data_preprocess.py

import re
import os
#import streamlit as st
import subprocess
import re
from Bio import Entrez
from docx import Document
import fitz
import spacy
from spacy.cli import download
from NER.PDF import pdf
from NER.WordDoc import wordDoc
from NER.html import extractHTML
from NER.word2Vec import word2vec
#from transformers import pipeline
import urllib.parse, requests
from pathlib import Path
import pandas as pd
import model
import pipeline
import tempfile
import nltk
nltk.download('punkt_tab')
def download_excel_file(url, save_path="temp.xlsx"):
    if "view.officeapps.live.com" in url:
        parsed_url = urllib.parse.parse_qs(urllib.parse.urlparse(url).query)
        real_url = urllib.parse.unquote(parsed_url["src"][0])
        response = requests.get(real_url)
        with open(save_path, "wb") as f:
            f.write(response.content)
        return save_path
    elif url.startswith("http") and (url.endswith(".xls") or url.endswith(".xlsx")):
        response = requests.get(url)
        response.raise_for_status()  # Raises error if download fails
        with open(save_path, "wb") as f:
            f.write(response.content)
            print(len(response.content))
        return save_path
    else:
        print("URL must point directly to an .xls or .xlsx file\n or it already downloaded.")
        return url
def extract_text(link,saveFolder):
  try:
      text = ""
      name = link.split("/")[-1]
      print("name: ", name)  
      #file_path = Path(saveFolder) / name
      local_temp_path = os.path.join(tempfile.gettempdir(), name)
      print("this is local temp path: ", local_temp_path)  
      if os.path.exists(local_temp_path):
        input_to_class = local_temp_path
        print("exist")  
      else:
        #input_to_class = link  # Let the class handle downloading  
        # 1. Check if file exists in shared Google Drive folder
        file_id = pipeline.find_drive_file(name, saveFolder)
        if file_id:
            print("📥 Downloading from Google Drive...")
            pipeline.download_file_from_drive(name, saveFolder, local_temp_path)
        else:
            print("🌐 Downloading from web link...")
            response = requests.get(link)
            with open(local_temp_path, 'wb') as f:
                f.write(response.content)
            print("✅ Saved locally.")
    
            # 2. Upload to Drive so it's available for later
            pipeline.upload_file_to_drive(local_temp_path, name, saveFolder)
    
        input_to_class = local_temp_path
        print(input_to_class)  
      # pipeline.download_file_from_drive(name, saveFolder, local_temp_path)  
      # pdf
      if link.endswith(".pdf"):
        # if file_path.is_file():
        #   link = saveFolder + "/" + name
        #   print("File exists.")
        #p = pdf.PDF(local_temp_path, saveFolder)
        print("inside pdf and input to class: ", input_to_class)  
        print("save folder in extract text: ", saveFolder)  
        p = pdf.PDF(input_to_class, saveFolder)  
        #p = pdf.PDF(link,saveFolder)
        #text = p.extractTextWithPDFReader()
        text = p.extractText()  
        print("text from pdf:")
        print(text)  
        #text_exclude_table = p.extract_text_excluding_tables()
      # worddoc
      elif link.endswith(".doc") or link.endswith(".docx"):
        #d = wordDoc.wordDoc(local_temp_path,saveFolder)
        d = wordDoc.wordDoc(input_to_class,saveFolder)  
        text = d.extractTextByPage()
      # html
      else:  
        if link.split(".")[-1].lower() not in "xlsx":
            if "http" in link or "html" in link:
              print("html link: ", link)  
              html = extractHTML.HTML("",link)
              text = html.getListSection() # the text already clean
              print("text html: ")
              print(text)  
      # Cleanup: delete the local temp file
      if name:
          if os.path.exists(local_temp_path):
            os.remove(local_temp_path)
            print(f"🧹 Deleted local temp file: {local_temp_path}")   
      print("done extract text")        
  except:
      text = ""
  return text

