split_files_to_excel.py

import numpy as np
import io
import os
import zipfile
import logging
import collections
import tempfile
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
import gradio as gr

from langchain.document_loaders import PDFMinerPDFasHTMLLoader
from bs4 import BeautifulSoup
import re
from langchain.docstore.document import Document

import unstructured
from unstructured.partition.docx import partition_docx
from unstructured.partition.auto import partition

from transformers import AutoTokenizer

from pypdf import PdfReader

import pandas as pd

import requests
import json

MODEL = "thenlper/gte-base"
CHUNK_SIZE = 1500
CHUNK_OVERLAP = 400

embeddings = HuggingFaceEmbeddings(
    model_name=MODEL,
    cache_folder=os.getenv("SENTENCE_TRANSFORMERS_HOME")
)



model_id = "mistralai/Mistral-7B-Instruct-v0.1"
access_token = os.getenv("HUGGINGFACE_SPLITFILES_API_KEY")

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    padding_side="left",
    token = access_token
)

text_splitter = CharacterTextSplitter(
    separator = "\n",
    chunk_size = CHUNK_SIZE,
    chunk_overlap  = CHUNK_OVERLAP,
    length_function = len,
)


# def update_label(label1):
#     return gr.update(choices=list(df.columns))

def function_split_call(fi_input, dropdown, choice, chunk_size):
    if choice == "Intelligent split":
        return split_in_df(fi_input)
    elif choice == "Non intelligent split":
        return non_intelligent_split(fi_input, chunk_size)
    else:
        return split_by_keywords(fi_input,dropdown)

def change_textbox(dropdown,radio):
    if len(dropdown) == 0 :
        dropdown = ["introduction", "objective", "summary", "conclusion"]
    if radio == "Intelligent split by keywords":
        return gr.Dropdown(dropdown, multiselect=True, visible=True, allow_custom_value=True), gr.Number(visible=False)
    elif radio == "Non intelligent split":
        return gr.Dropdown(dropdown, visible=False),gr.Number(label="Chunk size", value=1000, interactive=True, visible=True)
    else:
        return gr.Dropdown(dropdown, visible=False),gr.Number(visible=False)


def group_text_by_font_size(content):
    cur_fs = []
    cur_text = ''
    cur_page = -1
    cur_c = content[0]
    multi_fs = False
    snippets = []   # first collect all snippets that have the same font size
    for c in content:
        # print(f"c={c}\n\n")
        if c.find('a') != None and c.find('a').get('name'):
            cur_page = int(c.find('a').get('name'))
        sp_list = c.find_all('span')
        if not sp_list:
            continue
        for sp in sp_list:
            # print(f"sp={sp}\n\n")
            if not sp:
                continue
            st = sp.get('style')
            if not st:
                continue
            fs = re.findall('font-size:(\d+)px',st)
            # print(f"fs={fs}\n\n")
            if not fs:
                continue
            fs = [int(fs[0])]
            if len(cur_fs)==0:
                cur_fs = fs
            if fs == cur_fs:
                cur_text += sp.text
            elif not sp.find('br') and cur_c==c:
                cur_text += sp.text
                cur_fs.extend(fs)
                multi_fs = True
            elif sp.find('br') and multi_fs == True: # if a br tag is found and the text is in a different fs, it is the last part of the multifontsize line
                cur_fs.extend(fs)
                snippets.append((cur_text+sp.text,max(cur_fs), cur_page))
                cur_fs = []
                cur_text = ''
                cur_c = c
                multi_fs = False
            else:
                snippets.append((cur_text,max(cur_fs), cur_page))
                cur_fs = fs
                cur_text = sp.text
                cur_c = c
                multi_fs = False
    snippets.append((cur_text,max(cur_fs), cur_page))
    return snippets

def get_titles_fs(fs_list):
    filtered_fs_list = [item[0] for item in fs_list if item[0] > fs_list[0][0]]
    return sorted(filtered_fs_list, reverse=True)

def calculate_total_characters(snippets):
    font_sizes = {}  #dictionary to store font-size and total characters

    for text, font_size, _ in snippets:
        #remove newline# and digits
        cleaned_text = text.replace('\n', '')
        #cleaned_text = re.sub(r'\d+', '', cleaned_text)
        total_characters = len(cleaned_text)

