import streamlit as st
from transformers import pipeline
from textblob import TextBlob
from transformers import BertForSequenceClassification, AdamW, BertConfig
st.set_page_config(layout='wide', initial_sidebar_state='expanded')

with col1:
    st.title("Spamd: Turkish Spam Detector")
    st.markdown("Message spam detection tool for Turkish language. Due the small size of the dataset, I decided to go with transformers         technology Google BERT. Using the Turkish pre-trained model BERTurk, I imporved the accuracy of the tool by 18 percent compared to the previous model which used fastText.")

 
import torch
import numpy as np
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased")
from transformers import AutoModel
model = BertForSequenceClassification.from_pretrained("NimaKL/spamd_model")
token_id = []
attention_masks = []
def preprocessing(input_text, tokenizer):
    '''
                  Returns <class transformers.tokenization_utils_base.BatchEncoding> with the following fields:
                    - input_ids: list of token ids
                    - token_type_ids: list of token type ids
                    - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).
    '''
    return tokenizer.encode_plus(
        input_text,
        add_special_tokens = True,
        max_length = 32,
        pad_to_max_length = True,
        return_attention_mask = True,
        return_tensors = 'pt'
            )
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    with col1:
        st.success("Model Loaded!")
    def predict(new_sentence):
    # We need Token IDs and Attention Mask for inference on the new sentence
        test_ids = []
        test_attention_mask = []
        # Apply the tokenizer
        encoding = preprocessing(new_sentence, tokenizer)
        #Extract IDs and Attention Mask
        test_ids.append(encoding['input_ids'])
        test_attention_mask.append(encoding['attention_mask'])
        test_ids = torch.cat(test_ids, dim = 0)
        test_attention_mask = torch.cat(test_attention_mask, dim = 0)
        #Forward pass, calculate logit predictions
        with torch.no_grad():
            output = model(test_ids.to(device), token_type_ids = None, attention_mask = test_attention_mask.to(device))
            prediction = 'Spam' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Normal'
            pred = 'Predicted Class: '+ prediction
                return pred      
            if text or aButton:
                st.text_input("Enter the text you'd like to analyze for spam.")
                st.button('Analyze') 
                with col2:
                    st.header(predict(text))