src/application/text/search_detection.py

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

from src.application.text.preprocessing import split_into_sentences
from src.application.text.search import generate_search_phrases, search_by_google
from src.application.url_reader import URLReader
import numpy as np
import nltk
import torch
from nltk.corpus import stopwords
from sentence_transformers import SentenceTransformer, util
import math

from difflib import SequenceMatcher

# Download necessary NLTK data files
nltk.download('punkt', quiet=True)
nltk.download('punkt_tab', quiet=True)
nltk.download('stopwords', quiet=True)

# load the model
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
PARAPHASE_MODEL = SentenceTransformer('paraphrase-MiniLM-L6-v2')
PARAPHASE_MODEL.to(DEVICE)

BATCH_SIZE = 8

PARAPHRASE_THRESHOLD = 0.8
PARAPHRASE_THRESHOLD_FOR_OPPOSITE = 0.7
MIN_SAME_SENTENCE_LEN = 6
MIN_PHRASE_SENTENCE_LEN = 10
MIN_RATIO_PARAPHRASE_NUM = 0.7
MAX_CHAR_SIZE = 30000


def detect_text_by_relative_search(input_text, index, is_support_opposite = False):
    checked_urls = set()
    searched_phrases = generate_search_phrases(input_text[index])

    for candidate in searched_phrases:
        search_results = search_by_google(candidate)
        urls = [item['link'] for item in search_results.get("items", [])]

        for url in urls[:10]:
            if url in checked_urls: # visited url
                continue
            if "bbc.com" not in url:
                continue
                
            checked_urls.add(url)
            print(f"\t\tChecking URL: {url}")
            
            content = URLReader(url)
            
            if content.is_extracted is True:
                if content.title is None or content.text is None:
                    print(f"\t\t\t↑↑↑ Title or text not found")
                    continue
                
                page_text = content.title + "\n" + content.text
                if len(page_text) > MAX_CHAR_SIZE:
                    print(f"\t\t\t↑↑↑ More than {MAX_CHAR_SIZE} characters")
                    continue
                print(f"\t\t\t↑↑↑ Title: {content.title}") 
                paraphrase, aligned_first_sentences = check_paraphrase(input_text[index], page_text, url)
                
                if paraphrase is False:
                    return paraphrase, url, aligned_first_sentences, content.images, index
                
                sub_paraphrase = True
                while sub_paraphrase == True:
                    index += 1
                    print(f"----search {index} < {len(input_text)}----")
                    if index >= len(input_text):
                        print(f"input_text_last: {input_text[-1]}")
                        break
                    print(f"input_text: {input_text[index]}")
                    sub_paraphrase, sub_sentences = check_paraphrase(input_text[index], page_text, url)
                    print(f"sub_paraphrase: {sub_paraphrase}")
                    print(f"sub_sentences: {sub_sentences}")
                    if sub_paraphrase == True:
                        aligned_first_sentences["input_sentence"] += "<br>" + sub_sentences["input_sentence"]
                        aligned_first_sentences["matched_sentence"] += "<br>" + sub_sentences["matched_sentence"]
                        aligned_first_sentences["similarity"] += sub_sentences["similarity"]
                        aligned_first_sentences["similarity"] /= 2
                
                print(f"paraphrase: {paraphrase}")
                print(f"aligned_first_sentences: {aligned_first_sentences}")
                return paraphrase, url, aligned_first_sentences, content.images, index
                
    return False, None, [], [], index

def longest_common_subsequence(arr1, arr2):
    """
    Finds the length of the longest common subsequence (contiguous) between
        two arrays.

    Args:
        arr1: The first array.
        arr2: The second array.

    Returns:
        The length of the longest common subsequence. 
        Returns 0 if either input is invalid.
    """

    if not isinstance(arr1, list) or not isinstance(arr2, list):
        return 0

    n = len(arr1)
    m = len(arr2)

    if n == 0 or m == 0: #handle empty list
        return 0

    # Create table dp with size (n+1) x (m+1)
    dp = [[0] * (m + 1) for _ in range(n + 1)]
    max_length = 0

    for i in range(1, n + 1):
        for j in range(1, m + 1):
            if arr1[i - 1] == arr2[j - 1]:
                dp[i][j] = dp[i - 1][j - 1] + 1
                max_length = max(max_length, dp[i][j])
            else:
                dp[i][j] = 0  # set 0 since the array must be consecutive

    return max_length


def check_sentence(input_sentence, source_sentence, min_same_sentence_len,
                   min_phrase_sentence_len, verbose=False):
    """
    Checks if two sentences are similar based on exact match or 
        longest common subsequence.

    Args:
        input_sentence: The input sentence.
        source_sentence: The source sentence.
        min_same_sentence_len: Minimum length for exact sentence match.
        min_phrase_sentence_len: Minimum length for common subsequence match.
        verbose: If True, print debug information.