        #update the dictionary
        if font_size in font_sizes:
            font_sizes[font_size] += total_characters
        else:
            font_sizes[font_size] = total_characters
    #convert the dictionary into a sorted list of tuples
    size_charac_list = sorted(font_sizes.items(), key=lambda x: x[1], reverse=True)

    return size_charac_list

def create_documents(source, snippets, font_sizes):
    docs = []

    titles_fs = get_titles_fs(font_sizes)

    for snippet in snippets:
        cur_fs = snippet[1]
        if cur_fs>font_sizes[0][0] and len(snippet[0])>2:
            content = min((titles_fs.index(cur_fs)+1), 3)*"#" + " " + snippet[0].replace("  ", " ")
            category = "Title"
        else:
            content = snippet[0].replace("  ", " ")
            category = "Paragraph"
        metadata={"source":source, "filename":source.split("/")[-1], "file_directory": "/".join(source.split("/")[:-1]), "file_category":"", "file_sub-cat":"", "file_sub2-cat":"", "category":category, "filetype":source.split(".")[-1], "page_number":snippet[2]}
        categories = source.split("/")
        cat_update=""
        if len(categories)>4:
            cat_update = {"file_category":categories[1], "file_sub-cat":categories[2], "file_sub2-cat":categories[3]}
        elif len(categories)>3:
            cat_update = {"file_category":categories[1], "file_sub-cat":categories[2]}
        elif len(categories)>2:
            cat_update = {"file_category":categories[1]}
        metadata.update(cat_update)
        docs.append(Document(page_content=content, metadata=metadata))
    return docs

## Group Chunks docx or pdf

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def group_chunks_by_section(chunks, min_chunk_size=512):
    filtered_chunks = [chunk for chunk in chunks if chunk.metadata['category'] != 'PageBreak']# Add more filters if needed
    #print(f"filtered = {len(filtered_chunks)} - before = {len(chunks)}")
    new_chunks = []
    seen_paragraph = False
    new_title = True #switches when there is a new paragraph to create a new chunk
    for i, chunk in enumerate(filtered_chunks):
#         print(f"\n\n\n#{i}:METADATA: {chunk.metadata['category']}")
        if new_title:
            #print(f"<-- NEW title DETECTED -->")
            new_chunk = chunk
            new_title = False
            add_content = False
            new_chunk.metadata['titles'] = ""
            #print(f"CONTENT: {new_chunk.page_content}\nMETADATA: {new_chunk.metadata['category']} \n  title: {new_chunk.metadata['title']}")

        if chunk.metadata['category'].lower() =='title':
            new_chunk.metadata['titles'] += f"{chunk.page_content} ~~ "
        else:
            #Activates when a paragraph is seen after one or more titles
            seen_paragraph = True

        #Avoid adding the title 2 times to the page content
        if add_content:#and chunk.page_content not in new_chunk.page_content
            new_chunk.page_content += f"\n{chunk.page_content}"
        #edit the end_page number, the last one keeps its place
        try:
            new_chunk.metadata['end_page'] = chunk.metadata['page_number']
        except:
            print("", end="")
            #print("Exception: No page number in metadata")

        add_content = True

        #If filtered_chunks[i+1] raises an error, this is probably because this is the last chunk
        try:
            #If the next chunk is a title and we have already seen a paragraph and the current chunk content is long enough, we create a new document
            if filtered_chunks[i+1].metadata['category'].lower() =="title" and seen_paragraph and len(new_chunk.page_content)>min_chunk_size:
                if 'category' in new_chunk.metadata:
                    new_chunk.metadata.pop('category')
                new_chunks.append(new_chunk)
                new_title = True
                seen_paragraph = False
        #index out of range
        except:
            new_chunks.append(new_chunk)
            #print('🆘 Gone through all chunks 🆘')
            break
    return new_chunks

