ds_erp_ai/utils.py

from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import Docx2txtLoader
from langchain_groq import ChatGroq
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from uuid import uuid4
from langchain_core.documents  import Document
from text_extractor import TextExtractor
import os
from concurrent.futures import ThreadPoolExecutor
import math
import json
from groq import Groq
import re
import time
import shutil
import numpy as np
from pydub import AudioSegment
import base64
import requests
from moviepy.editor import VideoFileClip
import ffmpeg
from dotenv import load_dotenv
load_dotenv()

# OpenAI API Key
api_key = os.getenv('OPENAI_API_KEY')
# setting up groq api key
os.environ["GROQ_API_KEY"] = os.getenv('GROQ_API_KEY')
client = Groq(api_key = os.getenv('GROQ_API_KEY'))
model = 'whisper-large-v3'


# chat set up
GROQ_LLM = ChatGroq(temperature=0, model_name="llama3-8b-8192", max_tokens=100)

# ----------------------------------------------------------------------------------------------------
# loading the embedding model
def load_embedding_model():
    model_name = "BAAI/bge-small-en"
    model_kwargs = {"device": "cuda"} #can also be cpu
    encode_kwargs = {"normalize_embeddings": True}
    embeddings = HuggingFaceBgeEmbeddings(
                model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
            )
    return embeddings

# ----------------------------------------------------------------------------------------------------
# loading the embedding model
embeddings = load_embedding_model()


# --------------------------------------------------------TEXT PREPROCESSING--------------------------------------------
def create_documents(doc, file_type='text'):
    text = doc[0].page_content
    metadata = doc[0].metadata
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=10,
        length_function=len,
        is_separator_regex=False,
    )
    docs = text_splitter.create_documents([text])
    # converting the text into documents
    documents = []
    for i, chunk in enumerate(docs):
        # Increment page number based on the chunk index
        doc_metadata = metadata.copy()
        doc_metadata['page'] = i  # Assign page number based on chunk index
        doc_metadata['file_type'] = file_type
        document = Document(page_content=chunk.page_content, metadata=doc_metadata)
        documents.append(document)
    return documents


def load_txt_document(document_path):
    try:
        txt_doc = TextLoader(document_path)
        text = txt_doc.load()
        # implementig document splitting
        docs = create_documents(text)
        return docs
    except:
        raise ValueError(f"Error loading -- {document_path}")
  
  
def load_docx_document(document_path):
    try:
        docx_doc = Docx2txtLoader(document_path)
        text = docx_doc.load()
        # implementig document splitting
        docs = create_documents(text)
        return docs
    except:
        raise ValueError(f"Error loading -- {document_path}")

    
# creating a function that checks the document type and loads the document
def load_pdf_document(document_path):
    try:
        pdf_doc = PyPDFLoader(document_path)
        pages = pdf_doc.load_and_split()
        return pages
    except:
        raise ValueError(f"Error loading -- {document_path}")
    

# A general function that loads textual documents
def load_document(document_path):
    if document_path.endswith(".pdf"):
        return load_pdf_document(document_path)
    elif document_path.endswith(".txt"):
        return load_txt_document(document_path)
    elif document_path.endswith(".docx"):
        return load_docx_document(document_path)
    else:
        raise ValueError(f"Unsupported document type for {document_path}")

# ----------------------------------------------------IMAGE PROCESSING------------------------------------------------
# Function to encode the image
def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

# Vision API to process the image
def process_image(image_path):
    global api_key

    # Getting the base64 string
    base64_image = encode_image(image_path)

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }

    try:
        payload = {
            "model": "gpt-4o-mini",
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": "What’s in this image?"
                        },
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{base64_image}"
                            }
                        }
                    ]
                }
            ],
            "max_tokens": 300
        }

        response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
        # returning the content of the response
        response = response.json()['choices'][0]['message']['content']
    except Exception as e:
        response = "Image not good enough for processing"

    return response


# create image document
def create_image_document(image_path, file_type='image'):
    # getting the image name from the image path
    image_name = image_path.split('/')[-1].split('.')[0]
    # setting image name as metadata
    metadata = {'filename': image_name, 'file_type': file_type}
    text_extractor = TextExtractor()
    text = text_extractor.read_text_from_image(image_path)
    # removing special characters and line breaks
    text = ''.join(e for e in text if e.isalnum() or e.isspace() or e == '\n')
    
