n```\n" else: tool_info_md += f"Arguments: `{args_str}`\n" if hasattr(step_log, "observations") and step_log.observations and step_log.observations.strip(): obs_content = step_log.observations.strip() obs_content = re.sub(r"^Execution logs:\s*", "", obs_content).strip() if obs_content: tool_info_md += f"📝 **Tool Output/Logs:**\n```text\n{obs_content}\n```\n" if hasattr(step_log, "error") and step_log.error: tool_info_md += f"💥 **Error:** {str(step_log.error)}\n" yield {"role": "assistant", "content": tool_info_md.strip()} elif hasattr(step_log, "error") and step_log.error: yield {"role": "assistant", "content": f"💥 **Error:** {str(step_log.error)}"} footnote_parts = [] if step_log.step_number is not None: footnote_parts.append(f"Step {step_log.step_number}") if hasattr(step_log, "duration") and step_log.duration is not None: footnote_parts.append(f"Duration: {round(float(step_log.duration), 2)}s") if hasattr(step_log, "input_token_count") and step_log.input_token_count is not None: footnote_parts.append(f"InTokens: {step_log.input_token_count:,}") if hasattr(step_log, "output_token_count") and step_log.output_token_count is not None: footnote_parts.append(f"OutTokens: {step_log.output_token_count:,}") if footnote_parts: footnote_text = " | ".join(footnote_parts) yield {"role": "assistant", "content": f"""

{footnote_text}

"""} yield {"role": "assistant", "content": "---"} def stream_to_gradio( agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None, ): if not _is_package_available("gradio"): raise ModuleNotFoundError("Install 'gradio': `pip install 'smolagents[gradio]'`") if hasattr(agent, 'interaction_logs'): agent.interaction_logs.clear() print("DEBUG Gradio: Cleared agent interaction_logs for new run.") all_step_logs = [] for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args): all_step_logs.append(step_log) if hasattr(agent.model, "last_input_token_count") and agent.model.last_input_token_count is not None: if isinstance(step_log, ActionStep): step_log.input_token_count = agent.model.last_input_token_count step_log.output_token_count = agent.model.last_output_token_count for msg_dict in pull_messages_from_step_dict(step_log): yield msg_dict if not all_step_logs: yield {"role": "assistant", "content": "Agent did not produce any output."} return final_answer_content = all_step_logs[-1] actual_content_for_handling = final_answer_content if hasattr(final_answer_content, 'final_answer') and not isinstance(final_answer_content, (str, PILImage.Image, tuple)): actual_content_for_handling = final_answer_content.final_answer print(f"DEBUG Gradio: Extracted actual_content_for_handling from FinalAnswerStep: {type(actual_content_for_handling)}") if isinstance(actual_content_for_handling, PILImage.Image): print("DEBUG Gradio (stream_to_gradio): Actual content IS a raw PIL Image.") try: with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file: actual_content_for_handling.save(tmp_file, format="PNG") image_path_for_gradio = tmp_file.name print(f"DEBUG Gradio: Saved PIL image to temp path: {image_path_for_gradio}") yield {"role": "assistant", "content": (image_path_for_gradio, "Generated Image")} return except Exception as e: print(f"DEBUG Gradio: Error saving extracted PIL image: {e}") yield {"role": "assistant", "content": f"**Final Answer (Error displaying image):** {e}"} return final_answer_processed = handle_agent_output_types(actual_content_for_handling) print(f"DEBUG Gradio: final_answer_processed type after handle_agent_output_types: {type(final_answer_processed)}") if isinstance(final_answer_processed, AgentText): yield {"role": "assistant", "content": f"**Final Answer:**\n{final_answer_processed.to_string()}"} elif isinstance(final_answer_processed, AgentImage): image_path = final_answer_processed.to_string() print(f"DEBUG Gradio (stream_to_gradio): final_answer_processed is AgentImage. Path: {image_path}") if image_path and os.path.exists(image_path): yield {"role": "assistant", "content": (image_path, "Generated Image (from AgentImage)")} else: err_msg = f"Error: Image path from AgentImage ('{image_path}') not found or invalid." print(f"DEBUG Gradio: {err_msg}") yield {"role": "assistant", "content": f"**Final Answer ({err_msg})**"} elif isinstance(final_answer_processed, AgentAudio): audio_path = final_answer_processed.to_string() print(f"DEBUG Gradio (stream_to_gradio): AgentAudio path: {audio_path}") if audio_path and os.path.exists(audio_path): yield {"role": "assistant", "content": (audio_path, "Generated Audio")} else: err_msg = f"Error: Audio path from AgentAudio ('{audio_path}') not found" print(f"DEBUG Gradio: {err_msg}") yield {"role": "assistant", "content": f"**Final Answer ({err_msg})**"} else: yield {"role": "assistant", "content": f"**Final Answer:**\n{str(final_answer_processed)}"} class GradioUI: def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None): if not _is_package_available("gradio"): raise ModuleNotFoundError("Install 'gradio': `pip install 'smolagents[gradio]'`") self.agent = agent self.file_upload_folder = None self._latest_file_path_for_download = None def _get_created_document_path(self): if hasattr(self.agent, 'interaction_logs') and self.agent.interaction_logs: print(f"DEBUG Gradio UI: Checking {len(self.agent.interaction_logs)} interaction log entries for created document paths.") for log_entry in reversed(self.agent.interaction_logs): if isinstance(log_entry, ActionStep): observations = getattr(log_entry, 'observations', None) tool_calls = getattr(log_entry, 'tool_calls', []) is_python_interpreter_step = any(tc.name == "python_interpreter" for tc in tool_calls) if is_python_interpreter_step and observations and isinstance(observations, str): # CRITICAL DEBUG LINE: Print the exact observations string print(f"DEBUG Gradio UI (_get_created_document_path): Python Interpreter Observations: '''{observations}'''") match = re.search( r"(?:Document created \((?:docx|pdf|txt)\):|Document converted to PDF:)\s*(/tmp/[a-zA-Z0-9_]+/generated_document\.(?:docx|pdf|txt))", observations, re.MULTILINE ) if match: extracted_path = match.group(1) print(f"DEBUG Gradio UI: Regex matched. Extracted path: '{extracted_path}'") normalized_path = os.path.normpath(extracted_path) if os.path.exists(normalized_path): print(f"DEBUG Gradio UI: Validated path for download: {normalized_path}") return normalized_path else: print(f"DEBUG Gradio UI: Path from create_document output ('{normalized_path}') does not exist.") print("DEBUG Gradio UI: No valid generated document path found in agent logs.") return None def interact_with_agent(self, prompt_text: str, current_chat_history: list): print(f"DEBUG Gradio: interact_with_agent called with prompt: '{prompt_text}'") updated_chat_history = current_chat_history + [{"role": "user", "content": prompt_text}] yield updated_chat_history, gr.update(value=None, visible=False) # For file_download_display_component agent_responses_for_history = [] for msg_dict in stream_to_gradio(self.agent, task=prompt_text, reset_agent_memory=False): agent_responses_for_history.append(msg_dict) yield updated_chat_history + agent_responses_for_history, gr.update(value=None, visible=False) # For file_download_display_component final_chat_display_content = updated_chat_history + agent_responses_for_history document_path_to_display = self._get_created_document_path() if document_path_to_display: print(f"DEBUG Gradio: Document found for display: {document_path_to_display}") # CORRECTED: Use gr.update() for the File component yield final_chat_display_content, gr.update(value=document_path_to_display, label=os.path.basename(document_path_to_display), visible=True) else: print(f"DEBUG Gradio: No document found for display.") # CORRECTED: Use gr.update() for the File component yield final_chat_display_content, gr.update(value=None, visible=False) def log_user_message(self, text_input_value: str): full_prompt = text_input_value print(f"DEBUG Gradio: Prepared prompt for agent: {full_prompt[:300]}...") return full_prompt, "" # prepare_and_show_download_file is not needed if we directly update the gr.File component def launch(self, **kwargs): with gr.Blocks(fill_height=True, theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue)) as demo: prepared_prompt_for_agent = gr.State("") gr.Markdown("## Smol Talk with your Agent") with gr.Row(equal_height=False): with gr.Column(scale=3): chatbot_display = gr.Chatbot( type="messages", avatar_images=(None, "https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-round.png"), height=700, show_copy_button=True, bubble_full_width=False, show_label=False ) text_message_input = gr.Textbox( lines=1, placeholder="Type your message and press Enter, or Shift+Enter for new line...", show_label=False ) with gr.Column(scale=1): # "Generated File" section directly shows the gr.File component gr.Markdown("### Generated Document") file_download_display_component = gr.File( label="Downloadable Document", visible=False, interactive=False ) text_message_input.submit( self.log_user_message, [text_message_input], [prepared_prompt_for_agent, text_message_input] ).then( self.interact_with_agent, [prepared_prompt_for_agent, chatbot_display], [chatbot_display, file_download_display_component] # Outputs update chatbot and file component ) demo.launch(debug=True, share=kwargs.get("share", False), **kwargs) __all__ = ["stream_to_gradio", "GradioUI"]