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
## Split documents by font

def split_pdf(file_path, folder):
    loader = PDFMinerPDFasHTMLLoader(file_path)

    data = loader.load()[0]   # entire pdf is loaded as a single Document
    soup = BeautifulSoup(data.page_content,'html.parser')
    content = soup.find_all('div')#List of all elements in div tags
    try:
        snippets = group_text_by_font_size(content)
    except Exception as e:
        print("ERROR WHILE GROUPING BY FONT SIZE", e)
        snippets = [("ERROR WHILE GROUPING BY FONT SIZE", 0, -1)]
    font_sizes = calculate_total_characters(snippets)#get the amount of characters for each font_size
    chunks = create_documents(file_path, snippets, font_sizes)
    return chunks

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def split_docx(file_path, folder):
    chunks_elms = partition_docx(filename=file_path)
    chunks = []
    file_categories = file_path.split("/")
    for chunk_elm in chunks_elms:
        category = chunk_elm.category
        if category == "Title":
            chunk = Document(page_content= min(chunk_elm.metadata.to_dict()['category_depth']+1, 3)*"#" + ' ' + chunk_elm.text, metadata=chunk_elm.metadata.to_dict())
        else:
            chunk = Document(page_content=chunk_elm.text, metadata=chunk_elm.metadata.to_dict())
        metadata={"source":file_path, "filename":file_path.split("/")[-1], "file_category":"", "file_sub-cat":"", "file_sub2-cat":"", "category":category, "filetype":file_path.split(".")[-1]}
        cat_update=""
        if len(file_categories)>4:
            cat_update = {"file_category":file_categories[1], "file_sub-cat":file_categories[2], "file_sub2-cat":file_categories[3]}
        elif len(file_categories)>3:
            cat_update = {"file_category":file_categories[1], "file_sub-cat":file_categories[2]}
        elif len(file_categories)>2:
            cat_update = {"file_category":file_categories[1]}
        metadata.update(cat_update)
        chunk.metadata.update(metadata)
        chunks.append(chunk)
    return chunks

# Load the index of documents (if it has already been built)

def rebuild_index(input_folder, output_folder):
    paths_time = []
    to_keep = set()
    print(f'number of files {len(paths_time)}')
    if len(output_folder.list_paths_in_partition()) > 0:
        with tempfile.TemporaryDirectory() as temp_dir:
            for f in output_folder.list_paths_in_partition():
                with output_folder.get_download_stream(f) as stream:
                    with open(os.path.join(temp_dir, os.path.basename(f)), "wb") as f2:
                        f2.write(stream.read())
            index = FAISS.load_local(temp_dir, embeddings)
            to_remove = []
            logging.info(f"{len(index.docstore._dict)} vectors loaded")
            for idx, doc in index.docstore._dict.items():
                source = (doc.metadata["source"], doc.metadata["last_modified"])
                if source in paths_time:
                    # Identify documents already indexed and still present in the source folder
                    to_keep.add(source)
                else:
                    # Identify documents removed from the source folder
                    to_remove.append(idx)

            docstore_id_to_index = {v: k for k, v in index.index_to_docstore_id.items()}

            # Remove documents that have been deleted from the source folder
            vectors_to_remove = []
            for idx in to_remove:
                del index.docstore._dict[idx]
                ind = docstore_id_to_index[idx]
                del index.index_to_docstore_id[ind]
                vectors_to_remove.append(ind)
            index.index.remove_ids(np.array(vectors_to_remove, dtype=np.int64))

            index.index_to_docstore_id = {
                i: ind
                for i, ind in enumerate(index.index_to_docstore_id.values())
            }
            logging.info(f"{len(to_remove)} vectors removed")
    else:
        index = None
    to_add = [path[0] for path in paths_time if path not in to_keep]
    print(f'to_keep: {to_keep}')
    print(f'to_add: {to_add}')
    return index, to_add

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def split_chunks_by_tokens(documents, max_length=170, overlap=10):
    # Create an empty list to store the resized documents
    resized = []

    # Iterate through the original documents list
    for doc in documents:
        encoded = tokenizer.encode(doc.page_content)
        if len(encoded) > max_length:
            remaining_encoded = tokenizer.encode(doc.page_content)
            while len(remaining_encoded) > 0:
                split_doc = Document(page_content=tokenizer.decode(remaining_encoded[:max(10, max_length)]), metadata=doc.metadata.copy())
                resized.append(split_doc)
                remaining_encoded = remaining_encoded[max(10, max_length - overlap):]

        else:
            resized.append(doc)
    print(f"Number of chunks before resplitting: {len(documents)} \nAfter splitting: {len(resized)}")
    return resized