    # if the text is empty, then we will process the image with OpenAI vision model
    if text == '':
        text = process_image(image_path)
        
    # checking if there's no value error or something, we will only return the text if there isnt any error
    if text != "Image not good enough for processing":
        # creating a document from the text
        doc = Document(page_content=text, metadata=metadata)
        # returning the document
        return [doc]
    else:
        pass # if there's an error, we will return None

# -----------------------------------------------AUDIO PROCESSING-----------------------------------------------------
# Audio to Text
def audio_to_text(filepath):
    with open(filepath, "rb") as file:
        translation = client.audio.translations.create(
            file=(filepath, file.read()),
            model="whisper-large-v3",
        )
    return translation.text


def split_audio_by_duration(audio_file_path, chunk_duration_minutes, print_output=True):
    # Convert chunk duration to milliseconds
    chunk_length_ms = chunk_duration_minutes * 60 * 1000

    # Load audio file
    audio = AudioSegment.from_file(audio_file_path)
    audio_duration_ms = len(audio)

    # Create a temporary directory for storing chunks
    base_filename = os.path.basename(audio_file_path).split('.')[0]
    chunk_folder = f"{base_filename}_chunks"
    if not os.path.exists(chunk_folder):
        os.makedirs(chunk_folder)

    chunk_paths = []

    if audio_duration_ms > chunk_length_ms:
        # Calculate the number of chunks, using math.ceil to ensure rounding up
        num_chunks = math.ceil(audio_duration_ms / chunk_length_ms)

        for i in range(int(num_chunks)):
            start_ms = i * chunk_length_ms
            end_ms = min(start_ms + chunk_length_ms, audio_duration_ms)
            chunk = audio[start_ms:end_ms]
            chunk_filename = f"{chunk_folder}/{base_filename}_chunk{i+1}.mp3"
            chunk.export(chunk_filename, format="mp3")
            chunk_paths.append(chunk_filename)
            if print_output:
                print(f'Exporting {chunk_filename}')
    else:
        # If audio duration is less than the chunk duration, store the whole file as a single chunk
        chunk_filename = f"{chunk_folder}/{base_filename}_chunk1.mp3"
        audio.export(chunk_filename, format="mp3")
        chunk_paths.append(chunk_filename)
        if print_output:
            print(f'Exporting {chunk_filename}')

    return chunk_folder, chunk_paths

def transcribe_audio_chunks(audio_file_path, chunk_duration_minutes, file_type='audio'):
    # Split the audio file into chunks
    chunk_folder, chunk_paths = split_audio_by_duration(audio_file_path, chunk_duration_minutes)

    documents = []
    for chunk_path in chunk_paths:
        # Transcribe the chunk
        transcript = audio_to_text(chunk_path)  # Assuming this function exists

        # Extract the base filename and chunk index using regex
        chunk_filename = os.path.basename(chunk_path)
        match = re.search(r'(.*)_chunk(\d+)\.mp3$', chunk_filename)
        if match:
            base_filename = match.group(1)
            chunk_index = int(match.group(2))
        else:
            # Default values in case of unexpected filename format
            base_filename = os.path.splitext(chunk_filename)[0]
            chunk_index = 1  # Assuming it's the first chunk

        # Calculate the chunk's start and end times in minutes
        start_min = (chunk_index - 1) * chunk_duration_minutes
        end_min = chunk_index * chunk_duration_minutes
        actual_end_min = min(end_min, (len(AudioSegment.from_file(audio_file_path)) // 60000))  # To handle the last chunk's actual duration

