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| import math | |
| import os | |
| import re | |
| import numexpr | |
| from langchain_community.document_loaders import ArxivLoader | |
| import requests | |
| from smolagents import ( | |
| CodeAgent, | |
| GoogleSearchTool, | |
| InferenceClientModel, | |
| WikipediaSearchTool, | |
| tool, | |
| ) | |
| hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| def calculator(expression: str) -> str: | |
| """Calculate expression using Python's numexpr library. | |
| Expression should be a single line mathematical expression | |
| that solves the problem. | |
| Examples: | |
| "37593 * 67" for "37593 times 67" | |
| "37593**(1/5)" for "37593^(1/5)" | |
| Args: | |
| expression: the expression to be compute | |
| """ | |
| local_dict = {"pi": math.pi, "e": math.e} | |
| return str( | |
| numexpr.evaluate( | |
| expression.strip(), | |
| global_dict={}, # restrict access to globals | |
| local_dict=local_dict, # add common mathematical functions | |
| ) | |
| ) | |
| def arxiv_search_tool(query: str) -> str: | |
| """ | |
| Searches Arxiv and returns a summary or full text of the given topic of the 5 most likely related papers, along with the page URL. | |
| Args: | |
| query: the topic to be searched | |
| """ | |
| docs = ArxivLoader(query=query, load_max_docs=5).load() | |
| if len(docs) > 0: | |
| return "\n".join( | |
| [ | |
| f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>' | |
| for doc in docs | |
| ] | |
| ) | |
| return "No content found" | |
| def download_attached_files(task_id: str) -> str: | |
| """ | |
| Get the first attached file if exists and save it. Returns the filename. | |
| Args: | |
| task_id: this id of the task | |
| """ | |
| api_url = DEFAULT_API_URL | |
| file_url = f"{api_url}/files/{task_id}" | |
| response = requests.get(file_url, timeout=15) | |
| if response.status_code == 200: | |
| cd = response.headers.get("content-disposition") | |
| if cd: | |
| fname = re.findall("filename=(.+)", cd) | |
| if len(fname) > 0: | |
| with open(fname[0], "wb") as file: | |
| file.write(response.content) | |
| return f"{fname[0]} saved " | |
| return "no file attached to this task" | |
| tools = [ | |
| calculator, | |
| arxiv_search_tool, | |
| download_attached_files, | |
| GoogleSearchTool(provider="serper"), | |
| WikipediaSearchTool(), | |
| ] | |
| model_id = "Qwen/Qwen3-235B-A22B" | |
| model = InferenceClientModel( | |
| model_id=model_id, token=hf_token | |
| ) # You can choose to not pass any model_id to InferenceClientModel to use a default model | |
| agent = CodeAgent( | |
| model=model, | |
| tools=tools, | |
| add_base_tools=True, | |
| additional_authorized_imports=["pandas", "numpy", "csv", "subprocess"], | |
| ) | |