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def split_chunks_by_tokens_period(documents, max_length=170, overlap=10, min_chunk_size=20):
    # Create an empty list to store the resized documents
    resized = []
    previous_file=""
    to_encode = ""
    skip_next = False
    # Iterate through the original documents list
    for i, doc in enumerate(documents):
        if skip_next:
            skip_next = False
            continue
        current_file = doc.metadata['source']
        if current_file != previous_file: #chunk counting
            previous_file = current_file
            chunk_counter = 0
            is_first_chunk = True  # Keep track of the first chunk in the document
        to_encode += doc.page_content
        # if last chunk < min_chunk_size we add it to the previous chunk for the splitting.
        if (documents[i+1] is documents[-1] or documents[i+1].metadata['source'] != documents[i+2].metadata['source']) and len(tokenizer.encode(documents[i+1].page_content)) < min_chunk_size: # if the next doc is the last doc of the current file or the last of the corpus 
            # print('SAME DOC')
            skip_next = True
            to_encode += documents[i+1].page_content
        #print(f"to_encode:\n{to_encode}")
        encoded = tokenizer.encode(to_encode)#encode the current document
        if len(encoded) < min_chunk_size and not skip_next:
            # print(f"len(encoded):{len(encoded)}<min_chunk_size:{min_chunk_size}")
            continue
        elif skip_next:
            split_doc = Document(page_content=tokenizer.decode(encoded).replace('<s> ', ''), metadata=doc.metadata.copy())
            split_doc.metadata['token_length'] = len(tokenizer.encode(split_doc.page_content))
            resized.append(split_doc)
            # print(f"Added a document of {split_doc.metadata['token_length']} tokens 1")
            to_encode = ""
            continue
        else:
            # print(f"len(encoded):{len(encoded)}>=min_chunk_size:{min_chunk_size}")
            to_encode = ""
        if len(encoded) > max_length:
            # print(f"len(encoded):{len(encoded)}>=max_length:{max_length}")
            remaining_encoded = encoded
            is_last_chunk = False
            while len(remaining_encoded) > 1 and not is_last_chunk:
                # Check for a period in the first 'overlap' tokens
                overlap_text = tokenizer.decode(remaining_encoded[:overlap])# Index by token
                period_index_b = overlap_text.find('.')# Index by character
                if len(remaining_encoded)>max_length + min_chunk_size:
                    # print("len(remaining_encoded)>max_length + min_chunk_size")
                    current_encoded = remaining_encoded[:max(10, max_length)]
                else:
                    # print("not len(remaining_encoded)>max_length + min_chunk_size")
                    current_encoded = remaining_encoded #if the last chunk is to small, concatenate it with the previous one
                    is_last_chunk = True
                    split_doc = Document(page_content=tokenizer.decode(current_encoded).replace('<s> ', ''), metadata=doc.metadata.copy())
                    split_doc.metadata['token_length'] = len(tokenizer.encode(split_doc.page_content))
                    resized.append(split_doc)
                    # print(f"Added a document of {split_doc.metadata['token_length']} tokens 2")
                    break
                period_index_e = -1 # an amount of character that I am sure will be greater or equal to the max lengh of a chunk, could have done len(tokenizer.decode(current_encoded))
                if len(remaining_encoded)>max_length+min_chunk_size:# If it is not the last sub chunk
                    # print("len(remaining_encoded)>max_length+min_chunk_size")
                    overlap_text_last = tokenizer.decode(current_encoded[-overlap:])
                    period_index_last = overlap_text_last.find('.')
                    if period_index_last != -1 and period_index_last < len(overlap_text_last) - 1:
                        # print(f"period index last found at {period_index_last}")
                        period_index_e = period_index_last - len(overlap_text_last)
                        # print(f"period_index_e :{period_index_e}")
                    # print(f"last :{overlap_text_last}")
                if not is_first_chunk:#starting after the period in overlap
                    # print("not is_first_chunk", period_index_b)
                    if period_index_b == -1:# Period not found in overlap
                        # print(". not found in overlap")
                        split_doc = Document(page_content=tokenizer.decode(current_encoded)[:period_index_e].replace('<s> ', ''), metadata=doc.metadata.copy()) # Keep regular splitting
                    else:
                        if is_last_chunk : #not the first but the last
                            # print("is_last_chunk")
                            split_doc = Document(page_content=tokenizer.decode(current_encoded)[period_index_b+1:].replace('<s> ', ''), metadata=doc.metadata.copy())
                        #print("Should start after \".\"")
                        else:
                            # print("not is_last_chunk", period_index_e, len(to_encode))
                            split_doc = Document(page_content=tokenizer.decode(current_encoded)[period_index_b+1:period_index_e].replace('<s> ', ''), metadata=doc.metadata.copy()) # Split at the begining and the end
                else:#first chunk
                    # print("else")
                    split_doc = Document(page_content=tokenizer.decode(current_encoded)[:period_index_e].replace('<s> ', ''), metadata=doc.metadata.copy()) # split only at the end if its first chunk
                if 'titles' in split_doc.metadata:
                    # print("title in metadata")
                    chunk_counter += 1
                    split_doc.metadata['chunk_id'] = chunk_counter
                #A1 We could round chunk length in token if we ignore the '.' position in the overlap and save time of computation
                split_doc.metadata['token_length'] = len(tokenizer.encode(split_doc.page_content))
                resized.append(split_doc)
                print(f"Added a document of {split_doc.metadata['token_length']} tokens 3")
                remaining_encoded = remaining_encoded[max(10, max_length - overlap):]
                is_first_chunk = False
                # # print(len(tokenizer.encode(split_doc.page_content)), split_doc.page_content[:50], "\n-----------------")
                # print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
                # print(split_doc.page_content[:100])
                # # print("😂😂😂😂")
                # print(split_doc.page_content[-100:])
                # print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
        else:# len(encoded)>min_chunk_size:#ignore the chunks that are too small
            print(f"found a chunk with the perfect size:{len(encoded)}")
            #print(f"◀Document:{{ {doc.page_content} }} was not added because to short▶")
            if 'titles' in doc.metadata:#check if it was splitted by or split_docx
                chunk_counter += 1
                doc.metadata['chunk_id'] = chunk_counter
            doc.metadata['token_length'] = len(encoded)
            doc.page_content = tokenizer.decode(encoded).replace('<s> ', '')
            resized.append(doc)
            print(f"Added a document of {doc.metadata['token_length']} tokens 4")
    print(f"Number of chunks before resplitting: {len(documents)} \nAfter splitting: {len(resized)}")
    return resized

