monitor.py

import os
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from ocr import process_pdf, process_image, process_text, process_docx, process_xlsx, process_csv
from docx import Document
import pandas as pd

class Watcher(FileSystemEventHandler):
    def __init__(self, tagged_data):
        super().__init__()
        self.tagged_data = tagged_data

    def on_created(self, event):
        # Process different file types
        if event.src_path not in self.tagged_data:
            if event.src_path.endswith(".pdf"):
                print(f"New PDF found: {event.src_path}")
                text = process_pdf(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)
            elif event.src_path.endswith(".jpg") or event.src_path.endswith(".png"):
                print(f"New image found: {event.src_path}")
                text = process_image(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)
            elif event.src_path.endswith(".txt"):
                print(f"New text file found: {event.src_path}")
                text = process_text(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)
            elif event.src_path.endswith(".docx"):
                print(f"New Word document found: {event.src_path}")
                text = process_docx(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)
            elif event.src_path.endswith(".xlsx"):
                print(f"New Excel file found: {event.src_path}")
                text = process_xlsx(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)
            elif event.src_path.endswith(".csv"):
                print(f"New CSV file found: {event.src_path}")
                text = process_csv(event.src_path)
                tags = extract_tags(text)
                self.tagged_data[event.src_path] = tags
                print(tags)

def monitor_directory(path, tagged_data):
    observer = Observer()
    observer.schedule(Watcher(tagged_data), path=path, recursive=False)
    observer.start()
    print("Monitoring started...")

def process_existing_files(directory, tagged_data):
    """
    Function to process all existing files in the directory.
    :param directory: str: The directory path to check for existing files.
    :param tagged_data: dict: Dictionary to update with tagged data.
    """
    for filename in os.listdir(directory):
        file_path = os.path.join(directory, filename)
        if os.path.isfile(file_path) and file_path not in tagged_data:
            print(f"Processing existing file: {file_path}")
            if filename.endswith(".pdf"):
                text = process_pdf(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)
            elif filename.endswith(".jpg") or filename.endswith(".png"):
                text = process_image(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)
            elif filename.endswith(".txt"):
                text = process_text(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)
            elif filename.endswith(".docx"):
                text = process_docx(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)
            elif filename.endswith(".xlsx"):
                text = process_xlsx(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)
            elif filename.endswith(".csv"):
                text = process_csv(file_path)
                tags = extract_tags(text)
                tagged_data[file_path] = tags
                print(tags)

def extract_tags(text):
    """
    Function to extract named entities from the text using SpaCy.
    :param text: str: The extracted text from a document.
    :return: dict: Dictionary of extracted entities.
    """
    import spacy
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)
    entities = {ent.text: ent.label_ for ent in doc.ents}  # Extract entity and its label (e.g., PERSON, DATE)
    return entities

# Other functions for processing documents remain unchanged...