app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool,HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool

from kaggle.api.kaggle_api_extended import KaggleApi

from Gradio_UI import GradioUI

# Below is an example of a tool that does nothing. Amaze us with your creativity !
@tool
def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
    #Keep this format for the description / args / args description but feel free to modify the tool
    """A tool that does nothing yet 
    Args:
        arg1: the first argument
        arg2: the second argument
    """
    return "What magic will you build ?"

@tool
def search_kaggle_datasets(search_term:str, kaggle_username=None:str, kaggle_key=None:str, max_results:int)-> str:
    """Search for datasets on Kaggle based on a search term.
    Args:
        search_term: The term to search for.
        kaggle_username: Your Kaggle username.
        kaggle_key: Your Kaggle API key.
        max_results: Maximum number of results to return.
    """
     # Initialize the Kaggle API
    api = KaggleApi()

    # Authenticate using provided credentials
    if kaggle_username and kaggle_key:
        # Create a temporary kaggle.json file
        kaggle_json_content = f'{{"username":"{kaggle_username}","key":"{kaggle_key}"}}'
        kaggle_json_path = os.path.expanduser("~/.kaggle/kaggle.json")
        os.makedirs(os.path.dirname(kaggle_json_path), exist_ok=True)
        with open(kaggle_json_path, "w") as f:
            f.write(kaggle_json_content)
        os.chmod(kaggle_json_path, 0o600)  # Set permissions to read/write for the owner only
    else:
        # Use the default kaggle.json file if no credentials are provided
        return 'Error in searching Kaggle datasets: No username or key provided.'

    api.authenticate()

    # Search for datasets
    datasets = api.dataset_list(search=search_term)

    # Limit the number of results
    datasets = datasets[:max_results]

    # Extract relevant information
    results = []
    for dataset in datasets:
        dataset_info = api.dataset_view(dataset)
        results.append({
            'title': dataset_info['title'],
            'url': f"https://www.kaggle.com/{dataset_info['ref']}",
            'size': dataset_info['size'],
            'files': dataset_info['files'],
            'last_updated': dataset_info['lastUpdated']
        })

    # Clean up the temporary kaggle.json file if it was created
    if kaggle_username and kaggle_key:
        os.remove(kaggle_json_path)

    return results


@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """A tool that fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    try:
        # Create timezone object
        tz = pytz.timezone(timezone)
        # Get current time in that timezone
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
        return f"The current local time in {timezone} is: {local_time}"
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"


final_answer = FinalAnswerTool()

# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 

model = HfApiModel(
max_tokens=2096,
temperature=0.5,
model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
custom_role_conversions=None,
)


# Import tool from Hub
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)
    
agent = CodeAgent(
    model=model,
    tools=[final_answer], ## add your tools here (don't remove final answer)
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()