app.py

import gradio as gr
import pandas as pd
import joblib
import json
from json import encoder

encoder.FLOAT_REPR = lambda o: format(o, '.2f')
 
model = joblib.load("nb_model.pickle")

'''
def return_output(text):
    """Alternative function to output predictions as simple text"""
    output = predict(text)
    output_string = f"Device: {str(output[0])}\n\nProbability: {int(output[1])}"
    return output_string
    '''


def predict(text):

    data = pd.DataFrame({"text": [text]})
    #prediction_class = model.predict(data)[0]
    #prediction_prob = round(model.predict_proba(data).max(), 3)
    #return prediction_class, prediction_prob
    pred = model.predict_proba(data)[0]
    return {'Android': json.dumps(pred[0]), 'iPhone': json.dumps(pred[1])}


description = "According to the dataset used for this model, Trump  mainly uses two devices for tweeting" \
              " - an Android and an iPhone device.\nIt seems likely that members of his staff are tweeting on his" \
              " behalf using iPhone.\nTry and see if you can write an 'iPhone' and an 'Android' tweet."

iface = gr.Interface(fn=predict,
                     #fn=return_output,
                     inputs="text",
                     #outputs="text",
                     outputs="label",
                     allow_flagging='auto', title="iPhone or Android?",
                     interpretation="default", description=description)

iface.launch(enable_queue=True)