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import argparse | |
import json | |
import os | |
import re | |
import threading | |
import mimetypes | |
import shutil | |
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, | |
SearchInformationTool, | |
SimpleTextBrowser, | |
VisitTool, | |
) | |
from scripts.visual_qa import visualizer | |
from tqdm import tqdm | |
from smolagents import ( | |
# MANAGED_AGENT_PROMPT, | |
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 | |
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() | |
SET = "validation" | |
custom_role_conversions = {"tool-call": "assistant", "tool-response": "user"} | |
# skip | |
_ = """ | |
### LOAD EVALUATION DATASET | |
eval_ds = datasets.load_dataset("gaia-benchmark/GAIA", "2023_all")[SET] | |
eval_ds = eval_ds.rename_columns({"Question": "question", "Final answer": "true_answer", "Level": "task"}) | |
def preprocess_file_paths(row): | |
if len(row["file_name"]) > 0: | |
row["file_name"] = f"data/gaia/{SET}/" + row["file_name"] | |
return row | |
eval_ds = eval_ds.map(preprocess_file_paths) | |
eval_df = pd.DataFrame(eval_ds) | |
print("Loaded evaluation dataset:") | |
print(eval_df["task"].value_counts()) | |
# """ | |
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) | |
# --- 수정된 부분: OPENAI_API_BASE의 잘못된 엔드포인트 제거 --- | |
openai_api_base = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1") | |
# 만약 환경변수에 불필요한 엔드포인트가 포함되어 있다면 제거합니다. | |
if openai_api_base.endswith("/chat/completions"): | |
openai_api_base = openai_api_base.rsplit("/chat/completions", 1)[0] | |
model = LiteLLMModel( | |
os.getenv("MODEL_ID", "gpt-4o-mini"), | |
custom_role_conversions=custom_role_conversions, | |
api_base=openai_api_base, | |
api_key=os.getenv("OPENAI_API_KEY"), | |
) | |
# --- 수정 끝 --- | |
text_limit = 20000 | |
ti_tool = TextInspectorTool(model, text_limit) | |
browser = SimpleTextBrowser(**BROWSER_CONFIG) | |
WEB_TOOLS = [ | |
SearchInformationTool(browser), | |
VisitTool(browser), | |
PageUpTool(browser), | |
PageDownTool(browser), | |
FinderTool(browser), | |
FindNextTool(browser), | |
ArchiveSearchTool(browser), | |
TextInspectorTool(model, text_limit), | |
] | |
agent = CodeAgent( | |
model=model, | |
tools=[visualizer] + WEB_TOOLS, | |
max_steps=5, | |
verbosity_level=2, | |
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, agent, file_upload_folder: str | None = None): | |
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 interact_with_agent(self, prompt, messages): | |
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 | |
""" | |
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): | |
with gr.Blocks(theme="ocean", fill_height=True) as demo: | |
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 below. 👇""") | |
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, | |
) | |
# 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") | |
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]) | |
demo.launch(debug=True, share=True, **kwargs) | |
GradioUI(agent).launch() | |