def extract_table(link,saveFolder):
  try:  
      table = []
      name = link.split("/")[-1]
      #file_path = Path(saveFolder) / name
      local_temp_path = os.path.join(tempfile.gettempdir(), name)
      if os.path.exists(local_temp_path):
        input_to_class = local_temp_path
        print("exist")  
      else:
        #input_to_class = link  # Let the class handle downloading  
        # 1. Check if file exists in shared Google Drive folder
        file_id = pipeline.find_drive_file(name, saveFolder)
        if file_id:
            print("📥 Downloading from Google Drive...")
            pipeline.download_file_from_drive(name, saveFolder, local_temp_path)
        else:
            print("🌐 Downloading from web link...")
            response = requests.get(link)
            with open(local_temp_path, 'wb') as f:
                f.write(response.content)
            print("✅ Saved locally.")
    
            # 2. Upload to Drive so it's available for later
            pipeline.upload_file_to_drive(local_temp_path, name, saveFolder)
    
        input_to_class = local_temp_path
        print(input_to_class)
      #pipeline.download_file_from_drive(name, saveFolder, local_temp_path)
      # pdf
      if link.endswith(".pdf"):
        # if file_path.is_file():
        #   link = saveFolder + "/" + name
        #   print("File exists.")
        #p = pdf.PDF(local_temp_path,saveFolder)
        p = pdf.PDF(input_to_class,saveFolder)  
        table = p.extractTable()
      # worddoc
      elif link.endswith(".doc") or link.endswith(".docx"):
        #d = wordDoc.wordDoc(local_temp_path,saveFolder)
        d = wordDoc.wordDoc(input_to_class,saveFolder)  
        table = d.extractTableAsList()
      # excel
      elif link.split(".")[-1].lower() in "xlsx":
        # download excel file if it not downloaded yet
        savePath = saveFolder +"/"+ link.split("/")[-1]
        excelPath = download_excel_file(link, savePath)
        try:
            #xls = pd.ExcelFile(excelPath)
            xls = pd.ExcelFile(local_temp_path)
            table_list = []
            for sheet_name in xls.sheet_names:
                df = pd.read_excel(xls, sheet_name=sheet_name)
                cleaned_table = df.fillna("").astype(str).values.tolist()
                table_list.append(cleaned_table)
            table = table_list
        except Exception as e:
            print("❌ Failed to extract tables from Excel:", e)
      # html
      elif "http" in link or "html" in link:
        html = extractHTML.HTML("",link)
        table = html.extractTable() # table is a list
      table = clean_tables_format(table)
      # Cleanup: delete the local temp file
      if os.path.exists(local_temp_path):
        os.remove(local_temp_path)
        print(f"🧹 Deleted local temp file: {local_temp_path}")
  except:
      table = []
  return table

def clean_tables_format(tables):
    """
    Ensures all tables are in consistent format: List[List[List[str]]]
    Cleans by:
    - Removing empty strings and rows
    - Converting all cells to strings
    - Handling DataFrames and list-of-lists
    """
    cleaned = []
    if tables:
      for table in tables:
          standardized = []

          # Case 1: Pandas DataFrame
          if isinstance(table, pd.DataFrame):
              table = table.fillna("").astype(str).values.tolist()

          # Case 2: List of Lists
          if isinstance(table, list) and all(isinstance(row, list) for row in table):
              for row in table:
                  filtered_row = [str(cell).strip() for cell in row if str(cell).strip()]
                  if filtered_row:
                      standardized.append(filtered_row)

          if standardized:
              cleaned.append(standardized)

    return cleaned

import json
def normalize_text_for_comparison(s: str) -> str:
    """
    Normalizes text for robust comparison by:
    1. Converting to lowercase.
    2. Replacing all types of newlines with a single consistent newline (\n).
    3. Removing extra spaces (e.g., multiple spaces, leading/trailing spaces on lines).
    4. Stripping leading/trailing whitespace from the entire string.
    """
    s = s.lower()
    s = s.replace('\r\n', '\n') # Handle Windows newlines
    s = s.replace('\r', '\n')   # Handle Mac classic newlines
    
