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 rdkit import Chem
from rdkit.Chem import AllChem, Descriptors, Draw
import io
import base64
import numpy as np
from fastapi import HTTPException

from Gradio_UI import GradioUI

import os
from huggingface_hub import login
login(token = os.getenv('bidhan_hf_agents_learning'))

# 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 get_molecule_info(smiles: str) -> dict:
    """
    Fetches molecular structure, key features, and generates a 3D conformer.

    Args:
        smiles: The SMILES string of the molecule.

    Returns:
        A dictionary containing:
        - 2D molecular structure (Base64 PNG)
        - 3D coordinates of the lowest-energy conformer
        - Molecular descriptors (MW, LogP, TPSA, H-bond donors/acceptors, rotatable bonds)
    """
    try:
        # Convert SMILES to a molecule
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            raise ValueError("Invalid SMILES string")

        # Generate 2D molecular image
        img = Draw.MolToImage(mol)
        img_buffer = io.BytesIO()
        img.save(img_buffer, format="PNG")
        img_b64 = base64.b64encode(img_buffer.getvalue()).decode()

        # Compute molecular descriptors
        mol_weight = Descriptors.MolWt(mol)
        logp = Descriptors.MolLogP(mol)
        tpsa = Descriptors.TPSA(mol)
        h_donors = Descriptors.NumHDonors(mol)
        h_acceptors = Descriptors.NumHAcceptors(mol)
        rotatable_bonds = Descriptors.NumRotatableBonds(mol)

        # Generate 3D conformer
        mol_3d = Chem.AddHs(mol)  # Add hydrogens for correct geometry
        AllChem.EmbedMolecule(mol_3d, AllChem.ETKDG())  # Generate initial 3D structure
        AllChem.UFFOptimizeMolecule(mol_3d)  # Optimize using UFF

        # Extract 3D coordinates
        conf = mol_3d.GetConformer()
        coords = [
            {
                "atom": mol_3d.GetAtomWithIdx(i).GetSymbol(),
                "x": conf.GetAtomPosition(i).x,
                "y": conf.GetAtomPosition(i).y,
                "z": conf.GetAtomPosition(i).z,
            }
            for i in range(mol_3d.GetNumAtoms())
        ]

        return {
            "image": f"data:image/png;base64,{img_b64}",
            "molecular_weight": mol_weight,
            "logP": logp,
            "TPSA": tpsa,
            "H_bond_donors": h_donors,
            "H_bond_acceptors": h_acceptors,
            "rotatable_bonds": rotatable_bonds,
            "3D_coordinates": coords
        }
    
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@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=1024,
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, get_molecule_info],  # Add the tool here
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name="Molecular Structure Fetcher",
    description="Fetch molecular structures and properties from SMILES strings",
    prompt_templates=prompt_templates
)


GradioUI(agent).launch(debug=True, share=False)