kfahn/Generative_Art_Examples
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 27greeting_message = "I can show you examples of generative art!"28example_user_message = "What does the appolonian gasket look like?"29 30def pull_messages_from_step(31 step_log: MemoryStep,32):33 """Extract ChatMessage objects from agent steps with proper nesting"""34 import gradio as gr35 36 if isinstance(step_log, ActionStep):37 # Output the step number38 step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""39 yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")40 41 # First yield the thought/reasoning from the LLM42 if hasattr(step_log, "model_output") and step_log.model_output is not None:43 # Clean up the LLM output44 model_output = step_log.model_output.strip()45 # Remove any trailing <end_code> and extra backticks, handling multiple possible formats46 model_output = re.sub(r"```\s*<end_code>", "```", model_output) # handles ```<end_code>47 model_output = re.sub(r"<end_code>\s*```", "```", model_output) # handles <end_code>```48 model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output) # handles ```\n<end_code>49 model_output = model_output.strip()50 yield gr.ChatMessage(role="assistant", content=model_output)51 52 # For tool calls, create a parent message53 if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:54 first_tool_call = step_log.tool_calls[0]55 used_code = first_tool_call.name == "python_interpreter"56 parent_id = f"call_{len(step_log.tool_calls)}"57 58 # Tool call becomes the parent message with timing info59 # First we will handle arguments based on type60 args = first_tool_call.arguments61 if isinstance(args, dict):62 content = str(args.get("answer", str(args)))63 else:64 content = str(args).strip()65 66 if used_code:67 # Clean up the content by removing any end code tags68 content = re.sub(r"```.*?\n", "", content) # Remove existing code blocks69 content = re.sub(r"\s*<end_code>\s*", "", content) # Remove end_code tags70 content = content.strip()71 if not content.startswith("```python"):72 content = f"```python\n{content}\n```"73 74 parent_message_tool = gr.ChatMessage(75 role="assistant",76 content=content,77 metadata={78 "title": f"🛠️ Used tool {first_tool_call.name}",79 "id": parent_id,80 "status": "pending",81 },82 )83 yield parent_message_tool84 85 # Nesting execution logs under the tool call if they exist86 if hasattr(step_log, "observations") and (87 step_log.observations is not None and step_log.observations.strip()88 ): # Only yield execution logs if there's actual content89 log_content = step_log.observations.strip()90 if log_content:91 log_content = re.sub(r"^Execution logs:\s*", "", log_content)92 yield gr.ChatMessage(93 role="assistant",94 content=f"{log_content}",95 metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},96 )97 98 # Nesting any errors under the tool call99 if hasattr(step_log, "error") and step_log.error is not None:100 yield gr.ChatMessage(101 role="assistant",102 content=str(step_log.error),103 metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},104 )105 106 # Update parent message metadata to done status without yielding a new message107 parent_message_tool.metadata["status"] = "done"108 109 # Handle standalone errors but not from tool calls110 elif hasattr(step_log, "error") and step_log.error is not None:111 yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})112 113 # Calculate duration and token information114 step_footnote = f"{step_number}"115 if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):116 token_str = (117 f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"118 )119 step_footnote += token_str120 if hasattr(step_log, "duration"):121 step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None122 step_footnote += step_duration123 step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """124 yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")125 yield gr.ChatMessage(role="assistant", content="-----")126 127 128def stream_to_gradio(129 agent,130 task: str,131 reset_agent_memory: bool = False,132 additional_args: Optional[dict] = None,133):134 """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""135 if not _is_package_available("gradio"):136 raise ModuleNotFoundError(137 "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"138 )139 import gradio as gr140 141 total_input_tokens = 0142 total_output_tokens = 0143 144 for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):145 # Track tokens if model provides them146 if hasattr(agent.model, "last_input_token_count"):147 total_input_tokens += agent.model.last_input_token_count148 total_output_tokens += agent.model.last_output_token_count149 if isinstance(step_log, ActionStep):150 step_log.input_token_count = agent.model.last_input_token_count151 step_log.output_token_count = agent.model.last_output_token_count152 153 for message in pull_messages_from_step(154 step_log,155 ):156 yield message157 158 final_answer = step_log # Last log is the run's final_answer159 final_answer = handle_agent_output_types(final_answer)160 161 if isinstance(final_answer, AgentText):162 yield gr.ChatMessage(163 role="assistant",164 content=f"**Final answer:**\n{final_answer.to_string()}\n",165 )166 elif isinstance(final_answer, AgentImage):167 yield gr.ChatMessage(168 role="assistant",169 content={"path": final_answer.to_string(), "mime_type": "image/png"},170 )171 elif isinstance(final_answer, AgentAudio):172 yield gr.ChatMessage(173 role="assistant",174 content={"path": final_answer.to_string(), "mime_type": "audio/wav"},175 )176 else:177 yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")178 179 180class GradioUI:181 """A one-line interface to launch your agent in Gradio"""182 183 def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):184 if not _is_package_available("gradio"):185 raise ModuleNotFoundError(186 "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"187 )188 self.agent = agent189 self.file_upload_folder = file_upload_folder190 if self.file_upload_folder is not None:191 if not os.path.exists(file_upload_folder):192 os.mkdir(file_upload_folder)193 194 def interact_with_agent(self, prompt, messages):195 import gradio as gr196 197 messages.append(gr.ChatMessage(role="user", content=prompt))198 yield messages199 for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):200 messages.append(msg)201 yield messages202 yield messages203 204 def upload_file(205 self,206 file,207 file_uploads_log,208 allowed_file_types=[209 "application/pdf",210 "application/vnd.openxmlformats-officedocument.wordprocessingml.document",211 "text/plain",212 ],213 ):214 """215 Handle file uploads, default allowed types are .pdf, .docx, and .txt216 """217 import gradio as gr218 219 if file is None:220 return gr.Textbox("No file uploaded", visible=True), file_uploads_log221 222 try:223 mime_type, _ = mimetypes.guess_type(file.name)224 except Exception as e:225 return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log226 227 if mime_type not in allowed_file_types:228 return gr.Textbox("File type disallowed", visible=True), file_uploads_log229 230 # Sanitize file name231 original_name = os.path.basename(file.name)232 sanitized_name = re.sub(233 r"[^\w\-.]", "_", original_name234 ) # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores235 236 type_to_ext = {}237 for ext, t in mimetypes.types_map.items():238 if t not in type_to_ext:239 type_to_ext[t] = ext240 241 # Ensure the extension correlates to the mime type242 sanitized_name = sanitized_name.split(".")[:-1]243 sanitized_name.append("" + type_to_ext[mime_type])244 sanitized_name = "".join(sanitized_name)245 246 # Save the uploaded file to the specified folder247 file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))248 shutil.copy(file.name, file_path)249 250 return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]251 252 def log_user_message(self, text_input, file_uploads_log):253 return (254 text_input255 + (256 f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"257 if len(file_uploads_log) > 0258 else ""259 ),260 "",261 )262 263 def launch(self, **kwargs):264 import gradio as gr265 266 with gr.Blocks(fill_height=True) as demo:267 gr.Markdown(greeting_message)268 stored_messages = gr.State([])269 file_uploads_log = gr.State([])270 chatbot = gr.Chatbot(271 label="Agent",272 type="messages",273 avatar_images=(274 None,275 "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",276 ),277 resizeable=True,278 scale=1,279 )280 # If an upload folder is provided, enable the upload feature281 if self.file_upload_folder is not None:282 upload_file = gr.File(label="Upload a file")283 upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)284 upload_file.change(285 self.upload_file,286 [upload_file, file_uploads_log],287 [upload_status, file_uploads_log],288 )289 text_input = gr.Textbox(lines=1, label="Chat Message", placeholder=example_user_message)290 text_input.submit(291 self.log_user_message,292 [text_input, file_uploads_log],293 [stored_messages, text_input],294 ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])295 296 demo.launch(debug=True, share=True, **kwargs)297 298 299__all__ = ["stream_to_gradio", "GradioUI"]