#!/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 mimetypes import os import re import shutil from typing import Optional import tempfile # Added for PIL image saving from PIL import Image as PILImage # Added for PIL image handling 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 # Ensure gradio is imported at the top level 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() # More robust cleaning for potentially wrapped in backticks or with newlines model_output = re.sub(r"```\s*[\s\S]*|[\s\S]*\s*```", "```", model_output, flags=re.DOTALL) model_output = re.sub(r"", "", model_output) # Remove standalone tag 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] # Process first tool call for simplicity in this format 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 # Clean up common wrapping issues 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() # Remove "Execution logs:" prefix if present for cleaner display obs_content = re.sub(r"^Execution logs:\s*", "", obs_content).strip() if obs_content: # Only show if there's something after stripping tool_info_md += f"📝 **Tool Output/Logs:**\n```\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: # Standalone error not from a tool call yield {"role": "assistant", "content": f"💥 **Error:** {str(step_log.error)}"} # --- Minimal footnote for type="messages" --- 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: # Check for 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: # Check for 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": "---"} # Separator def stream_to_gradio( agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None, ): """Runs an agent, yields message dicts for Gradio type='messages' Chatbot.""" if not _is_package_available("gradio"): raise ModuleNotFoundError("Install 'gradio': `pip install 'smolagents[gradio]'`") if hasattr(agent, 'interaction_logs'): # Clear logs for this new agent run agent.interaction_logs.clear() print("DEBUG Gradio: Cleared agent interaction_logs for new run.") for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args): 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): # Use new dict-yielding function yield msg_dict final_answer_content = step_log # Last step_log is the final output/state # --- Handle final answer for type="messages" --- if isinstance(final_answer_content, PILImage.Image): print("DEBUG Gradio (stream_to_gradio): Final answer is raw PIL Image.") try: with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file: final_answer_content.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}") # For Gradio type="messages", image content is just the path string yield {"role": "assistant", "content": image_path_for_gradio} return except Exception as e: print(f"DEBUG Gradio: Error saving PIL image from final_answer_content: {e}") yield {"role": "assistant", "content": f"**Final Answer (Error displaying image):** {e}"} return final_answer_processed = handle_agent_output_types(final_answer_content) 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): AgentImage path: {image_path}") if image_path and os.path.exists(image_path): yield {"role": "assistant", "content": image_path} else: err_msg = f"Error: Image path from AgentImage not found or invalid ('{image_path}')" 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} else: err_msg = f"Error: Audio path from AgentAudio not found ('{audio_path}')" 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: """A one-line interface to launch your agent in Gradio""" 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 = file_upload_folder if self.file_upload_folder is not None: if not os.path.exists(self.file_upload_folder): os.makedirs(self.file_upload_folder, exist_ok=True) self._latest_file_path_for_download = None def _check_for_created_file(self): self._latest_file_path_for_download = None 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 files.") for log_entry in reversed(self.agent.interaction_logs): # Check recent logs first if isinstance(log_entry, ActionStep) and hasattr(log_entry, 'tool_calls') and log_entry.tool_calls: for tool_call in log_entry.tool_calls: if tool_call.name == "create_document": tool_output_value = getattr(log_entry, 'observations', None) print(f"DEBUG Gradio UI: Log for 'create_document' call, observed output: {tool_output_value}") if tool_output_value and isinstance(tool_output_value, str): # Try to extract path if it's wrapped, e.g. by "Execution logs:" cleaned_output = re.sub(r"^Execution logs:\s*", "", tool_output_value).strip() path_match = re.search(r"(/tmp/[a-zA-Z0-9_]+/generated_document\.(?:docx|pdf|txt))", cleaned_output) extracted_path = path_match.group(1) if path_match else cleaned_output if not extracted_path.lower().startswith("error:"): normalized_path = os.path.normpath(extracted_path) if os.path.exists(normalized_path): self._latest_file_path_for_download = normalized_path print(f"DEBUG Gradio UI: File path for download set: {self._latest_file_path_for_download}") return True else: print(f"DEBUG Gradio UI: Path from 'create_document' log ('{normalized_path}') does not exist.") else: print(f"DEBUG Gradio UI: 'create_document' tool reported error in observations: {extracted_path}") print("DEBUG Gradio UI: No valid 'create_document' output found for download.") return False def interact_with_agent(self, prompt_text: str, current_chat_tuples: list): # current_chat_tuples is the history from the chatbot (list of lists/tuples) # Convert to 'messages' format if needed, or adapt stream_to_gradio if chatbot is not type="messages" # For type="messages", current_chat_tuples is already list of dicts. print(f"DEBUG Gradio: interact_with_agent called with prompt: '{prompt_text}'") print(f"DEBUG Gradio: Current chat history (input): {current_chat_tuples}") # Add user's new message to the chat history list current_chat_messages = current_chat_tuples + [{"role": "user", "content": prompt_text}] # Initial yield to show user