app.py

from smolagents import CodeAgent,ToolCallingAgent,HfApiModel,load_tool,tool
import yaml
from tools.final_answer import FinalAnswerTool
from tools.user_input import UserInputTool

from kaggle.api.kaggle_api_extended import KaggleApi
import os

from Gradio_UI import GradioUI

os.environ['KAGGLE_USERNAME'] = ''
os.environ['KAGGLE_KEY'] = ''

def auth_kaggle() -> KaggleApi:
    """Authenticate Kaggle and return the API object.
    """
    api = KaggleApi()
    try:
        api.authenticate()
    except Exception as e:
        return f"Error authenticating with Kaggle: {str(e)}"
    return api

@tool
def search_kaggle_datasets(search_term:str, 
                           max_results:int = 10
                           ) -> list[dict[str]]:
    """Search for datasets on Kaggle based on a search term and return list of datasets metadata.
    Args:
        search_term: The term to search for.
        max_results: Maximum number of results to return.
    """

    kaggle_api = auth_kaggle()
    
    # Search for datasets
    datasets = kaggle_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 = kaggle_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']
        })
    return results

@tool
def download_kaggle_dataset(
    dataset_ref: str,
    download_path: str,
    unzip: bool = True
) -> str:
    """Download a dataset from Kaggle.
    Args:
        dataset_ref: The reference of the dataset (e.g., "username/dataset-name").
        download_path: The directory where the dataset will be downloaded.
        unzip: Whether to unzip the dataset after downloading. Default is True.
    """
    # Ensure the download path exists
    os.makedirs(download_path, exist_ok=True)

    kaggle_api = auth_kaggle()

    # Download the dataset
    kaggle_api.dataset_download_files(dataset_ref, path=download_path, unzip=unzip)

    return f"Dataset '{dataset_ref}' downloaded to '{download_path}'."

final_answer = FinalAnswerTool()
user_input = UserInputTool()

# 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)

conversional_agent = ToolCallingAgent(
    model=model,
    tools=[user_input],
    max_steps=6,
    name='ask_question',
    description='Ask a question to the user and get the answer',
)
    
agent = CodeAgent(
    model=model,
    tools=[final_answer,
           search_kaggle_datasets, 
           user_input,
           download_kaggle_dataset,
           image_generation_tool],
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=2,
    name=None,
    description=None,
    managed_agents=[conversional_agent],
    prompt_templates=prompt_templates,
    additional_authorized_imports=['pandas', 
                                   'matplotlib', 
                                   'seaborn'],
    add_base_tools=True,
)


GradioUI(agent).launch()