    # Replace sequences of whitespace (including multiple newlines) with a single space
    # This might be too aggressive if you need to preserve paragraph breaks,
    # but good for exact word-sequence matching.
    s = re.sub(r'\s+', ' ', s) 
    
    return s.strip()
def merge_text_and_tables(text, tables, max_tokens=12000, keep_tables=True, tokenizer="cl100k_base", accession_id=None, isolate=None):
    """
    Merge cleaned text and table into one string for LLM input.
    - Avoids duplicating tables already in text
    - Extracts only relevant rows from large tables
    - Skips or saves oversized tables
    """
    import importlib
    json = importlib.import_module("json")

    def estimate_tokens(text_str):
        try:
            enc = tiktoken.get_encoding(tokenizer)
            return len(enc.encode(text_str))
        except:
            return len(text_str) // 4  # Fallback estimate

    def is_table_relevant(table, keywords, accession_id=None):
        flat = " ".join(" ".join(row).lower() for row in table)
        if accession_id and accession_id.lower() in flat:
            return True    
        return any(kw.lower() in flat for kw in keywords)
    preview, preview1 = "",""    
    llm_input = "## Document Text\n" + text.strip() + "\n"
    clean_text = normalize_text_for_comparison(text)

    if tables:
        for idx, table in enumerate(tables):
          keywords = ["province","district","region","village","location", "country", "region", "origin", "ancient", "modern"]
          if accession_id:  keywords += [accession_id.lower()]
          if isolate: keywords += [isolate.lower()]
          if is_table_relevant(table, keywords, accession_id):
            if len(table) > 0:
              for tab in table:
                preview = " ".join(tab) if tab else ""
                preview1 = "\n".join(tab) if tab else ""
                clean_preview = normalize_text_for_comparison(preview)
                clean_preview1 = normalize_text_for_comparison(preview1)
                if clean_preview not in clean_text:
                  if clean_preview1 not in clean_text:
                    table_str = json.dumps([tab], indent=2)
                    llm_input += f"## Table {idx+1}\n{table_str}\n"
    return llm_input.strip()

def preprocess_document(link, saveFolder, accession=None, isolate=None):
    try:
      text = extract_text(link, saveFolder)
      print("text and link")
      print(link)
      print(text)  
    except: text = ""  
    try: 
      tables = extract_table(link, saveFolder)
    except: tables = []  
    if accession: accession = accession
    if isolate: isolate = isolate
    try:
      final_input = merge_text_and_tables(text, tables, max_tokens=12000, accession_id=accession, isolate=isolate)
    except: final_input = ""
    return text, tables, final_input

def extract_sentences(text):
    sentences = re.split(r'(?<=[.!?])\s+', text)
    return [s.strip() for s in sentences if s.strip()]

def is_irrelevant_number_sequence(text):
    if re.search(r'\b[A-Z]{2,}\d+\b|\b[A-Za-z]+\s+\d+\b', text, re.IGNORECASE):
        return False
    word_count = len(re.findall(r'\b[A-Za-z]{2,}\b', text))
    number_count = len(re.findall(r'\b\d[\d\.]*\b', text))
    total_tokens = len(re.findall(r'\S+', text))
    if total_tokens > 0 and (word_count / total_tokens < 0.2) and (number_count / total_tokens > 0.5):
        return True
    elif re.fullmatch(r'(\d+(\.\d+)?\s*)+', text.strip()):
        return True
    return False

def remove_isolated_single_digits(sentence):
    tokens = sentence.split()
    filtered_tokens = []
    for token in tokens:
        if token == '0' or token == '1':
            pass
        else:
            filtered_tokens.append(token)
    return ' '.join(filtered_tokens).strip()

def get_contextual_sentences_BFS(text_content, keyword, depth=2):
    def extract_codes(sentence):
    # Match codes like 'A1YU101', 'KM1', 'MO6' — at least 2 letters + numbers
      return [code for code in re.findall(r'\b[A-Z]{2,}[0-9]+\b', sentence, re.IGNORECASE)]
    sentences = extract_sentences(text_content)
    relevant_sentences = set()
    initial_keywords = set()

