app.py

import evaluate
from evaluate.utils import launch_gradio_widget
import gradio as gr

module = evaluate.load("saicharan2804/molgenevalmetric")
# launch_gradio_widget(module)

iface = gr.Interface(
    fn = module.compute,
    inputs=[
        gr.File(label="Generated SMILES"), 
        gr.File(label="Training Data", value=None),
        ],
    outputs="text"
)

iface.launch()


# import pandas as pd
# from molgenevalmetric import penalized_logp
# import evaluate

# df = pd.read_csv('/Users/saicharan/chembl_10000.csv')
# ls= df['SMILES'].tolist()
# ls_gen = ls[0:500]
# ls_train = ls[500:1000]

# print('computing')
# print(penalized_logp(gen=ls_gen))
# print(SYBAscore(gen=ls_gen))
# print(qed_metric(gen=ls_gen))
# print(logP_metric(gen=ls_gen))
# print(average_sascore(gen=ls_gen))
# print(oracles(gen=ls_gen, train=ls_train))

# met = evaluate.load("saicharan2804/molgenevalmetric")
# print(met.compute(gensmi = ls_gen, trainsmi = ls_train))