Rustam39/First_agent_template
0
1#!/usr/bin/env python2# coding=utf-83# Copyright 2024 The HuggingFace Inc. team. All rights reserved.4#5# Licensed under the Apache License, Version 2.0 (the "License");6# you may not use this file except in compliance with the License.7# You may obtain a copy of the License at8#9# http://www.apache.org/licenses/LICENSE-2.010#11# Unless required by applicable law or agreed to in writing, software12# distributed under the License is distributed on an "AS IS" BASIS,13# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14# See the License for the specific language governing permissions and15# limitations under the License.16import mimetypes17import os18import re19import shutil20from typing import Optional21 22from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types23from smolagents.agents import ActionStep, MultiStepAgent24from smolagents.memory import MemoryStep25from smolagents.utils import _is_package_available26 27 28def pull_messages_from_step(29 step_log: MemoryStep,30):31 """Extract ChatMessage objects from agent steps with proper nesting"""32 import gradio as gr33 34 if isinstance(step_log, ActionStep):35 # Output the step number36 step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""37 yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")38 39 # First yield the thought/reasoning from the LLM40 if hasattr(step_log, "model_output") and step_log.model_output is not None:41 # Clean up the LLM output42 model_output = step_log.model_output.strip()43 # Remove any trailing <end_code> and extra backticks, handling multiple possible formats44 model_output = re.sub(r"```\s*<end_code>", "```", model_output) # handles ```<end_code>45 model_output = re.sub(r"<end_code>\s*```", "```", model_output) # handles <end_code>```46 model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output) # handles ```\n<end_code>47 model_output = model_output.strip()48 yield gr.ChatMessage(role="assistant", content=model_output)49 50 # For tool calls, create a parent message51 if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:52 first_tool_call = step_log.tool_calls[0]53 used_code = first_tool_call.name == "python_interpreter"54 parent_id = f"call_{len(step_log.tool_calls)}"55 56 # Tool call becomes the parent message with timing info57 # First we will handle arguments based on type58 args = first_tool_call.arguments59 if isinstance(args, dict):60 content = str(args.get("answer", str(args)))61 else:62 content = str(args).strip()63 64 if used_code:65 # Clean up the content by removing any end code tags66 content = re.sub(r"```.*?\n", "", content) # Remove existing code blocks67 content = re.sub(r"\s*<end_code>\s*", "", content) # Remove end_code tags68 content = content.strip()69 if not content.startswith("```python"):70 content = f"```python\n{content}\n```"71 72 parent_message_tool = gr.ChatMessage(73 role="assistant",74 content=content,75 metadata={76 "title": f"🛠️ Used tool {first_tool_call.name}",77 "id": parent_id,78 "status": "pending",79 },80 )81 yield parent_message_tool82 83 # Nesting execution logs under the tool call if they exist84 if hasattr(step_log, "observations") and (85 step_log.observations is not None and step_log.observations.strip()86 ): # Only yield execution logs if there's actual content87 log_content = step_log.observations.strip()88 if log_content:89 log_content = re.sub(r"^Execution logs:\s*", "", log_content)90 yield gr.ChatMessage(91 role="assistant",92 content=f"{log_content}",93 metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},94 )95 96 # Nesting any errors under the tool call97 if hasattr(step_log, "error") and step_log.error is not None:98 yield gr.ChatMessage(99 role="assistant",100 content=str(step_log.error),101 metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},102 )103 104 # Update parent message metadata to done status without yielding a new message105 parent_message_tool.metadata["status"] = "done"106 107 # Handle standalone errors but not from tool calls108 elif hasattr(step_log, "error") and step_log.error is not None:109 yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})110 111 # Calculate duration and token information112 step_footnote = f"{step_number}"113 if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):114 token_str = (115 f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"116 )117 step_footnote += token_str118 if hasattr(step_log, "duration"):119 step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None120 step_footnote += step_duration121 step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """122 yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")123 yield gr.ChatMessage(role="assistant", content="-----")124 125 126def stream_to_gradio(127 agent,128 task: str,129 reset_agent_memory: bool = False,130 additional_args: Optional[dict] = None,131):132 """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""133 if not _is_package_available("gradio"):134 raise ModuleNotFoundError(135 "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"136 )137 import gradio as gr138 139 total_input_tokens = 0140 total_output_tokens = 0141 142 for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):143 # Track tokens if model provides them144 if hasattr(agent.model, "last_input_token_count"):145 total_input_tokens += agent.model.last_input_token_count146 total_output_tokens += agent.model.last_output_token_count147 if isinstance(step_log, ActionStep):148 step_log.input_token_count = agent.model.last_input_token_count149 step_log.output_token_count = agent.model.last_output_token_count150 151 for message in pull_messages_from_step(152 step_log,153 ):154 yield message155 156 final_answer = step_log # Last log is the run's final_answer157 final_answer = handle_agent_output_types(final_answer)158 159 if isinstance(final_answer, AgentText):160 yield gr.ChatMessage(161 role="assistant",162 content=f"**Final answer:**\n{final_answer.to_string()}\n",163 )164 elif isinstance(final_answer, AgentImage):165 yield gr.ChatMessage(166 role="assistant",167 content={"path": final_answer.to_string(), "mime_type": "image/png"},168 )169 elif isinstance(final_answer, AgentAudio):170 yield gr.ChatMessage(171 role="assistant",172 content={"path": final_answer.to_string(), "mime_type": "audio/wav"},173 )174 else:175 yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")176 177 178class GradioUI:179 """A one-line interface to launch your agent in Gradio"""180 181 def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):182 if not _is_package_available("gradio"):183 raise ModuleNotFoundError(184 "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"185 )186 self.agent = agent187 self.file_upload_folder = file_upload_folder188 if self.file_upload_folder is not None:189 if not os.path.exists(file_upload_folder):190 os.mkdir(file_upload_folder)191 192 def interact_with_agent(self, prompt, messages):193 import gradio as gr194 195 messages.append(gr.ChatMessage(role="user", content=prompt))196 yield messages197 for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):198 messages.append(msg)199 yield messages200 yield messages201 202 def upload_file(203 self,204 file,205 file_uploads_log,206 allowed_file_types=[207 "application/pdf",208 "application/vnd.openxmlformats-officedocument.wordprocessingml.document",209 "text/plain",210 ],211 ):212 """213 Handle file uploads, default allowed types are .pdf, .docx, and .txt214 """215 import gradio as gr216 217 if file is None:218 return gr.Textbox("No file uploaded", visible=True), file_uploads_log219 220 try:221 mime_type, _ = mimetypes.guess_type(file.name)222 except Exception as e:223 return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log224 225 if mime_type not in allowed_file_types:226 return gr.Textbox("File type disallowed", visible=True), file_uploads_log227 228 # Sanitize file name229 original_name = os.path.basename(file.name)230 sanitized_name = re.sub(231 r"[^\w\-.]", "_", original_name232 ) # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores233 234 type_to_ext = {}235 for ext, t in mimetypes.types_map.items():236 if t not in type_to_ext:237 type_to_ext[t] = ext238 239 # Ensure the extension correlates to the mime type240 sanitized_name = sanitized_name.split(".")[:-1]241 sanitized_name.append("" + type_to_ext[mime_type])242 sanitized_name = "".join(sanitized_name)243 244 # Save the uploaded file to the specified folder245 file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))246 shutil.copy(file.name, file_path)247 248 return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]249 250 def log_user_message(self, text_input, file_uploads_log):251 return (252 text_input253 + (254 f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"255 if len(file_uploads_log) > 0256 else ""257 ),258 "",259 )260 261 def launch(self, **kwargs):262 import gradio as gr263 264 with gr.Blocks(fill_height=True) as demo:265 stored_messages = gr.State([])266 file_uploads_log = gr.State([])267 chatbot = gr.Chatbot(268 label="Agent",269 type="messages",270 avatar_images=(271 None,272 "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",273 ),274 resizeable=True,275 scale=1,276 )277 # If an upload folder is provided, enable the upload feature278 if self.file_upload_folder is not None:279 upload_file = gr.File(label="Upload a file")280 upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)281 upload_file.change(282 self.upload_file,283 [upload_file, file_uploads_log],284 [upload_status, file_uploads_log],285 )286 text_input = gr.Textbox(lines=1, label="Chat Message")287 text_input.submit(288 self.log_user_message,289 [text_input, file_uploads_log],290 [stored_messages, text_input],291 ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])292 293 demo.launch(debug=True, share=True, **kwargs)294 295 296__all__ = ["stream_to_gradio", "GradioUI"]