    # Define a regex to capture codes like A1YU101 or KM1
    # This pattern looks for an alphanumeric sequence followed by digits at the end of the string
    code_pattern = re.compile(r'([A-Z0-9]+?)(\d+)$', re.IGNORECASE)

    # Attempt to parse the keyword into its prefix and numerical part using re.search
    keyword_match = code_pattern.search(keyword)

    keyword_prefix = None
    keyword_num = None

    if keyword_match:
        keyword_prefix = keyword_match.group(1).lower()
        keyword_num = int(keyword_match.group(2))

    for sentence in sentences:
        sentence_added = False

        # 1. Check for exact match of the keyword
        if re.search(r'\b' + re.escape(keyword) + r'\b', sentence, re.IGNORECASE):
            relevant_sentences.add(sentence.strip())
            initial_keywords.add(keyword.lower())
            sentence_added = True

        # 2. Check for range patterns (e.g., A1YU101-A1YU137)
        # The range pattern should be broad enough to capture the full code string within the range.
        range_matches = re.finditer(r'([A-Z0-9]+-\d+)', sentence, re.IGNORECASE) # More specific range pattern if needed, or rely on full code pattern below
        range_matches = re.finditer(r'([A-Z0-9]+\d+)-([A-Z0-9]+\d+)', sentence, re.IGNORECASE) # This is the more robust range pattern

        for r_match in range_matches:
            start_code_str = r_match.group(1)
            end_code_str = r_match.group(2)

            # CRITICAL FIX: Use code_pattern.search for start_match and end_match
            start_match = code_pattern.search(start_code_str)
            end_match = code_pattern.search(end_code_str)

            if keyword_prefix and keyword_num is not None and start_match and end_match:
                start_prefix = start_match.group(1).lower()
                end_prefix = end_match.group(1).lower()
                start_num = int(start_match.group(2))
                end_num = int(end_match.group(2))

                # Check if the keyword's prefix matches and its number is within the range
                if keyword_prefix == start_prefix and \
                   keyword_prefix == end_prefix and \
                   start_num <= keyword_num <= end_num:
                    relevant_sentences.add(sentence.strip())
                    initial_keywords.add(start_code_str.lower())
                    initial_keywords.add(end_code_str.lower())
                    sentence_added = True
                    break # Only need to find one matching range per sentence

        # 3. If the sentence was added due to exact match or range, add all its alphanumeric codes
        #    to initial_keywords to ensure graph traversal from related terms.
        if sentence_added:
          for word in extract_codes(sentence):
            initial_keywords.add(word.lower())


    # Build word_to_sentences mapping for all sentences
    word_to_sentences = {}
    for sent in sentences:
      codes_in_sent = set(extract_codes(sent))
      for code in codes_in_sent:
          word_to_sentences.setdefault(code.lower(), set()).add(sent.strip())


    # Build the graph
    graph = {}
    for sent in sentences:
      codes = set(extract_codes(sent))
      for word1 in codes:
          word1_lower = word1.lower()
          graph.setdefault(word1_lower, set())
          for word2 in codes:
              word2_lower = word2.lower()
              if word1_lower != word2_lower:
                  graph[word1_lower].add(word2_lower)


    # Perform BFS/graph traversal
    queue = [(k, 0) for k in initial_keywords if k in word_to_sentences]
    visited_words = set(initial_keywords)

    while queue:
        current_word, level = queue.pop(0)
        if level >= depth:
            continue

        relevant_sentences.update(word_to_sentences.get(current_word, []))

        for neighbor in graph.get(current_word, []):
            if neighbor not in visited_words:
                visited_words.add(neighbor)
                queue.append((neighbor, level + 1))

    final_sentences = set()
    for sentence in relevant_sentences:
        if not is_irrelevant_number_sequence(sentence):
            processed_sentence = remove_isolated_single_digits(sentence)
            if processed_sentence:
                final_sentences.add(processed_sentence)

    return "\n".join(sorted(list(final_sentences)))



def get_contextual_sentences_DFS(text_content, keyword, depth=2):
    sentences = extract_sentences(text_content)