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE

def split_doc_in_chunks(input_folder):
    docs = []
    for i, filename in enumerate(input_folder):
        path = filename#os.path.join(input_folder, filename)
        print(f"Treating file {i}/{len(input_folder)}")
        # Select the appropriate document loader
        chunks=[]
        if path.endswith(".pdf"):
            try:
                print("Treatment of pdf file", path)
                raw_chuncks = split_pdf(path, input_folder)
                chunks = group_chunks_by_section(raw_chuncks)
                print(f"Document splitted in {len(chunks)} chunks")
                # for chunk in chunks:
                    # print(f"\n\n____\n\n\nPDF CONTENT: \n{chunk.page_content}\ntitle: {chunk.metadata['title']}\nFile Name: {chunk.metadata['filename']}\n\n")
            except Exception as e:
                print("Error while splitting the pdf file: ", e)
        elif path.endswith(".docx"):
            try:
                print ("Treatment of docx file", path)
                raw_chuncks = split_docx(path, input_folder)
                #print(f"RAW :\n***\n{raw_chuncks}")
                chunks = group_chunks_by_section(raw_chuncks)
                print(f"Document splitted in {len(chunks)} chunks")
                #if "cards-Jan 2022-SP.docx" in path:
                    #for chunk in chunks:
                        #print(f"\n\n____\n\n\nDOCX CONTENT: \n{chunk.page_content}\ntitle: {chunk.metadata['title']}\nFile Name: {chunk.metadata['filename']}\n\n")
            except Exception as e:
                print("Error while splitting the docx file: ", e)
        elif path.endswith(".doc"):
            try:
                loader = UnstructuredFileLoader(path)
                # Load the documents and split them in chunks
                chunks = loader.load_and_split(text_splitter=text_splitter)
                counter, counter2 = collections.Counter(), collections.Counter()
                filename = os.path.basename(path)
                # Define a unique id for each chunk
                for chunk in chunks:
                    chunk.metadata["filename"] = filename.split("/")[-1]
                    chunk.metadata["file_directory"] = filename.split("/")[:-1]
                    chunk.metadata["filetype"] = filename.split(".")[-1]
                    if "page" in chunk.metadata:
                        counter[chunk.metadata['page']] += 1
                        for i in range(len(chunks)):
                            counter2[chunks[i].metadata['page']] += 1
                            chunks[i].metadata['source'] = filename
                    else:
                        if len(chunks) == 1:
                            chunks[0].metadata['source'] = filename
            #The file type is not supported (e.g. .xlsx)
            except Exception as e:
                print(f"An error occurred: {e}")
        try:
            if len(chunks)>0:
                docs += chunks
        except NameError as e:
            print(f"An error has occured: {e}")
    return docs

