import streamlit as st
import pickle
from tensorflow.keras.models import load_model
import tensorflow as tf
import pickle
import os
#import tensorflow_hub as hub
import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras .preprocessing.sequence import pad_sequences
import pickle
import json


with open('tokenizer.pkl', 'rb') as f:
    tokenizer = pickle.load(f)

with open('id2label.pkl', 'rb') as f:
    id2label = pickle.load(f)

with open('label2id.pkl', 'rb') as f:
    label2id = pickle.load(f)

    
model = load_model(r'model.h5')

def get_prediction(text):
    word_vector=tokenizer.texts_to_sequences([text])

    max_length=500
    word_vector_padded= pad_sequences(word_vector, maxlen= max_length, padding='post',
    truncating='post')

    y_pred= model.predict(word_vector_padded)
    prediction=y_pred.argmax(axis=1)[0]

    return id2label[int(prediction)]


def main():
    st.set_page_config(page_title="Spend Classification App", page_icon=":smiley:", layout="wide")
    st.title("Spend Classification App :smiley:")

    # Define pages
    #pages = ["spend classification"]

    # Add radio buttons to toggle between pages
    #page = st.sidebar.radio("Select a page", pages)


    #if page == pages[0]:
    st.header("Spend Classification")
    st.write("Enter a product description:")
    st.write("e.g. Key Features of Alisha Solid Women's Cycling Shorts Cotton Lycra Navy, Red, Navy,Specifications of Alisha Solid Women's Cycling Shorts Shorts Details Number of Contents in Sales Package Pack of 3 Fabric Cotton Lycra Type Cycling Shorts General Details Pattern Solid Ideal For Women's Fabric Care Gentle Machine Wash in Lukewarm Water, Do Not Bleach Additional Details Style Code ALTHT_3P_21 In the Box 3 shorts")
    input_string = st.text_input("")

    

    if st.button("Enter"):
        st.write("classification is:")
        pred = get_prediction(input_string)

        categories = pred.split(" >> ")
        formatted_output = []
        for i, category in enumerate(categories, 1):
            formatted_output.append(f'Hierarchy {i} classification: {category}')

        for line in formatted_output:

            st.write(line)

if __name__ == "__main__":
    main()