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import gradio as gr
from src.predict import predict
def update_amino_acid(safety_param, amino_acid):
if safety_param == "KIBA Score":
return gr.update(visible=True, value=amino_acid)
else:
return gr.update(visible=False)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
safety_param = gr.Dropdown(["Skin Reaction", "KIBA Score", "Liver Safety"], label="Parameter")
drug_name = gr.Textbox(label="Drug Name")
amino_acid = gr.Textbox(label="Amino Acid Name", visible=False)
with gr.Column():
safety_report = gr.Textbox(label="Drug Analysis")
analyze_drug_btn = gr.Button("Get Drug Analysis")
analyze_drug_btn.click(
fn=predict,
inputs=[safety_param, drug_name, amino_acid],
outputs=safety_report,
)
safety_param.change(
fn=update_amino_acid,
inputs=[safety_param, amino_acid],
outputs=amino_acid,
)
examples = [
["KIBA Score", "C1=CC=C(C=C1)C(=O)N", "RPDFCLEPPYTGPCKARIIRYFYNAKAGLCQTFVYGGCRAKRNNFKSAEDCMRTCGGA"],
["KIBA Score", "C1=CC=C(C=C1)C(=O)N", "Humanin"],
["Liver Safety", "benzamide", None],
["Liver Safety", "CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21", None],
["Skin Reaction", "Amoxicillin", None],
["Skin Reaction", "Atorvastatin", None]
]
gr.Examples(
examples=examples,
inputs=[safety_param, drug_name, amino_acid],
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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