app.py

import streamlit as st
import os
import json
import fitz
import re
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoModelForSequenceClassification, 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
from nltk.corpus import stopwords

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
def add_commonality_to_similarity_score(similarity, sentence, query):
    # Tokenize both the sentence and the query
    # sentence_words = set(sentence.split())
    # query_words = set(query.split())
    sentence_words = set(word for word in sentence.split() if word.lower() not in st.session_state.stop_words)
    query_words = set(word for word in query.split() if word.lower() not in st.session_state.stop_words)

    # Calculate the number of common words
    common_words = len(sentence_words.intersection(query_words))

    # Adjust the similarity score with the common words count
    combined_score = similarity + (common_words / max(len(query_words), 1))  # Normalize by the length of the query to keep the score between -1 and 1
    return combined_score,similarity,(common_words / max(len(query_words), 1))

def contradiction_detection(premise,hypothesis):
    inputs = st.session_state.roberta_tokenizer.encode_plus(premise, hypothesis, return_tensors="pt", truncation=True)

    # Get model predictions
    outputs = st.session_state.roberta_model(**inputs)

    # Get the logits (raw predictions before softmax)
    logits = outputs.logits

    # Apply softmax to get probabilities for each class
    probabilities = torch.softmax(logits, dim=1)

    # Class labels: 0 = entailment, 1 = neutral, 2 = contradiction
    predicted_class = torch.argmax(probabilities, dim=1).item()

    # Class labels
    labels = ["Contradiction", "Neutral", "Entailment"]

    # Output the result
    print(f"Prediction: {labels[predicted_class]}")
    return {labels[predicted_class]}


if 'is_initialized' not in st.session_state:
    st.session_state['is_initialized'] = True

    nltk.download('punkt')
    nltk.download('stopwords')
    # print("stop words start")
    # print(stopwords.words('english'))
    # print("stop words end")
    stop_words_list = stopwords.words('english')
    st.session_state.stop_words = set(stop_words_list)
    st.session_state.bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", )
    st.session_state.bert_model = BertModel.from_pretrained("bert-base-uncased", ).to('cuda')
    st.session_state.roberta_tokenizer = AutoTokenizer.from_pretrained("roberta-large-mnli")
    st.session_state.roberta_model = AutoModelForSequenceClassification.from_pretrained("roberta-large-mnli")
def encode_sentence(sentence):
    if len(sentence.strip()) < 4:
        return None

    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()
    return sentence_encoding

def encode_paragraph(paragraph):
    sentence_encodings = []
    paragraph_without_newline = paragraph.replace("\n", "")
    sentences = sent_tokenize(paragraph_without_newline)
    for sentence in sentences:
        # if sentence.strip().endswith('?'):
        #     sentence_encodings.append(None)
        #     continue
        sentence_encoding = encode_sentence(sentence)
        sentence_encodings.append([sentence, sentence_encoding])
    return sentence_encodings
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.append([sentence,bert_model(**sentence_tokens).last_hidden_state[:, 0, :].detach().numpy()])
            sentence_encodings=encode_paragraph(paragraph['paragraph'])
            st.session_state.paragraph_sentence_encodings.append([paragraph, sentence_encodings])
        st.rerun()

big_text = """
    <div style='text-align: center;'>
        <h1 style='font-size: 30x;'>Contradiction Dectection</h1>
    </div>
    """
    # Display the styled text
st.markdown(big_text, unsafe_allow_html=True)

def convert_pdf_to_paragraph_list(doc):
    paragraphs = []
    sentence_endings = ('.', '!', '?')
    start_page = 1

    for page_num in range(start_page - 1, len(doc)):  # start_page - 1 to adjust for 0-based index
        page = doc.load_page(page_num)
        blocks = page.get_text("blocks")

        block_index = 1
        for block in blocks:
            x0, y0, x1, y1, text, block_type, flags = block
            if text.strip() != "":
                text = text.strip()
                text = re.sub(r'\n\s+\n', '\n\n', text)
                list_pattern = re.compile(r'^\s*((?:\d+\.|[a-zA-Z]\.|[*-])\s+.+)', re.MULTILINE)
                match = list_pattern.search(text)
                containsList = False
                if match:
                    containsList = True
                    # print ("list detected")
                paragraph = ""
                if bool(re.search(r'\n{2,}', text)):
                    substrings = re.split(r'\n{2,}', text)
                    for substring in substrings:
                        if substring.strip() != "":
                            paragraph = substring
                            paragraphs.append(
                                {"paragraph": paragraph, "containsList": containsList, "page_num": page_num,
                                 "text": text});
                            # print(f"<substring> {substring} </substring>")
                else:
                    paragraph = text
                    paragraphs.append(
                        {"paragraph": paragraph, "containsList": containsList, "page_num": page_num, "text": None});
    return  paragraphs