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def resplit_by_end_of_sentence(docs, max_len, overlap, min_len):
    print("❌❌\nResplitting docs by end of sentence\n❌❌")
    resized_docs = split_chunks_by_tokens_period(docs, max_len, overlap, min_len)
    try:
        # add chunk title to all resplitted chunks #todo move this to split_chunks_by_tokens_period(inject_title = True) with a boolean parameter
        cur_source = ""
        cpt_chunk = 1
        for resized_doc in resized_docs:
            try:
                title = resized_doc.metadata['titles'].split(' ~~ ')[-2] #Getting the last title of the chunk and adding it to the content if it is not the case
                if title not in resized_doc.page_content:
                    resized_doc.page_content = title + "\n" + resized_doc.page_content
                if cur_source == resized_doc.metadata["source"]:
                    resized_doc.metadata['chunk_number'] = cpt_chunk
                else:
                    cpt_chunk = 1
                    cur_source = resized_doc.metadata["source"]
                    resized_doc.metadata['chunk_number'] = cpt_chunk
            except Exception as e:#either the title was notfound or title absent in metadata
                print("An error occured: ", e)
                #print(f"METADATA:\n{resized_doc.metadata}")
            cpt_chunk += 1
    except Exception as e:
        print('AN ERROR OCCURRED: ', e)
    return resized_docs

# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE
def build_index(docs, index, output_folder):
    if len(docs) > 0:
        if index is not None:
             # Compute the embedding of each chunk and index these chunks
            new_index = FAISS.from_documents(docs, embeddings)
            index.merge_from(new_index)
        else:
            index = FAISS.from_documents(docs, embeddings)
    with tempfile.TemporaryDirectory() as temp_dir:
        index.save_local(temp_dir)
        for f in os.listdir(temp_dir):
            output_folder.upload_file(f, os.path.join(temp_dir, f))


def extract_zip(zip_path):
    extracted_files = []
    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        for file_info in zip_ref.infolist():
            extracted_files.append(file_info.filename)
            zip_ref.extract(file_info.filename)
    return extracted_files

def split_in_df(files):
    processed_files = []
    print("Processing zip files...")
    for file_path in files:
        if file_path.endswith('.zip'):
            extracted_files = extract_zip(file_path)
            processed_files.extend(extracted_files)
        else:
            processed_files.append(file_path)
    print("Finished processing zip files\Splitting files into chunks...")
    documents = split_doc_in_chunks(processed_files)
    re_docs = resplit_by_end_of_sentence(documents, 1000, 100, 1500)
    print("Finished splitting")
    df = pd.DataFrame()
    for re_doc in re_docs:
        filename = re_doc.metadata['filename']
        content = re_doc.page_content

        # metadata = document.metadata
        # metadata_keys = list(metadata.keys())
        # metadata_values = list(metadata.values())

        doc_data = {'Filename': filename, 'Content': content}

        doc_data["Token_Length"] = re_doc.metadata['token_length']
        doc_data["Titles"] = re_doc.metadata['titles'] if 'titles' in re_doc.metadata else ""