    # Build word-to-sentences mapping
    word_to_sentences = {}
    for sent in sentences:
        words_in_sent = set(re.findall(r'\b[A-Za-z0-9\-_\/]+\b', sent))
        for word in words_in_sent:
            word_to_sentences.setdefault(word.lower(), set()).add(sent.strip())

    # Function to extract codes in a sentence
    def extract_codes(sentence):
      # Only codes like 'KSK1', 'MG272794', not pure numbers
      return [code for code in re.findall(r'\b[A-Z]{2,}[0-9]+\b', sentence, re.IGNORECASE)]

    # DFS with priority based on distance to keyword and early stop if country found
    def dfs_traverse(current_word, current_depth, max_depth, visited_words, collected_sentences, parent_sentence=None):
        country = "unknown"
        if current_depth > max_depth:
            return country, False

        if current_word not in word_to_sentences:
            return country, False

        for sentence in word_to_sentences[current_word]:
            if sentence == parent_sentence:
                continue  # avoid reusing the same sentence

            collected_sentences.add(sentence)

            #print("current_word:", current_word)
            small_sen = extract_context(sentence, current_word, int(len(sentence) / 4))
            #print(small_sen)
            country = model.get_country_from_text(small_sen)
            #print("small context country:", country)
            if country.lower() != "unknown":
                return country, True
            else:
                country = model.get_country_from_text(sentence)
                #print("full sentence country:", country)
                if country.lower() != "unknown":
                    return country, True

            codes_in_sentence = extract_codes(sentence)
            idx = next((i for i, code in enumerate(codes_in_sentence) if code.lower() == current_word.lower()), None)
            if idx is None:
                continue

            sorted_children = sorted(
                [code for code in codes_in_sentence if code.lower() not in visited_words],
                key=lambda x: (abs(codes_in_sentence.index(x) - idx),
                               0 if codes_in_sentence.index(x) > idx else 1)
            )

            #print("sorted_children:", sorted_children)
            for child in sorted_children:
                child_lower = child.lower()
                if child_lower not in visited_words:
                    visited_words.add(child_lower)
                    country, should_stop = dfs_traverse(
                        child_lower, current_depth + 1, max_depth,
                        visited_words, collected_sentences, parent_sentence=sentence
                    )
                    if should_stop:
                        return country, True

        return country, False

    # Begin DFS
    collected_sentences = set()
    visited_words = set([keyword.lower()])
    country, status = dfs_traverse(keyword.lower(), 0, depth, visited_words, collected_sentences)

    # Filter irrelevant sentences
    final_sentences = set()
    for sentence in collected_sentences:
        if not is_irrelevant_number_sequence(sentence):
            processed = remove_isolated_single_digits(sentence)
            if processed:
                final_sentences.add(processed)
    if not final_sentences:
      return country, text_content
    return country, "\n".join(sorted(list(final_sentences)))

# Helper function for normalizing text for overlap comparison
def normalize_for_overlap(s: str) -> str:
    s = re.sub(r'[^a-zA-Z0-9\s]', ' ', s).lower()
    s = re.sub(r'\s+', ' ', s).strip()
    return s

def merge_texts_skipping_overlap(text1: str, text2: str) -> str:
    if not text1: return text2
    if not text2: return text1

    # Case 1: text2 is fully contained in text1 or vice-versa
    if text2 in text1:
        return text1
    if text1 in text2:
        return text2

    # --- Option 1: Original behavior (suffix of text1, prefix of text2) ---
    # This is what your function was primarily designed for.
    # It looks for the overlap at the "junction" of text1 and text2.
    
    max_junction_overlap = 0
    for i in range(min(len(text1), len(text2)), 0, -1):
        suffix1 = text1[-i:]
        prefix2 = text2[:i]
        # Prioritize exact match, then normalized match
        if suffix1 == prefix2:
            max_junction_overlap = i
            break
        elif normalize_for_overlap(suffix1) == normalize_for_overlap(prefix2):
            max_junction_overlap = i
            break # Take the first (longest) normalized match

    if max_junction_overlap > 0:
        merged_text = text1 + text2[max_junction_overlap:]
        return re.sub(r'\s+', ' ', merged_text).strip()