        # Create a document with the transcript and metadata
        metadata = {
                "filename": base_filename,
                "duration": f"{start_min}-{end_min} minutes", 
                "file_type": file_type,
            }
        document = Document(page_content=transcript, metadata=metadata)
        documents.append(document)

    # Delete the chunk folder after processing
    shutil.rmtree(chunk_folder)

    return documents



# creating a function to create audio document
def create_audio_document(audio_file_path, chunk_duration_minutes=3, file_type='audio'):
    documents = transcribe_audio_chunks(audio_file_path, chunk_duration_minutes, file_type)
    return documents

# ------------------------------------------------VIDEO PROCESSING-----------------------------------------------------
def preprocess_video_data(video_path: str, time_interval: int):
    
    # Load the video file
    video = VideoFileClip(video_path)
    
    # Get the duration of the video
    duration = video.duration
    
    # create an audio version of the video
    audio_path = video_path.replace('.mp4', '.mp3')
    _ = video.audio.write_audiofile(audio_path)
    
    # creating a snapshot of the videos at the time interval
    # Extract the video filename without extension
    video_name = os.path.splitext(os.path.basename(video_path))[0]

    # Create a directory for snapshots using the video name
    snapshot_dir = os.path.join(os.path.dirname(video_path), f"{video_name}")
    os.makedirs(snapshot_dir, exist_ok=True)

    # Get the duration of the video using ffmpeg
    probe = ffmpeg.probe(video_path)
    duration = float(probe['format']['duration'])

    # Loop through the video and take snapshots at 0s, 3min, 6min, etc.
    for i in range(0, int(duration), time_interval):
        start_time = i
        end_time = min(i + time_interval, int(duration))
        
        # Format the interval as 'start-end'
        interval_str = f"{start_time}-{end_time}"
        
        # Save the snapshot as an image file in the created folder
        frame_img = os.path.join(snapshot_dir, f"{interval_str}s.png")
        
        # Extract the frame using ffmpeg
        (
            ffmpeg
            .input(video_path, ss=start_time)
            .output(frame_img, vframes=1)
            .run()
        )

    print(f"Snapshots saved in {snapshot_dir}.")
    
    
    # now creating document from the audio file
    documents = create_audio_document(audio_path, chunk_duration_minutes=0.5, file_type='video')
    
    # deleting the audio file
    os.remove(audio_path)
    return documents


#----------------------------------------------------DOC SUMMARIZER --------------------------------------------------
def doc_summarizer(document_page: list) -> str:
    initiator_prompt = PromptTemplate(
        template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
        Create a short summary of the document based on the provided text. 
        
        Start with: This document is about...
        
        <|eot_id|><|start_header_id|>user<|end_header_id|>
        DOCUMENT: {document_page} \n
        
        <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
        input_variables=["document_page"],
    )

    initiator_router = initiator_prompt | GROQ_LLM | StrOutputParser()
    output = initiator_router.invoke({"document_page":document_page})
    return output


#-----------------------------------------------------OTHERS--------------------------------------------------------------

def save_embedded_data(embeddings, key="data"):
  embeddings.save_local(f"index/faiss_index_{key}")
  print("Embeddings saved")

def load_embedded_data(embeddings=embeddings, key="data"):
  embed_db = FAISS.load_local(f"index/faiss_index_{key}", embeddings, allow_dangerous_deserialization=True)
  return embed_db

#-----------------------------------------------------Data Loading Process----------------------------------------------------

