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import os
import argparse
from pathlib import Path
from typing import List, Dict, Any, Optional
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
# Import pandas for Excel/CSV handling
try:
import pandas as pd
except ImportError:
print("Error: 'pandas' and 'openpyxl' libraries are required for ExcelAgent.")
print("Please install them with: pip install pandas openpyxl")
exit(1)
# Load environment variables (e.g., HF_TOKEN) from a .env file
load_dotenv()
# This is the system prompt you provided
AGENT_SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
class ExcelAgent:
"""
Encapsulated agent for answering questions about an Excel file.
This agent works in two stages:
1. Read the Excel file and convert its content to a text (CSV) format.
2. Pass the user's question and the text-based data to a chat model
to get the final answer.
Usage:
# Assumes HF_TOKEN is in your environment
agent = ExcelAgent()
# Ask a question with an Excel file
question = "What are the total sales?"
excel_path = "/path/to/your/data.xlsx"
answer = agent.answer(question, excel_path)
# Or use the callable shortcut
answer = agent(question, excel_path)
"""
def __init__(
self,
chat_model_id: Optional[str] = None,
provider: Optional[str] = "novita",
temperature: float = 0.1,
api_key: Optional[str] = None,
):
"""
Initializes the ExcelAgent.
Args:
chat_model_id: The repo ID of the chat model.
provider: The provider for the chat InferenceClient.
temperature: The generation temperature for the chat model.
api_key: The API key. Defaults to os.environ["HF_TOKEN"].
"""
# We use the same powerful model as the ImageAgent
self.chat_model_id = chat_model_id or "meta-llama/Llama-4-Scout-17B-16E-Instruct"
self.temperature = temperature
# Get API key from arg or environment
self.api_key = api_key or os.environ.get("HF_TOKEN")
if not self.api_key:
raise ValueError(
"API key not found. Please set the HF_TOKEN environment variable "
"or pass it as 'api_key' to the ExcelAgent."
)
# Client for Chat
self.chat_client = InferenceClient(
provider=provider,
api_key=self.api_key
)
# ---- Public API ----
def answer(self, question: str, excel_path: str) -> str:
"""
Run the agent to answer a question about an Excel file.
Args:
question: The text prompt to ask.
excel_path: The local file path to the Excel file (.xlsx).
Returns:
The parsed "Final Answer" string from the chat model.
"""
# 1. Read Excel and convert to text
try:
data_as_text = self._excel_to_text(excel_path)
except FileNotFoundError:
return f"Error: Excel file not found at {excel_path}"
except Exception as e:
return f"Error during Excel processing: {e}"
# 2. Build the combined prompt for the chat model
combined_prompt = (
f"You will be given a user's question and the content of an Excel file. "
f"The Excel data is represented in CSV format. "
f"Your task is to answer the question based *only* on the provided data.\n\n"
f"--- DATA (CSV format) ---\n"
f"{data_as_text}\n"
f"--- END DATA ---\n\n"
f"--- QUESTION ---\n"
f"{question}\n"
f"--- END QUESTION ---"
)
# 3. Build the messages payload
messages = self._build_chat_messages(combined_prompt)
# 4. Call the Chat API
try:
completion = self.chat_client.chat.completions.create(
model=self.chat_model_id,
messages=messages,
temperature=self.temperature,
)
response_text = completion.choices[0].message.content
except Exception as e:
return f"Error calling Chat API: {e}"
# 5. Parse and return the final answer
return self._parse_answer(response_text)
def __call__(self, question: str, excel_path: str) -> str:
"""Callable shortcut for self.answer()."""
return self.answer(question, excel_path)
# ---- Internals ----
def _excel_to_text(self, excel_path: str) -> str:
"""Reads an Excel file and converts it to a CSV string."""
p = Path(excel_path)
if not p.exists():
raise FileNotFoundError(f"No file found at {excel_path}")
try:
# Read the Excel file
# By default, reads the first sheet
df = pd.read_excel(str(p))
# Convert DataFrame to a CSV string
# index=False to avoid writing row numbers
csv_string = df.to_csv(index=False)
return csv_string
except Exception as e:
# Re-raise as a more informative exception
raise RuntimeError(f"Error reading/converting Excel file: {e}") from e
def _build_chat_messages(self, combined_prompt: str) -> List[Dict[str, Any]]:
"""Constructs the message payload for the chat API."""
return [
{
"role": "system",
"content": AGENT_SYSTEM_PROMPT,
},
{
"role": "user",
"content": combined_prompt,
},
]
def _parse_answer(self, response: str) -> str:
"""Extracts the text after 'FINAL ANSWER: '."""
try:
# Use rpartition for robustness, splits on the *last* occurrence
_before, _marker, final_answer = response.rpartition("FINAL ANSWER: ")
if _marker:
return final_answer.strip()
else:
# If marker isn't found, return the whole response with a warning
print("Warning: Could not find 'FINAL ANSWER:' marker. Returning full response.")
return response.strip()
except Exception as e:
print(f"Error parsing response: {e}")
return response.strip()
# ---- CLI Entrypoint ----
def main():
"""
Allows running the agent from the command line.
Example:
python excel_agent.py "What are the total sales?" "path/to/my_data.xlsx"
"""
parser = argparse.ArgumentParser(description="Class-based agent for Excel questions.")
parser.add_argument("question", type=str, help="Your question (quoted).")
parser.add_argument("excel_path", type=str, help="Path to the Excel file.")
args = parser.parse_args()
try:
excel_agent = ExcelAgent()
answer = excel_agent.answer(args.question, args.excel_path)
print(answer)
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
main() |