import argparse import json import os import threading from concurrent.futures import ThreadPoolExecutor, as_completed from datetime import datetime from pathlib import Path from typing import List, Optional import datasets import pandas as pd from dotenv import load_dotenv from huggingface_hub import login import gradio as gr from scripts.reformulator import prepare_response from scripts.run_agents import ( get_single_file_description, get_zip_description, ) from scripts.text_inspector_tool import TextInspectorTool from scripts.text_web_browser import ( ArchiveSearchTool, FinderTool, FindNextTool, PageDownTool, PageUpTool, SimpleTextBrowser, VisitTool, ) from scripts.visual_qa import visualizer from tqdm import tqdm from smolagents import ( CodeAgent, HfApiModel, LiteLLMModel, Model, ToolCallingAgent, ) from smolagents.agent_types import AgentText, AgentImage, AgentAudio from smolagents.gradio_ui import pull_messages_from_step, handle_agent_output_types from smolagents import Tool class GoogleSearchTool(Tool): name = "web_search" description = """Performs a google web search for your query then returns a string of the top search results.""" inputs = { "query": {"type": "string", "description": "The search query to perform."}, "filter_year": { "type": "integer", "description": "Optionally restrict results to a certain year", "nullable": True, }, } output_type = "string" def __init__(self): super().__init__(self) import os self.serpapi_key = os.getenv("SERPER_API_KEY") def forward(self, query: str, filter_year: Optional[int] = None) -> str: import requests if self.serpapi_key is None: raise ValueError("Missing SerpAPI key. Make sure you have 'SERPER_API_KEY' in your env variables.") params = { "engine": "google", "q": query, "api_key": self.serpapi_key, "google_domain": "google.com", } headers = { 'X-API-KEY': self.serpapi_key, 'Content-Type': 'application/json' } if filter_year is not None: params["tbs"] = f"cdr:1,cd_min:01/01/{filter_year},cd_max:12/31/{filter_year}" response = requests.request("POST", "https://google.serper.dev/search", headers=headers, data=json.dumps(params)) if response.status_code == 200: results = response.json() else: raise ValueError(response.json()) if "organic" not in results.keys(): print("REZZZ", results.keys()) if filter_year is not None: raise Exception( f"No results found for query: '{query}' with filtering on year={filter_year}. Use a less restrictive query or do not filter on year." ) else: raise Exception(f"No results found for query: '{query}'. Use a less restrictive query.") if len(results["organic"]) == 0: year_filter_message = f" with filter year={filter_year}" if filter_year is not None else "" return f"No results found for '{query}'{year_filter_message}. Try with a more general query, or remove the year filter." web_snippets = [] if "organic" in results: for idx, page in enumerate(results["organic"]): date_published = "" if "date" in page: date_published = "\nDate published: " + page["date"] source = "" if "source" in page: source = "\nSource: " + page["source"] snippet = "" if "snippet" in page: snippet = "\n" + page["snippet"] redacted_version = f"{idx}. [{page['title']}]({page['link']}){date_published}{source}\n{snippet}" redacted_version = redacted_version.replace("Your browser can't play this video.", "") web_snippets.append(redacted_version) return "## Search Results\n" + "\n\n".join(web_snippets) # web_search = GoogleSearchTool() # print(web_search(query="Donald Trump news")) # quit() AUTHORIZED_IMPORTS = [ "requests", "zipfile", "os", "pandas", "numpy", "sympy", "json", "bs4", "pubchempy", "xml", "yahoo_finance", "Bio", "sklearn", "scipy", "pydub", "io", "PIL", "chess", "PyPDF2", "pptx", "torch", "datetime", "fractions", "csv", ] load_dotenv(override=True) login(os.getenv("HF_TOKEN")) append_answer_lock = threading.Lock() custom_role_conversions = {"tool-call": "assistant", "tool-response": "user"} user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0" BROWSER_CONFIG = { "viewport_size": 1024 * 5, "downloads_folder": "downloads_folder", "request_kwargs": { "headers": {"User-Agent": user_agent}, "timeout": 300, }, "serpapi_key": os.getenv("SERPAPI_API_KEY"), } os.makedirs(f"./{BROWSER_CONFIG['downloads_folder']}", exist_ok=True) model = LiteLLMModel( "gpt-4o", custom_role_conversions=custom_role_conversions, api_key=os.getenv("OPENAI_API_KEY") ) text_limit = 20000 ti_tool = TextInspectorTool(model, text_limit) browser = SimpleTextBrowser(**BROWSER_CONFIG) WEB_TOOLS = [ GoogleSearchTool(), VisitTool(browser), PageUpTool(browser), PageDownTool(browser), FinderTool(browser), FindNextTool(browser), ArchiveSearchTool(browser), TextInspectorTool(model, text_limit), ] # Agent creation in a factory function def create_agent(): """Creates a fresh agent instance for each session""" return CodeAgent( model=model, tools=[visualizer] + WEB_TOOLS, max_steps=10, verbosity_level=1, additional_authorized_imports=AUTHORIZED_IMPORTS, planning_interval=4, ) document_inspection_tool = TextInspectorTool(model, 20000) 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.""" for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args): 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/wav"}, ) 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, file_upload_folder: str | None = None): 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 interact_with_agent(self, prompt, messages, session_state): # Get or create session-specific agent if 'agent' not in session_state: session_state['agent'] = create_agent() # Adding monitoring try: # log the existence of agent memory has_memory = hasattr(session_state['agent'], 'memory') print(f"Agent has memory: {has_memory}") if has_memory: print(f"Memory type: {type(session_state['agent'].memory)}") messages.append(gr.ChatMessage(role="user", content=prompt)) yield messages for msg in stream_to_gradio(session_state['agent'], task=prompt, reset_agent_memory=False): messages.append(msg) yield messages yield messages except Exception as e: print(f"Error in interaction: {str(e)}") raise 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 """ 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 "" ), gr.Textbox(value="", interactive=False, placeholder="Please wait while Steps are getting populated"), gr.Button(interactive=False) ) def detect_device(self, request: gr.Request): # Check whether the user device is a mobile or a computer if not request: return "Unknown device" # Method 1: Check sec-ch-ua-mobile header is_mobile_header = request.headers.get('sec-ch-ua-mobile') if is_mobile_header: return "Mobile" if '?1' in is_mobile_header else "Desktop" # Method 2: Check user-agent string user_agent = request.headers.get('user-agent', '').lower() mobile_keywords = ['android', 'iphone', 'ipad', 'mobile', 'phone'] if any(keyword in user_agent for keyword in mobile_keywords): return "Mobile" # Method 3: Check platform platform = request.headers.get('sec-ch-ua-platform', '').lower() if platform: if platform in ['"android"', '"ios"']: return "Mobile" elif platform in ['"windows"', '"macos"', '"linux"']: return "Desktop" # Default case if no clear indicators return "Desktop" def launch(self, **kwargs): with gr.Blocks(theme="ocean", fill_height=True) as demo: # Different layouts for mobile and computer devices @gr.render() def layout(request: gr.Request): device = self.detect_device(request) print(f"device - {device}") # Render layout with sidebar if device == "Desktop": with gr.Blocks(fill_height=True,) as sidebar_demo: with gr.Sidebar(): gr.Markdown("""# open Deep Research - free the AI agents! OpenAI just published [Deep Research](https://openai.com/index/introducing-deep-research/), a very nice assistant that can perform deep searches on the web to answer user questions. However, their agent has a huge downside: it's not open. So we've started a 24-hour rush to replicate and open-source it. Our resulting [open-Deep-Research agent](https://github.com/huggingface/smolagents/tree/main/examples/open_deep_research) took the #1 rank of any open submission on the GAIA leaderboard! ✨ You can try a simplified version here.

