#!/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 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 from Code_Functions import speak_text import io import librosa import numpy as np def mp3_bytes_to_numpy(audio_bytes, sr=None): # Load audio from the MP3 bytes; sr=None preserves the original sample rate. audio_np, sr = librosa.load(io.BytesIO(audio_bytes), sr=sr) return audio_np #from jokes import gradio_search_jokes # from app import agent # Импортируем объект агента # def gradio_search_jokes(word): # """Wrapper function that sends the request to the agent and retrieves the joke and its audio.""" # response = self.agent.run(word) # Отправляем запрос агенту # response_text = response.get("final_answer", "No response from agent.") # Получаем текст ответа # # Генерируем аудио # audio_file = speak_text(response_text) # return response_text, audio_file def pull_messages_from_step( step_log: MemoryStep, ): """Extract ChatMessage objects from agent steps with proper nesting""" 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 "" yield gr.ChatMessage(role="assistant", content=f"**{step_number}**") # First yield the thought/reasoning from the LLM if hasattr(step_log, "model_output") and step_log.model_output is not None: # Clean up the LLM output 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) # 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" parent_id = f"call_{len(step_log.tool_calls)}" # 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}", "id": parent_id, "status": "pending", }, ) yield parent_message_tool # Nesting execution logs under the tool call 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"{log_content}", metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"}, ) # Nesting any errors under the tool call 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", "parent_id": parent_id, "status": "done"}, ) # Update parent message metadata to done status without yielding a new message parent_message_tool.metadata["status"] = "done" # Handle standalone errors but not from tool calls elif hasattr(step_log, "error") and step_log.error is not None: yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"}) # Calculate duration and token information step_footnote = f"{step_number}" if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"): token_str = ( f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}" ) step_footnote += token_str if hasattr(step_log, "duration"): step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None step_footnote += step_duration step_footnote = f"""{step_footnote} """ yield gr.ChatMessage(role="assistant", content=f"{step_footnote}") yield gr.ChatMessage(role="assistant", content="-----") def stream_to_gradio( agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None, ): """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages.""" if not _is_package_available("gradio"): raise ModuleNotFoundError( "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`" ) import gradio as gr total_input_tokens = 0 total_output_tokens = 0 for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args): # Track tokens if model provides them if hasattr(agent.model, "last_input_token_count"): total_input_tokens += agent.model.last_input_token_count total_output_tokens += agent.model.last_output_token_count 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 message in pull_messages_from_step( step_log, ): yield message final_answer = step_log # Last log is the run's final_answer final_answer = handle_agent_output_types(final_answer) if isinstance(final_answer, AgentText): yield gr.ChatMessage( role="assistant", content=f"**Final answer:**\n{final_answer.to_string()}\n", ) elif isinstance(final_answer, AgentImage): yield gr.ChatMessage( role="assistant", content={"path": final_answer.to_string(), "mime_type": "image/png"}, ) elif isinstance(final_answer, AgentAudio): yield gr.ChatMessage( role="assistant", content={"path": final_answer.to_string(), "mime_type": "audio/mpeg"}, ) elif isinstance(final_answer, str) and final_answer.strip().startswith("/tmp/"): # Fallback: if the final answer is a string that is a file path, treat it as audio. yield gr.ChatMessage( role="assistant", content={"path": final_answer.strip(), "mime_type": "audio/mpeg"}, ) else: yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}") 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( "Please install 'gradio' extra to use the GradioUI: `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(file_upload_folder): os.mkdir(file_upload_folder) # def gradio_search_jokes(self, word): # Теперь это МЕТОД класса # """Wrapper