app.py

import gradio as gr
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

#loading the saved model 
from keras.models import load_model 
model = load_model('Model.h5')


def page(word):
    
    num_words=3000
    
    tokenizer=Tokenizer(num_words=num_words,oov_token='oov')
    tokenizer.fit_on_texts(word)
    word_tk= tokenizer.texts_to_sequences([word])
    word_pad= pad_sequences(word_tk, maxlen=10)
    prediction = model.predict(word_pad)
    f_prediction= np.argmax(prediction)
    if f_prediction == 1:
        return "Seller is authentic, page recommended"
    elif f_prediction ==2:
       return "Seller is not authentic, page not recommended"
    elif f_prediction ==3:
       return "Seller is not authentic, page not recommended"  
    else:
       return "Error"
  

demo=gr.Interface(fn=page,
                  inputs=gr.Textbox(placeholder="Enter your review and the rating to check the authenticity of any product or page"),
                  outputs="text")

demo.launch(debug=True)