""") with gr.Group(): gr.Markdown("**Your request**", container=True) text_input = gr.Textbox(lines=3, label="Your request", container=False, placeholder="Enter your prompt here and press Shift+Enter or press the button") launch_research_btn = gr.Button("Run", variant="primary") # If an upload folder is provided, enable the upload feature 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], ) gr.HTML("

Powered by:

") with gr.Row(): gr.HTML("""
logo huggingface/smolagents
""") # Add session state to store session-specific data session_state = gr.State({}) # Initialize empty state for each session stored_messages = gr.State([]) file_uploads_log = gr.State([]) chatbot = gr.Chatbot( label="open-Deep-Research", type="messages", avatar_images=( None, "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png", ), resizeable=False, scale=1, elem_id="my-chatbot", show_share_button=False, ) text_input.submit( self.log_user_message, [text_input, file_uploads_log], [stored_messages, text_input, launch_research_btn], ).then(self.interact_with_agent, # Include session_state in function calls [stored_messages, chatbot, session_state], [chatbot] ).then(lambda : (gr.Textbox(interactive=True, placeholder="Enter your prompt here and press the button"), gr.Button(interactive=True)), None, [text_input, launch_research_btn]) launch_research_btn.click( self.log_user_message, [text_input, file_uploads_log], [stored_messages, text_input, launch_research_btn], ).then(self.interact_with_agent, # Include session_state in function calls [stored_messages, chatbot, session_state], [chatbot] ).then(lambda : (gr.Textbox(interactive=True, placeholder="Enter your prompt here and press the button"), gr.Button(interactive=True)), None, [text_input, launch_research_btn]) # Render simple layout else: with gr.Blocks(fill_height=True,) as simple_demo: gr.Markdown("""# open Deep Research - free the AI agents! _Built with [smolagents](https://github.com/huggingface/smolagents)_ OpenAI just published [Deep Research](https://openai.com/index/introducing-deep-research/), a very nice assistant that can perform deep searches on the web to answer user questions. However, their agent has a huge downside: it's not open. So we've started a 24-hour rush to replicate and open-source it. Our resulting [open-Deep-Research agent](https://github.com/huggingface/smolagents/tree/main/examples/open_deep_research) took the #1 rank of any open submission on the GAIA leaderboard! ✨ You can try a simplified version below. 👇""") # Add session state to store session-specific data session_state = gr.State({}) # Initialize empty state for each session stored_messages = gr.State([]) file_uploads_log = gr.State([]) chatbot = gr.Chatbot( label="open-Deep-Research", type="messages", avatar_images=( None, "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png", ), resizeable=True, scale=1, show_share_button=False ) # If an upload folder is provided, enable the upload feature 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="Your request", placeholder="Enter your prompt here and press the button") launch_research_btn = gr.Button("Run", variant="primary",) text_input.submit( self.log_user_message, [text_input, file_uploads_log], [stored_messages, text_input, launch_research_btn], ).then(self.interact_with_agent, # Include session_state in function calls [stored_messages, chatbot, session_state], [chatbot] ).then(lambda : (gr.Textbox(interactive=True, placeholder="Enter your prompt here and press the button"), gr.Button(interactive=True)), None, [text_input, launch_research_btn]) launch_research_btn.click( self.log_user_message, [text_input, file_uploads_log], [stored_messages, text_input, launch_research_btn], ).then(self.interact_with_agent, # Include session_state in function calls [stored_messages, chatbot, session_state], [chatbot] ).then(lambda : (gr.Textbox(interactive=True, placeholder="Enter your prompt here and press the button"), gr.Button(interactive=True)), None, [text_input, launch_research_btn]) demo.launch(debug=True, **kwargs) GradioUI().launch()