app.py

import gradio as gr
import pandas as pd
import pickle
    
def prediction(sequences):
    sequences = sequences.strip()
    sequences = [seq.strip() for seq in sequences.split('\n')]
    sequences = pd.DataFrame({'Sequence':sequences})
    results = pd.DataFrame(model.predict_proba(sequences))
    results.columns = ["no-AFP score", "AFP score"]
    activities = pd.DataFrame({'Antifungal':results['AFP score'] > .5})
    return pd.concat((sequences, activities, results), axis = 1)

   
model = pickle.load(open("AFP_Model.pkl", 'rb'))

with gr.Blocks() as demo:
    demo.title = "AFPtransferPred"
    inp = gr.Textbox(lines=5, max_lines=6, placeholder="Enter peptides sequences in raw format (one line per sequence)", label="")
    with gr.Row():
        btn1 = gr.Button("Submit")
        btn2 = gr.Button("Clear", elem_id="btn2")
    out = gr.DataFrame(headers = ["Sequence", "Antifungal", "no-AFP score", 'AFP-score'])
    
    btn1.click(fn=prediction, inputs=inp, outputs=out)
    btn2.click(fn=lambda:"", outputs=inp)

demo.launch(auth=("ipnacsic", "seqvence"))