import streamlit as st
import pandas as pd
import numpy as np
from unidecode import unidecode
import tensorflow as tf 
import cloudpickle
from transformers import DistilBertTokenizerFast
import os

def load_model():
    interpreter = tf.lite.Interpreter(model_path=os.path.join("models/news_classification_hf_distilbert.tflite"))
    with open("models/news_classification_labelencoder.bin", "rb") as model_file_obj:
        label_encoder = cloudpickle.load(model_file_obj)
        
    model_checkpoint = "distilbert-base-uncased"
    tokenizer = DistilBertTokenizerFast.from_pretrained(model_checkpoint)
    return interpreter, label_encoder, tokenizer

interpreter, label_encoder, tokenizer = load_model()

def inference(text):
    tflite_pred = "Can't Predict"
    if text != "":
        tokens = tokenizer(text, max_length=80, padding="max_length", truncation=True, return_tensors="tf")
        # tflite model inference  
        interpreter.allocate_tensors()
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()[0]
        attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids']
        interpreter.set_tensor(input_details[0]["index"], attention_mask)
        interpreter.set_tensor(input_details[1]["index"], input_ids)
        interpreter.invoke()
        tflite_pred = interpreter.get_tensor(output_details["index"])[0]
        tflite_pred_argmax = np.argmax(tflite_pred)
        tflite_pred = f"{label_encoder.inverse_transform([tflite_pred_argmax])[0].upper()} ({str(np.round(tflite_pred[tflite_pred_argmax], 5))})"
    return tflite_pred


def main():
    st.title("News Headline Classification")
    lang_trained = 'TECHNOLOGY, HEALTH, WORLD, ENTERTAINMENT, SPORTS, BUSINESS, NATION, SCIENCE'
    st.write(f'Model is fine-tuned on the following categories  \n{lang_trained}')
    review = st.text_area("Enter a news headline:", "", height=100)
    if st.button("Submit"):
        result = inference(review)
        st.write(result)

if __name__ == "__main__":
    main()