    Returns:
        True if the sentences are considered similar, False otherwise.
        Returns False if input is not valid.
    """

    if not isinstance(input_sentence, str) or not isinstance(source_sentence, str):
        return False

    input_sentence = input_sentence.strip()
    source_sentence = source_sentence.strip()

    if not input_sentence or not source_sentence:  # handle empty string
        return False

    input_words = input_sentence.split()  # split without arguments
    source_words = source_sentence.split()  # split without arguments

    if input_sentence == source_sentence and len(input_words) >= min_same_sentence_len:
        if verbose:
            print("Exact match found.")
        return True

    max_overlap_len = longest_common_subsequence(input_words, source_words)
    if verbose:
        print(f"Max overlap length: {max_overlap_len}")  # print overlap length
    if max_overlap_len >= min_phrase_sentence_len:
        return True

    return False


def check_paraphrase(input_text, page_text, url):
    """
    Checks if the input text is paraphrased in the content at the given URL.

    Args:
        input_text: The text to check for paraphrase.
        page_text: The text of the web page to compare with.
        verbose: If True, print debug information.

    Returns:
        A tuple containing:
            - is_paraphrase: True if the input text is considered a paraphrase, False otherwise.
            - paraphrase_results: A list of dictionaries, each containing:
                - input_sentence: The sentence from the input text.
                - matched_sentence: The corresponding sentence from the web page (if found).
                - similarity: The cosine similarity score between the sentences.
                - is_paraphrase_sentence: True if the individual sentence pair meets the paraphrase criteria, False otherwise.
    """
    is_paraphrase_text = False
    
    if not isinstance(input_text, str) or not isinstance(page_text, str):
        return False, []

    # Extract sentences from input text and web page
    #input_text = remove_punctuation(input_text)
    input_sentences = split_into_sentences(input_text)
    

    if not page_text:
        return is_paraphrase_text, []
    #page_text = remove_punctuation(page_text)
    page_sentences = split_into_sentences(page_text)
    if not input_sentences or not page_sentences:
        return is_paraphrase_text, []

    additional_sentences = []
    for sentence in page_sentences:
        if ", external" in sentence:
            additional_sentences.append(sentence.replace(", external", ""))
    page_sentences.extend(additional_sentences)
    
    # min_matching_sentences = math.ceil(len(input_sentences) * MIN_RATIO_PARAPHRASE_NUM)

    # Encode sentences into embeddings
    embeddings1 = PARAPHASE_MODEL.encode(input_sentences, convert_to_tensor=True, device=DEVICE)
    embeddings2 = PARAPHASE_MODEL.encode(page_sentences, convert_to_tensor=True, device=DEVICE)

    # Compute cosine similarity matrix
    similarity_matrix = util.cos_sim(embeddings1, embeddings2).cpu().numpy()

    # Find sentence alignments
    alignment = {}
    paraphrased_sentence_count = 0
    for i, sentence1 in enumerate(input_sentences):
        max_sim_index = np.argmax(similarity_matrix[i])
        max_similarity = similarity_matrix[i][max_sim_index]

        is_paraphrase_sentence = max_similarity > PARAPHRASE_THRESHOLD

        if is_paraphrase_sentence is False:
            alignment = {
                "input_sentence": sentence1,
                "matched_sentence": "",
                "similarity": max_similarity,
                "label": "",
                "paraphrase": is_paraphrase_sentence,
                "url": "",
            }
        else:
            alignment = {
                "input_sentence": sentence1,
                "matched_sentence": page_sentences[max_sim_index],
                "similarity": max_similarity,
                "label": "",
                "paraphrase": is_paraphrase_sentence,
                "url": url,
            }

        # Check for individual sentence paraphrase if overall paraphrase not yet found
        if not is_paraphrase_text and check_sentence(
            sentence1, page_sentences[max_sim_index], MIN_SAME_SENTENCE_LEN, MIN_PHRASE_SENTENCE_LEN
        ):
            is_paraphrase_text = True

        #alignment.append(item)
        paraphrased_sentence_count += 1 if is_paraphrase_sentence else 0

    # Check if enough sentences are paraphrases
    
    is_paraphrase_text = paraphrased_sentence_count > 0 #min_matching_sentences

    return is_paraphrase_text, alignment

def similarity_ratio(a, b):
    """
    Calculates the similarity ratio between two strings using SequenceMatcher.

    Args:
        a: The first string.
        b: The second string.

    Returns:
        A float representing the similarity ratio between 0.0 and 1.0.
        Returns 0.0 if either input is None or not a string.
    """
    if not isinstance(a, str) or not isinstance(b, str) or a is None or b is None:
        return 0.0  # Handle cases where inputs are not strings or None
    return SequenceMatcher(None, a, b).ratio()

def check_human(alligned_sentences):
    """
    Checks if a sufficient number of input sentences are found within
        source sentences.

    Returns:
        bool: True if the condition is met, False otherwise.
    """
    if not alligned_sentences:  # Handle empty data case
        return False

    if alligned_sentences["similarity"] >= 0.99:
        return True
    return False


if __name__ == '__main__':    
    pass