        # for key, value in zip(metadata_keys, metadata_values):
        #     doc_data[key] = value

        df = pd.concat([df, pd.DataFrame([doc_data])], ignore_index=True)

    df.to_excel("dataframe.xlsx", index=False)

    return "dataframe.xlsx"



# -------------------------------------------------------------------------------- SPLIT FILES BY KEYWORDS 

def split_by_keywords(files, key_words, words_limit=1000):
    processed_files = []
    extracted_content = []
    tabLine = []

    # For each files : stock the PDF, extract the Zips and convert the Doc & Docx to PDF
    try:
        not_duplicate = True
        for f in files:
            for p in processed_files:
                if (f[:f.rfind('.')] == p[:p.rfind('.')]):
                    not_duplicate = False  
            if not_duplicate: 
                if f.endswith('.zip'):
                    extracted_files = extract_zip(f)
                    print(f"Those are my extracted files{extracted_files}")
                    
                    for doc in extracted_files:
                        if doc.endswith('.doc') or doc.endswith('.docx'):
                            processed_files.append(transform_to_pdf(doc))

                        if doc.endswith('.pdf'):
                            processed_files.append(doc)

                if f.endswith('.pdf'):
                    processed_files.append(f)

                if f.endswith('.doc') or f.endswith('.docx'):
                    processed_files.append(transform_to_pdf(f))
    
    except Exception as ex:
        print(f"Error occured while processing files : {ex}")

    # For each processed files extract content
    for file in processed_files:

        try:
            file_name = file
            file = PdfReader(file)
            pdfNumberPages = len(file.pages)
            for pdfPage in range(0, pdfNumberPages):

                load_page = file.get_page(pdfPage)
                text = load_page.extract_text()
                lines = text.split("\n")
                sizeOfLines = len(lines) - 1

                for index, line in enumerate(lines):
                    print(line)
                    for key in key_words:
                        if key in line:
                            print("Found keyword")
                            lineBool = True
                            lineIndex = index
                            previousSelectedLines = []
                            stringLength = 0
                            linesForSelection = lines
                            loadOnce = True
                            selectedPdfPage = pdfPage

                            while lineBool:
                                print(lineIndex)
                                if stringLength > words_limit or lineIndex < 0:
                                    lineBool = False
                                else:
                                    if lineIndex == 0:
                                        print(f"Line index == 0")

                                        if pdfPage == 0:
                                            lineBool = False

                                        else:
                                            try:
                                                selectedPdfPage -= 1
                                                newLoad_page = file.get_page(selectedPdfPage)
                                                newText = newLoad_page.extract_text()
                                                newLines = newText.split("\n")
                                                linesForSelection = newLines
                                                print(f"len newLines{len(newLines)}")
                                                lineIndex = len(newLines) - 1
                                            except Exception as e:
                                                print(f"Loading previous PDF page failed")
                                                lineBool = False

                                    previousSelectedLines.append(linesForSelection[lineIndex])
                                    stringLength += len(linesForSelection[lineIndex])

                                    lineIndex -= 1
                            previousSelectedLines = ' '.join(previousSelectedLines[::-1])

                            lineBool = True
                            lineIndex = index + 1
                            nextSelectedLines = ""
                            linesForSelection = lines
                            loadOnce = True
                            selectedPdfPage = pdfPage

                            while lineBool:

                                if len(nextSelectedLines.split()) > words_limit:
                                    lineBool = False
                                else:
                                    if lineIndex > sizeOfLines:
                                        lineBool = False

                                        if pdfPage == pdfNumberPages - 1:
                                            lineBool = False

                                        else:
                                            try:
                                                selectedPdfPage += 1
                                                newLoad_page = file.get_page(selectedPdfPage)
                                                newText = newLoad_page.extract_text()
                                                newLines = newText.split("\n")
                                                linesForSelection = newLines
                                                lineIndex = 0
                                            except Exception as e:
                                                print(f"Loading next PDF page failed")
                                                lineBool = False
                                    else:
                                        nextSelectedLines += " " + linesForSelection[lineIndex]
                                    lineIndex += 1