function that sends the request to the agent and retrieves the joke and its audio.""" # print(f"[DEBUG] Запрос к агенту: {word}") # Отладочный вывод перед вызовом агента # response = self.agent.run(f"Find a dad joke that contains the word: {word}") # Запрашиваем у агента шутку # print("[DEBUG] Агент вернул ответ:", response) # Проверяем, что агент вообще что-то вернул # response_text = str(response) # Приводим объект к строке # Получаем текст ответа # print("[DEBUG] Текст ответа:", response_text) # Проверяем, что текст есть # # Генерируем аудио # audio_file = speak_text(response_text) # print("[DEBUG] Аудиофайл создан:", audio_file) # Проверяем, что аудиофайл сгенерировался # return response_text, audio_file def interact_with_agent(self, prompt, messages): import gradio as gr messages.append(gr.ChatMessage(role="user", content=prompt)) yield messages for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False): messages.append(msg) yield messages yield messages def upload_file( self, file, file_uploads_log, allowed_file_types=[ "application/pdf", "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "text/plain", ], ): """ Handle file uploads, default allowed types are .pdf, .docx, and .txt """ import gradio as gr if file is None: return gr.Textbox("No file uploaded", visible=True), file_uploads_log try: mime_type, _ = mimetypes.guess_type(file.name) except Exception as e: return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log if mime_type not in allowed_file_types: return gr.Textbox("File type disallowed", visible=True), file_uploads_log # Sanitize file name original_name = os.path.basename(file.name) sanitized_name = re.sub( r"[^\w\-.]", "_", original_name ) # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores type_to_ext = {} for ext, t in mimetypes.types_map.items(): if t not in type_to_ext: type_to_ext[t] = ext # Ensure the extension correlates to the mime type sanitized_name = sanitized_name.split(".")[:-1] sanitized_name.append("" + type_to_ext[mime_type]) sanitized_name = "".join(sanitized_name) # Save the uploaded file to the specified folder file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name)) shutil.copy(file.name, file_path) return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path] def log_user_message(self, text_input, file_uploads_log): return ( text_input + ( f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}" if len(file_uploads_log) > 0 else "" ), "", ) def launch(self, **kwargs): import gradio as gr # NEW: Add a helper function to extract audio file path from stored messages def extract_audio(messages): # Iterate backwards over messages until we find one with audio content. if not messages: return None for msg in reversed(messages): # Only process messages that are objects with a "content" attribute. if not hasattr(msg, "content"): continue # If the content is a dict and contains an audio mime type, return the file path. if isinstance(msg.content, dict): mime = msg.content.get("mime_type", "") if mime.startswith("audio"): # If the audio data is already provided under "data", return it. if "data" in msg.content: return msg.content["data"] # Otherwise, if a file path is provided (fallback), load and convert it. elif "path" in msg.content: with open(msg.content["path"], "rb") as f: audio_bytes = f.read() return mp3_bytes_to_numpy(audio_bytes) return None with gr.Blocks(fill_height=True) as demo: stored_messages = gr.State([]) file_uploads_log = gr.State([]) chatbot = gr.Chatbot( label="Agent", type="messages", avatar_images=( None, "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png", ), resizeable=True, scale=1, ) # NEW: Add a dedicated audio player component below the chatbot. audio_player = gr.Audio(label="Audio Pronunciation", type="filepath") if self.file_upload_folder is not None: upload_file = gr.File(label="Upload a file") upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False) upload_file.change( self.upload_file, [upload_file, file_uploads_log], [upload_status, file_uploads_log], ) text_input = gr.Textbox(lines=1, label="Chat Message") # Chain the functions: log user message -> interact with agent -> extract audio for player text_input.submit( self.log_user_message, [text_input, file_uploads_log], [stored_messages, text_input], ).then( self.interact_with_agent, [stored_messages, chatbot], [chatbot] ).then( fn=extract_audio, inputs=[stored_messages], outputs=[audio_player] ) # Optionally, you can arrange the components as you like, for example: # gr.Column([chatbot, audio_player, text_input]) demo.launch(debug=True, share=True, **kwargs) __all__ = ["stream_to_gradio", "GradioUI"]