import smolagents, numpy, math, xlrd, os import pandas as pd from typing import Union from smolagents import ( tool, CodeAgent, HfApiModel, InferenceClientModel, WebSearchTool, PythonInterpreterTool, FinalAnswerTool, DuckDuckGoSearchTool, GoogleSearchTool ) #* @tool def analyze_excel_file(file_path: str, query: str) -> str: """ Analyze an Excel file using pandas and answer a question about it. Args: file_path: Path to the Excel file query: Question about the data Returns: Analysis result or error message """ try: import pandas as pd # Read the Excel file df = pd.read_excel(file_path) # Run various analyses based on the query result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n" result += f"Columns: {', '.join(df.columns)}\n\n" # Add summary statistics result += "Summary statistics:\n" result += str(df.describe()) return result except ImportError: return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'." except Exception as e: return f"Error analyzing Excel file: {str(e)}" # class newAgent: """Adapts smolagents.CodeAgent to the HF course template API.""" def __init__(self): model_id = "meta-llama/Meta-Llama-3-70B-Instruct" # correct repo name hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") # read real secret if not hf_token: raise RuntimeError("HUGGINGFACEHUB_API_TOKEN not set in Space secrets") #* system_prompt=( "You are an agent that answers exam questions." "Your answers should contain only what is asked for in the question, without any other content." "Be exact and concise in your answers to get a good score on the exam questions. Do not add explanation to the answers." "If you are ask for a list of items, reply with ONLY those items separated by commas, no other text." "You have a tool to read Excel files. The Excel file tool is named read_excel" ) #* model = HfApiModel(model_id=model_id, token=hf_token) # include FinalAnswerTool in tools so agent knows when to stop tools = [FinalAnswerTool(), read_excel] self.agent = CodeAgent( tools=tools, model=model, add_base_tools=True, max_steps=5 # limit reasoning time ) def __call__(self, question: str) -> str: """ONE question in → ONE pure-text answer out.""" #↓ Replace .run with whatever method actually returns the answer string. result = self.agent.run(question) return result #answer = self.run #agent.run( # "At what temperature and for how long should I bake French baguettes made with type 65 flour?", #)