                            print(f"Previous Lines : {previousSelectedLines}")
                            print(f"Next Lines : {nextSelectedLines}")
                            selectedText = previousSelectedLines + ' ' + nextSelectedLines
                            print(selectedText)
                            tabLine.append([file_name, selectedText, key])
                            print(f"Selected line in keywords is: {line}")

        except Exception as ex:
            print(f"Error occured while extracting content : {ex}")

    for r in tabLine:
        text_joined = ''.join(r[1])
        text_joined = r[2] + " : \n " + text_joined
        extracted_content.append([r[0], text_joined])

    df = pd.DataFrame()
    for content in extracted_content:
        filename = content[0]
        text = content[1]

        # metadata = document.metadata
        # metadata_keys = list(metadata.keys())
        # metadata_values = list(metadata.values())

        doc_data = {'Filename': filename[filename.rfind("/")+1:], 'Content': text}

        # for key, value in zip(metadata_keys, metadata_values):
        #     doc_data[key] = value

        df = pd.concat([df, pd.DataFrame([doc_data])], ignore_index=True)

    df.to_excel("dataframe_keywords.xlsx", index=False)

    return "dataframe_keywords.xlsx"

# -------------------------------------------------------------------------------- NON INTELLIGENT SPLIT 

def transform_to_pdf(doc):
    instructions = {'parts': [{'file': 'document'}]}

    response = requests.request(
      'POST',
      'https://api.pspdfkit.com/build',
      headers = { 'Authorization': 'Bearer pdf_live_nS6tyylSW57PNw9TIEKKL3Tt16NmLCazlQWQ9D33t0Q'},
      files = {'document': open(doc, 'rb')},
      data = {'instructions': json.dumps(instructions)},
      stream = True
    )
    
    pdf_name = doc[:doc.find(".doc")] + ".pdf"
    
    if response.ok:
      with open(pdf_name, 'wb') as fd:
        for chunk in response.iter_content(chunk_size=8096):
          fd.write(chunk)
      return pdf_name

    else:
      print(response.text)
      exit()
      return none


def non_intelligent_split(files, chunk_size = 1000):
    extracted_content = []
    processed_files = []

    
    # For each files : stock the PDF, extract the Zips and convert the Doc & Docx to PDF
    try:
        not_duplicate = True
        for f in files:
            for p in processed_files:
                if (f[:f.rfind('.')] == p[:p.rfind('.')]):
                    not_duplicate = False  
            if not_duplicate: 
                if f.endswith('.zip'):
                    extracted_files = extract_zip(f)
                    print(f"Those are my extracted files{extracted_files}")
                    
                    for doc in extracted_files:
                        if doc.endswith('.doc') or doc.endswith('.docx'):
                            processed_files.append(transform_to_pdf(doc))

                        if doc.endswith('.pdf'):
                            processed_files.append(doc)

                if f.endswith('.pdf'):
                    processed_files.append(f)

                if f.endswith('.doc') or f.endswith('.docx'):
                    processed_files.append(transform_to_pdf(f))
    
    except Exception as ex:
        print(f"Error occured while processing files : {ex}")

    # Extract content from each processed files
    try:
        for f in processed_files:
            print(f"my filename is : {f}")
            file = PdfReader(f)
            pdfNumberPages = len(file.pages)
            selectedText = ""
            
            for pdfPage in range(0, pdfNumberPages):
                load_page = file.get_page(pdfPage)
                text = load_page.extract_text()
                lines = text.split("\n")
                sizeOfLines = 0

                for index, line in enumerate(lines):
                    sizeOfLines += len(line)
                    selectedText += " " + line
                    if sizeOfLines >= chunk_size:
                        textContent = (f"Page {str(pdfPage)} : {selectedText}")
                        extracted_content.append([f, textContent])
                        sizeOfLines = 0
                        selectedText = ""

            textContent = (f"Page {str(pdfNumberPages)} : {selectedText}")
            extracted_content.append([f, textContent])
    except Exception as ex:
        print(f"Error occured while extracting content from processed files : {ex}")

    df = pd.DataFrame()
    for content in extracted_content:
        filename = content[0]
        text = content[1]
        
        doc_data = {'Filename': filename[filename.rfind("/")+1:], 'Content': text}

        df = pd.concat([df, pd.DataFrame([doc_data])], ignore_index=True)

    df.to_excel("dataframe_keywords.xlsx", index=False)

    return "dataframe_keywords.xlsx"