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 Gradio_UI import GradioUI

@tool
def qsartoolbox_calculator_tool(smiles:str, calculatorGUID: str)-> str: #it's import to specify the return type
    """A tool that utilises the qsar toolbox calculator
    Args:
        smiles: a smiles formula
        calculatorGUID: a calculator guid to use 
    """
    
    return 4

@tool
def qsartoolbox_qsar_model_tool(smiles:str, qsarModelGUID: str)-> str: #it's import to specify the return type
    """This machine learning model serves as an interface for the QSAR Toolbox API, 
    allowing users to input SMILES strings and select a QSAR model to generate predictive values for various properties.
    
    Args:
        smiles: a smiles formula
        qsarModelGUID: a qsar model guid to use 
    """
    
    return 4

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,qsartoolbox_qsar_model_tool], ## 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()