    # --- Option 2: Longest Common Prefix (for cases like "Hi, I am Vy.") ---
    # This addresses your specific test case where the overlap is at the very beginning of both strings.
    # This is often used when trying to deduplicate content that shares a common start.

    longest_common_prefix_len = 0
    min_len = min(len(text1), len(text2))
    for i in range(min_len):
        if text1[i] == text2[i]:
            longest_common_prefix_len = i + 1
        else:
            break
    
    # If a common prefix is found AND it's a significant portion (e.g., more than a few chars)
    # AND the remaining parts are distinct, then apply this merge.
    # This is a heuristic and might need fine-tuning.
    if longest_common_prefix_len > 0 and \
       text1[longest_common_prefix_len:].strip() and \
       text2[longest_common_prefix_len:].strip():

        # Only merge this way if the remaining parts are not empty (i.e., not exact duplicates)
        # For "Hi, I am Vy. Nice to meet you." and "Hi, I am Vy. Goodbye Vy."
        # common prefix is "Hi, I am Vy."
        # Remaining text1: " Nice to meet you."
        # Remaining text2: " Goodbye Vy."
        # So we merge common_prefix + remaining_text1 + remaining_text2
        
        common_prefix_str = text1[:longest_common_prefix_len]
        remainder_text1 = text1[longest_common_prefix_len:]
        remainder_text2 = text2[longest_common_prefix_len:]
        
        merged_text = common_prefix_str + remainder_text1 + remainder_text2
        return re.sub(r'\s+', ' ', merged_text).strip()


    # If neither specific overlap type is found, just concatenate
    merged_text = text1 + text2
    return re.sub(r'\s+', ' ', merged_text).strip()

from docx import Document
from pipeline import upload_file_to_drive    
# def save_text_to_docx(text_content: str, file_path: str):
#     """
#     Saves a given text string into a .docx file.

#     Args:
#         text_content (str): The text string to save.
#         file_path (str): The full path including the filename where the .docx file will be saved.
#                          Example: '/content/drive/MyDrive/CollectData/Examples/test/SEA_1234/merged_document.docx'
#     """
#     try:
#         document = Document()

#         # Add the entire text as a single paragraph, or split by newlines for multiple paragraphs
#         for paragraph_text in text_content.split('\n'):
#             document.add_paragraph(paragraph_text)

#         document.save(file_path)
#         print(f"Text successfully saved to '{file_path}'")
#     except Exception as e:
#         print(f"Error saving text to docx file: {e}") 
# def save_text_to_docx(text_content: str, filename: str, drive_folder_id: str):
#     """
#     Saves a given text string into a .docx file locally, then uploads to Google Drive.

#     Args:
#         text_content (str): The text string to save.
#         filename (str): The target .docx file name, e.g. 'BRU18_merged_document.docx'.
#         drive_folder_id (str): Google Drive folder ID where to upload the file.
#     """
#     try:
#         # ✅ Save to temporary local path first
#         print("file name: ", filename)
#         print("length text content: ", len(text_content))
#         local_path = os.path.join(tempfile.gettempdir(), filename)
#         document = Document()
#         for paragraph_text in text_content.split('\n'):
#             document.add_paragraph(paragraph_text)
#         document.save(local_path)
#         print(f"✅ Text saved locally to: {local_path}")

#         # ✅ Upload to Drive
#         pipeline.upload_file_to_drive(local_path, filename, drive_folder_id)
#         print(f"✅ Uploaded '{filename}' to Google Drive folder ID: {drive_folder_id}")

#     except Exception as e:
#         print(f"❌ Error saving or uploading DOCX: {e}")
def save_text_to_docx(text_content: str, full_local_path: str):
    document = Document()
    for paragraph_text in text_content.split('\n'):
        document.add_paragraph(paragraph_text)
    document.save(full_local_path)
    print(f"✅ Saved DOCX locally: {full_local_path}")