message immediately and hide download items yield current_chat_messages, gr.update(visible=False), gr.update(value=None, visible=False) # Stream agent messages 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 progressively: current user message + all agent messages so far yield current_chat_messages + agent_responses_for_history, gr.update(visible=False), gr.update(value=None, visible=False) # After streaming all agent messages, check for created file file_found = self._check_for_created_file() # Final state for UI components final_chat_display = current_chat_messages + agent_responses_for_history print(f"DEBUG Gradio: Final chat history for display: {final_chat_display}") yield final_chat_display, gr.update(visible=file_found), gr.update(value=None, visible=False) def upload_file(self, file, file_uploads_log_state): if file is None: # No file selected return gr.update(value="No file uploaded.", visible=True), file_uploads_log_state # Ensure file_upload_folder exists (it should from __init__) if not self.file_upload_folder or not os.path.exists(self.file_upload_folder): os.makedirs(self.file_upload_folder, exist_ok=True) # Defensive check allowed_file_types = [ "application/pdf", "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "text/plain", "image/jpeg", "image/png", # Added image types ] # Gradio File object has 'name' (temp path) and 'orig_name' original_name = file.orig_name if hasattr(file, 'orig_name') else os.path.basename(file.name) # Try to guess mime type from temp file name first, then from original name if needed mime_type, _ = mimetypes.guess_type(file.name) if mime_type is None: # Fallback mime_type, _ = mimetypes.guess_type(original_name) if mime_type not in allowed_file_types: return gr.update(value=f"File type '{mime_type or 'unknown'}' for '{original_name}' is disallowed.", visible=True), file_uploads_log_state sanitized_name = re.sub(r"[^\w\-.]", "_", original_name) base_name, current_ext = os.path.splitext(sanitized_name) type_to_ext_map = {v: k for k, v_list in mimetypes. প্রেফারেন্সেস.items() for v in v_list} # More robust ext map type_to_ext_map.update({ # Manual overrides / common types "application/pdf": ".pdf", "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx", "text/plain": ".txt", "image/jpeg": ".jpg", "image/png": ".png" }) expected_ext = type_to_ext_map.get(mime_type) if expected_ext and current_ext.lower() != expected_ext.lower(): sanitized_name = base_name + expected_ext destination_path = os.path.join(self.file_upload_folder, sanitized_name) try: shutil.copy(file.name, destination_path) # file.name is the temp path from Gradio print(f"DEBUG Gradio: File '{original_name}' copied to '{destination_path}'") updated_log = file_uploads_log_state + [destination_path] return gr.update(value=f"Uploaded: {original_name} (as {sanitized_name})", visible=True), updated_log except Exception as e: print(f"DEBUG Gradio: Error copying uploaded file: {e}") return gr.update(value=f"Error uploading {original_name}: {e}", visible=True), file_uploads_log_state def log_user_message(self, text_input_value: str, current_file_uploads: list): full_prompt = text_input_value if current_file_uploads: files_str = ", ".join([os.path.basename(f) for f in current_file_uploads]) full_prompt += f"\n\n[Uploaded files for context: {files_str}]" print(f"DEBUG Gradio: Prepared prompt for agent: {full_prompt}") return full_prompt, "" # Clears the text input box def prepare_and_show_download_file(self): if self._latest_file_path_for_download and os.path.exists(self._latest_file_path_for_download): print(f"DEBUG Gradio UI: Preparing download for UI component: {self._latest_file_path_for_download}") return gr.File.update(value=self._latest_file_path_for_download, label=os.path.basename(self._latest_file_path_for_download), visible=True) else: print("DEBUG Gradio UI: No valid file path to prepare for download component.") gr.Warning("No file available for download or path is invalid.") return gr.File.update(visible=False) def launch(self, **kwargs): with gr.Blocks(fill_height=True, theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue)) as demo: file_uploads_log_state = gr.State([]) prepared_prompt_for_agent = gr.State("") gr.Markdown("# agente inteligente") with gr.Row(): with gr.Column(scale=3): chatbot_display = gr.Chatbot( label="Agent Interaction", type="messages", avatar_images=(None, "https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-round.png"), height=600, show_copy_button=True, bubble_full_width=False ) text_message_input = gr.Textbox( lines=1, label="Your Message to the Agent", placeholder="Type your message and press Enter, or Shift+Enter for new line..." ) with gr.Column(scale=1): if self.file_upload_folder is not None: gr.Markdown("### File Upload") file_uploader = gr.File(label="Upload a supporting file (PDF, DOCX, TXT, JPG, PNG)") upload_status_text = gr.Textbox(label="Upload Status", interactive=False, lines=2, max_lines=4) file_uploader.upload( self.upload_file, [file_uploader, file_uploads_log_state], [upload_status_text, file_uploads_log_state], ) gr.Markdown("### Generated File") download_action_button = gr.Button("Download Generated File", visible=False) file_download_display_component = gr.File(label="Downloadable Document", visible=False, interactive=False) # Event Handling Chain for Text Submission text_message_input.submit( self.log_user_message, # Step 1: Prepare prompt, clear input [text_message_input, file_uploads_log_state], [prepared_prompt_for_agent, text_message_input] ).then( self.interact_with_agent, # Step 2: Run agent, stream to chatbot, update download button [prepared_prompt_for_agent, chatbot_display], [chatbot_display, download_action_button, file_download_display_component] ) download_action_button.click( self.prepare_and_show_download_file, [], [file_download_display_component] ) demo.launch(debug=True, share=kwargs.get("share", False), **kwargs) __all__ = ["stream_to_gradio", "GradioUI"]