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from smolagents import tool
import numpy as np
import time
import datetime
import re
from markdownify import markdownify
import requests
from requests.exceptions import RequestException
import litellm
from dotenv import load_dotenv
from pathlib import Path
load_dotenv()
@tool
def calculator_tool(expression: str) -> str:
"""
A simple calculator that performs basic arithmetic operations.
Args:
expression: The mathematical expression to evaluate (e.g., '2 + 3 * 4').
"""
# Remove any non-digit or non-operator characters from the expression
expression = re.sub(r'[^0-9+\-*/().]', '', expression)
try:
# Evaluate the expression using the built-in eval() function
result = eval(expression)
return str(result)
except (SyntaxError, ZeroDivisionError, NameError, TypeError, OverflowError):
return "Error: Invalid expression"
@tool
def visit_webpage(url: str) -> str:
"""Visits a webpage at the given URL and returns its content as a markdown string.
Args:
url: The URL of the webpage to visit.
Returns:
The content of the webpage converted to Markdown, or an error message if the request fails.
"""
try:
# Send a GET request to the URL
response = requests.get(url)
response.raise_for_status() # Raise an exception for bad status codes
# Convert the HTML content to Markdown
markdown_content = markdownify(response.text).strip()
# Remove multiple line breaks
markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
return markdown_content
except RequestException as e:
return f"Error fetching the webpage: {str(e)}"
except Exception as e:
return f"An unexpected error occurred: {str(e)}"
@tool
def read_excel_file(file_path: str) -> str:
"""Reads an Excel file and returns its content as a string.
Args:
file_path: The path to the Excel file.
Returns:
The content of the Excel file as a string, or an error message if the file cannot be read.
"""
try:
import pandas as pd
# Read the Excel file
df = pd.read_excel(file_path)
# Convert the DataFrame to a string representation
return df.to_string()
except Exception as e:
return f"Error reading the Excel file: {str(e)}"
@tool
def read_python_file(file_path: str) -> str:
"""Reads a Python file and returns its content as a string.
Args:
file_path: The path to the Python file.
Returns:
The content of the Python file as a string, or an error message if the file cannot be read.
"""
try:
with open(file_path, 'r') as file:
content = file.read()
return content
except Exception as e:
return f"Error reading the Python file: {str(e)}"
@tool
def read_python_file(file_path: str) -> str:
"""Reads a Python file and returns its content as a string.
Args:
file_path: The path to the Python file.
Returns:
The content of the Python file as a string, or an error message if the file cannot be read.
"""
try:
with open(file_path, 'r') as file:
content = file.read()
return content
except Exception as e:
return f"Error reading the Python file: {str(e)}"
import base64
def convert_image_to_base64(image_path: str) -> str:
"""
Converts an image to a Base64 string.
Args:
image_path: The path to the image file.
Returns:
A Base64 encoded string of the image.
"""
try:
with open(image_path, "rb") as image_file:
# Read the image file as binary data
encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
return encoded_string
except Exception as e:
return f"Error converting image to Base64: {str(e)}"
@tool
def describe_image(image_path: str) -> str:
"""
Return the description of a given image.
Args:
image_path: the input path of the image to describe.
"""
encoded_data = convert_image_to_base64(image_path)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe the image in detail."
},
# {
# "type": "image_url",
# "image_url": {"url": image_path}
# }
{
"type": "file",
"file": {
"file_data": f"data:image/png;base64,{encoded_data}", # 👈 SET MIME_TYPE + DATA
}
},
]
}
]
# Make the API call to Gemini model
response = litellm.completion(
model="gemini/gemini-2.0-flash-lite",
messages=messages,
)
# Extract the response content
content = response.get('choices', [{}])[0].get('message', {}).get('content')
return content
@tool
def describe_audio(audio_path: str) -> str:
"""
Return the transcription of a given audio file.
Args:
audio_path: the input path of the audio to transcribe.
"""
audio_bytes = Path(audio_path).read_bytes()
encoded_data = base64.b64encode(audio_bytes).decode("utf-8")
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Please transcribe the content of this audio."},
{
"type": "file",
"file": {
"file_data": "data:audio/mp3;base64,{}".format(encoded_data), # 👈 SET MIME_TYPE + DATA
}
},
],
}
]
# Make the API call to Gemini model
response = litellm.completion(
model="gemini/gemini-2.0-flash-lite",
messages=messages,
)
# Extract the response content
content = response.get('choices', [{}])[0].get('message', {}).get('content')
return content
if __name__ == "__main__":
# example usage of image description
image_path = "./cca530fc-4052-43b2-b130-b30968d8aa44.png"
description = describe_image(image_path)
print(description) |