'''2 scenerios:
- quick look then found then deepdive and directly get location then stop
- quick look then found then deepdive but not find location then hold the related words then 
look another files iteratively for each related word and find location and stop'''
def extract_context(text, keyword, window=500):
    # firstly try accession number
    code_pattern = re.compile(r'([A-Z0-9]+?)(\d+)$', re.IGNORECASE)

    # Attempt to parse the keyword into its prefix and numerical part using re.search
    keyword_match = code_pattern.search(keyword)

    keyword_prefix = None
    keyword_num = None

    if keyword_match:
        keyword_prefix = keyword_match.group(1).lower()
        keyword_num = int(keyword_match.group(2))
    text = text.lower()    
    idx = text.find(keyword.lower())
    if idx == -1:
      if keyword_prefix:
        idx = text.find(keyword_prefix)
      if idx == -1:
        return "Sample ID not found."
      return text[max(0, idx-window): idx+window]  
    return text[max(0, idx-window): idx+window]
def process_inputToken(filePaths, saveLinkFolder,accession=None, isolate=None):
  cache = {}
  country = "unknown"
  output = ""
  tem_output, small_output = "",""
  keyword_appear = (False,"")
  keywords = []
  if isolate: keywords.append(isolate)
  if accession: keywords.append(accession)
  for f in filePaths:
    # scenerio 1: direct location: truncate the context and then use qa model?
    if keywords:
      for keyword in keywords:
        text, tables, final_input = preprocess_document(f,saveLinkFolder, isolate=keyword)
        if keyword in final_input:
          context = extract_context(final_input, keyword)
          # quick look if country already in context and if yes then return
          country = model.get_country_from_text(context)
          if country != "unknown":
            return country, context, final_input
          else:
            country = model.get_country_from_text(final_input)  
            if country != "unknown":
              return country, context, final_input
            else: # might be cross-ref
              keyword_appear = (True, f)
              cache[f] = context
              small_output = merge_texts_skipping_overlap(output, context) + "\n"
              chunkBFS = get_contextual_sentences_BFS(small_output, keyword)
              countryBFS = model.get_country_from_text(chunkBFS)
              countryDFS, chunkDFS = get_contextual_sentences_DFS(output, keyword)
              output = merge_texts_skipping_overlap(output, final_input)
              if countryDFS != "unknown" and countryBFS != "unknown":
                if len(chunkDFS) <= len(chunkBFS):
                  return countryDFS, chunkDFS, output
                else:
                  return countryBFS, chunkBFS, output
              else:        
                if countryDFS != "unknown":  
                  return countryDFS, chunkDFS, output
                if countryBFS != "unknown":
                  return countryBFS, chunkBFS, output
        else:
        # scenerio 2: 
          '''cross-ref: ex: A1YU101 keyword in file 2 which includes KM1 but KM1 in file 1 
          but if we look at file 1 first then maybe we can have lookup dict which country 
          such as Thailand as the key and its re''' 
          cache[f] = final_input
          if keyword_appear[0] == True:
            for c in cache:
              if c!=keyword_appear[1]:
                if cache[c].lower() not in output.lower():
                  output = merge_texts_skipping_overlap(output, cache[c]) + "\n"
                  chunkBFS = get_contextual_sentences_BFS(output, keyword)
                  countryBFS = model.get_country_from_text(chunkBFS)
                  countryDFS, chunkDFS = get_contextual_sentences_DFS(output, keyword)
                  if countryDFS != "unknown" and countryBFS != "unknown":
                    if len(chunkDFS) <= len(chunkBFS):
                      return countryDFS, chunkDFS, output
                    else:
                      return countryBFS, chunkBFS, output
                  else:        
                    if countryDFS != "unknown":  
                      return countryDFS, chunkDFS, output
                    if countryBFS != "unknown":
                      return countryBFS, chunkBFS, output
          else:
            if cache[f].lower() not in output.lower():
              output = merge_texts_skipping_overlap(output, cache[f]) + "\n"          
  if len(output) == 0 or keyword_appear[0]==False:
    for c in cache:
      if cache[c].lower() not in output.lower():
        output = merge_texts_skipping_overlap(output, cache[c]) + "\n"
  return country, "", output