app.py

import tensorflow_hub as hub
import pickle
import sklearn

embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4") 

with open('./model.pck', 'rb') as f:
    model = pickle.load(f)

import gradio as gr

def convert(text):
    #Se genera el embedding del texto
    text_embed = embed([text])
    #El modelo hace su predicción
    prediction = model.predict_proba(text_embed).flatten()
    #Se devuelve el percentaje que el modelo ha predicho para cada etiqueta
    return {"ham": float(prediction[0]), "spam" : float(prediction[1])}


iface = gr.Interface(
    fn=convert, 
    inputs="text",
    outputs="label",
    examples=["I will help you win the lottery, my friend", "Please, darling, could you pick up the kids from school today?"],
    title="Ham or spam?",
    description="Copy and paste the text message you just received and we'll let you know if it is ham or spam",
)

iface.launch()