import streamlit as st
import os
import json

from transformers import GPT2Tokenizer, GPT2LMHeadModel, BertTokenizer, BertModel,T5Tokenizer, T5ForConditionalGeneration,AutoTokenizer, AutoModelForSeq2SeqLM

import torch
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import nltk
from nltk.tokenize import sent_tokenize


def is_new_file_upload(uploaded_file):
    if 'last_uploaded_file' in st.session_state:
        # Check if the newly uploaded file is different from the last one
        if (uploaded_file.name != st.session_state.last_uploaded_file['name'] or
                uploaded_file.size != st.session_state.last_uploaded_file['size']):
            st.session_state.last_uploaded_file = {'name': uploaded_file.name, 'size': uploaded_file.size}
            # st.write("A new src image file has been uploaded.")
            return True
        else:
            # st.write("The same src image file has been re-uploaded.")
            return False
    else:
        # st.write("This is the first file upload detected.")
        st.session_state.last_uploaded_file = {'name': uploaded_file.name, 'size': uploaded_file.size}
        return True
big_text = """
    <div style='text-align: center;'>
        <h1 style='font-size: 30x;'>Knowledge Extraction 1</h1>
    </div>
    """
    # Display the styled text
st.markdown(big_text, unsafe_allow_html=True)

uploaded_json_file = st.file_uploader("Upload a pre-processed file",
                                           type=['json'])
st.markdown(
    f'<a href="https://ikmtechnology.github.io/ikmtechnology/untethered_extracted_paragraphs.json" target="_blank">Sample 1 download and then upload to above</a>',
    unsafe_allow_html=True)

if uploaded_json_file is not None:
    if is_new_file_upload(uploaded_json_file):
        print("is new file uploaded")
        save_path = './uploaded_files'
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        with open(os.path.join(save_path, uploaded_json_file.name), "wb") as f:
            f.write(uploaded_json_file.getbuffer())  # Write the file to the specified location
            st.success(f'Saved file temp_{uploaded_json_file.name} in {save_path}')
            st.session_state.uploaded_path=os.path.join(save_path, uploaded_json_file.name)
            # st.session_state.page_count = utils.get_pdf_page_count(st.session_state.uploaded_pdf_path)
            # print("page_count=",st.session_state.page_count)
        content = uploaded_json_file.read()
        try:
            st.session_state.restored_paragraphs = json.loads(content)
            #print(data)
            # Check if the parsed data is a dictionary
            if isinstance(st.session_state.restored_paragraphs, list):
                # Count the restored_paragraphs of top-level elements
                st.session_state.list_count  = len(st.session_state.restored_paragraphs)
                st.write(f'The number of elements at the top level of the hierarchy: {st.session_state.list_count }')
            else:
                st.write('The JSON content is not a dictionary.')
        except json.JSONDecodeError:
            st.write('Invalid JSON file.')
        st.rerun()
if 'is_initialized' not in st.session_state:
    st.session_state['is_initialized'] = True

    nltk.download('punkt')
    st.session_state.bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", )
    st.session_state.bert_model = BertModel.from_pretrained("bert-base-uncased", ).to('cuda')

if 'list_count' in st.session_state:
    st.write(f'The number of elements at the top level of the hierarchy: {st.session_state.list_count }')
    if 'paragraph_sentence_encodings' not in st.session_state:
        print("start embedding paragarphs")
        read_progress_bar = st.progress(0)
        st.session_state.paragraph_sentence_encodings = []
        for index,paragraph in enumerate(st.session_state.restored_paragraphs):
            #print(paragraph)

            progress_percentage = (index) / (st.session_state.list_count - 1)
            print(progress_percentage)
            read_progress_bar.progress(progress_percentage)

            sentence_encodings = []
            sentences = sent_tokenize(paragraph['text'])
            for sentence in sentences:
                if sentence.strip().endswith('?'):
                    sentence_encodings.append(None)
                    continue
                if len(sentence.strip()) < 4:
                    sentence_encodings.append(None)
                    continue
                sentence_tokens = st.session_state.bert_tokenizer(sentence, return_tensors="pt", padding=True, truncation=True).to('cuda')
                with torch.no_grad():
                    sentence_encoding = st.session_state.bert_model(**sentence_tokens).last_hidden_state[:, 0, :].cpu().numpy()
                sentence_encodings.append([sentence, sentence_encoding])
                # sentence_encodings.append([sentence,bert_model(**sentence_tokens).last_hidden_state[:, 0, :].detach().numpy()])
            st.session_state.paragraph_sentence_encodings.append([paragraph, sentence_encodings])