# creating a function to load all documents from a directory. 
def process_document(path, extension, text_doc, image_doc, audio_doc, video_doc):
    doc_name = os.path.basename(path).split('.')[0]
    
    process_map = {
        "text": load_document,
        "image": create_image_document,
        "audio": create_audio_document,
        "video": preprocess_video_data
    }
    
    if extension in text_doc:
        doc = process_map["text"](path)
        num_pages = len(doc)
    elif extension in image_doc:
        doc = process_map["image"](path)
        num_pages = 1
        doc_name = doc[0].metadata['filename']
    elif extension in audio_doc:
        doc = process_map["audio"](path)
        num_pages = len(doc)
        doc_name = doc[0].metadata['filename']
    elif extension in video_doc:
        doc = process_map["video"](path, time_interval=30)
        num_pages = len(doc)
        doc_name = doc[0].metadata['filename']
    else:
        return None, None, None  # Unhandled extension
    
    print(f"Document {doc_name} loaded")
    return doc, doc_name, num_pages

def load_documents_from_directory(directory_path: str):
    text_doc = ['pdf', 'txt', 'docx', 'doc', 'md']
    image_doc = ['jpg', 'jpeg', 'png', 'gif', 'bmp']
    audio_doc = ['mp3', 'wav', 'flac', 'ogg', 'm4a']
    video_doc = ['mp4', 'avi', 'mkv', 'flv', 'mov']
    
    files = os.listdir(directory_path)
    documents = []
    doc_names = []
    num_pages = []
    doc_summary = []
    
    def process_with_delay(file):
        result = process_document(os.path.join(directory_path, file), file.split('.')[-1], text_doc, image_doc, audio_doc, video_doc)
        time.sleep(0.1)  # Introduce a 0.1s delay between processing each document
        return result
    
    with ThreadPoolExecutor() as executor:
        results = executor.map(process_with_delay, files)
    
    for doc, doc_name, pages in results:
        if doc is not None:
            documents.append(doc)
            doc_names.append(doc_name)
            num_pages.append(pages)
            
            # creating doc summary
            first_page = doc[0].page_content
            summary = doc_summarizer(first_page)
            doc_summary.append(summary)
    
    docs_id = [uuid4().hex for _ in range(len(documents))]
    
    json_file = os.path.join(directory_path, 'data.json')
    data = {'doc_names': doc_names, 'docs_id': docs_id, 'num_pages': num_pages, 'doc_summaary': doc_summary}
    
    if os.path.exists(json_file):
        with open(json_file, 'r+') as f:
            existing_data = json.load(f)
            existing_data.update(data)
            f.seek(0)
            json.dump(existing_data, f)
    else:
        with open(json_file, 'w') as f:
            json.dump(data, f)
    
    return documents, docs_id, num_pages


# A function to create vector store
def create_vector_store(documents: list, docs_id: list, num_pages: list):
    # index set up with the embedding dimension
    index = faiss.IndexFlatL2(384)
    # Initialize the FAISS vector store
    vector_store = FAISS(
        embedding_function=embeddings,
        index=index,
        docstore=InMemoryDocstore(),
        index_to_docstore_id={},
    )
    # Now adding other documents to the store. 
    for i in range(len(documents)):
        doc_id = docs_id[i]
        page_ids = [doc_id+ str(i) for i in range(num_pages[i])]
        vector_store.add_documents(documents=documents[i], ids=page_ids)
        
    # saving the vector store automatically
    save_embedded_data(vector_store, key="data")
    
    return vector_store

# creating a function to add documents to the vector store
def add_documents_to_vector_store(embeddings, documents: list, docs_id: list, num_pages: list):
    # loading the vector store
    vector_store = load_embedded_data(embeddings)
    for i in range(len(documents)):
        doc_id = docs_id[i]
        page_ids = [doc_id+ str(i) for i in range(num_pages[i])]
        vector_store.add_documents(documents=documents[i], ids=page_ids)
    print ("Documents added to the vector store")
    

# A document search function
def search(query, k=20):
    # loading the embedded data
    embed_db = load_embedded_data()
    db = embed_db
    docs = db.similarity_search(query, k)
    all = []
    info = []
    for doc in docs:
        # all.append({doc.page_content})
        info.append(dict(doc.metadata))
    return info