uploaded_pdf_file = st.file_uploader("Upload a PDF file",
                                     type=['pdf'])
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)
st.markdown("sample queries for above file: <br/> What is death? What is a lucid dream? What is the seat of consciousness?",unsafe_allow_html=True)
st.markdown(
f'<a href="https://ikmtechnology.github.io/ikmtechnology/the_business_case_for_ai_extracted_paragraphs.json" target="_blank">Sample 2 download and then upload to above</a>',
    unsafe_allow_html=True)
st.markdown("sample queries for above file: <br/> what does nontechnical managers worry about? what if you put all the knowledge, frameworks, and tips from this book to full use? tell me about AI agent",unsafe_allow_html=True)
if uploaded_pdf_file is not None:
    if is_new_file_upload(uploaded_pdf_file):
        print("is new file uploaded")
        if 'prev_query' in st.session_state:
            del st.session_state['prev_query']
        if 'paragraph_sentence_encodings' in st.session_state:
            del st.session_state['paragraph_sentence_encodings']
        save_path = './uploaded_files'
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        with open(os.path.join(save_path, uploaded_pdf_file.name), "wb") as f:
            f.write(uploaded_pdf_file.getbuffer())  # Write the file to the specified location
            st.success(f'Saved file temp_{uploaded_pdf_file.name} in {save_path}')
            st.session_state.uploaded_path=os.path.join(save_path, uploaded_pdf_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)

        doc = fitz.open(st.session_state.uploaded_path)

        st.session_state.restored_paragraphs=convert_pdf_to_paragraph_list(doc)
        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}')
        st.rerun()

def find_sentences_scores(paragraph_sentence_encodings, query_encoding, processing_progress_bar,total_count):
    sentence_scores = []
    for index, paragraph_sentence_encoding in enumerate(paragraph_sentence_encodings):
        progress_percentage = index / (total_count - 1)
        processing_progress_bar.progress(progress_percentage)

        sentence_similarities = []
        for sentence_encoding in paragraph_sentence_encoding[1]:
            if sentence_encoding:
                similarity = cosine_similarity(query_encoding, sentence_encoding[1])[0][0]
                combined_score, similarity_score, commonality_score = add_commonality_to_similarity_score(similarity,
                                                                                                          sentence_encoding[0],
                                                                                                          query)
                sentence_similarities.append((combined_score, sentence_encoding[0], commonality_score))
                sentence_scores.append((combined_score, sentence_encoding[0]))

        sentence_similarities.sort(reverse=True, key=lambda x: x[0])
        # print(sentence_similarities)
        if len(sentence_similarities) >= 3:
            top_three_avg_similarity = np.mean([s[0] for s in sentence_similarities[:3]])
            top_three_avg_commonality = np.mean([s[2] for s in sentence_similarities[:3]])
            top_three_sentences = sentence_similarities[:3]
        elif sentence_similarities:
            top_three_avg_similarity = np.mean([s[0] for s in sentence_similarities])
            top_three_avg_commonality = np.mean([s[2] for s in sentence_similarities])
            top_three_sentences = sentence_similarities
        else:
            top_three_avg_similarity = 0
            top_three_avg_commonality = 0
            top_three_sentences = []
        # print(f"top_three_sentences={top_three_sentences}")
        # top_three_texts = [s[1] for s in top_three_sentences]
        # remaining_texts = [s[0] for s in paragraph_sentence_encoding[1] if s and s[0] not in top_three_texts]
        # reordered_paragraph = top_three_texts + remaining_texts
        #
        # original_paragraph = ' '.join([s[0] for s in paragraph_sentence_encoding[1] if s])
        # modified_paragraph = ' '.join(reordered_paragraph)

        paragraph_scores.append(
            (top_three_avg_similarity, top_three_avg_commonality,
             {'top_three_sentences': top_three_sentences, 'original_text': paragraph_sentence_encoding[0]})
        )

    sentence_scores = sorted(sentence_scores, key=lambda x: x[0], reverse=True)

if 'paragraph_sentence_encodings' in st.session_state:
    query = st.text_input("Enter your query")

    if query:
        if 'prev_query' not in st.session_state or st.session_state.prev_query != query:
            st.session_state.prev_query = query
            st.session_state.premise = query

            query_encoding = encode_sentence(query)
            paragraph_scores = []

            total_count = len(st.session_state.paragraph_sentence_encodings)
            processing_progress_bar = st.progress(0)



            sentence_scores = find_sentences_scores(
                st.session_state.paragraph_sentence_encodings, query_encoding, processing_progress_bar,total_count)

            st.session_state.paragraph_scores = sorted(paragraph_scores, key=lambda x: x[0], reverse=True)

        if 'paragraph_scores' in st.session_state:


            st.write("Top scored paragraphs and their scores:")
            for i, (similarity_score, commonality_score, paragraph) in enumerate(
                    st.session_state.paragraph_scores[:5]):
                #st.write("top_three_sentences: ", paragraph['top_three_sentences'])

                for top_sentence in paragraph['top_three_sentences']:
                    st.write("hyppthesis: ", top_sentence[1])
                    st.write(contradiction_detection(st.session_state.premise,top_sentence[1]))
                    #print(top_sentence[1])
                # st.write(f"Similarity Score: {similarity_score}, Commonality Score: {commonality_score}")
                # st.write("top_three_sentences: ", paragraph['top_three_sentences'])
                st.write("Original Paragraph: ", paragraph['original_text'])
                #A Member will be considered Actively at Work if he or she is able and available for active performance of all of his or her regular duties
                # st.write("Modified Paragraph: ", paragraph['modified_text'])