# Dependencies from flask import Flask, request, render_template, jsonify, send_file, redirect, url_for, flash, send_from_directory, session, Response from PIL import Image, ImageDraw import torch from transformers import LayoutLMv2ForTokenClassification, LayoutLMv3Tokenizer import csv import json import subprocess import os import torch import warnings from PIL import Image import sys from fastai import * from fastai.vision import * from fastai.metrics import error_rate from werkzeug.utils import secure_filename import pandas as pd from itertools import zip_longest import inspect from threading import Lock import signal import shutil from datetime import datetime import zipfile # LLM import argparse from asyncio.log import logger from Layoutlmv3_inference.ocr import prepare_batch_for_inference from Layoutlmv3_inference.inference_handler import handle import logging import os import copy import warnings warnings.filterwarnings("ignore", category=UserWarning, module='torch.serialization', lineno=1113) warnings.filterwarnings("ignore") from torch.serialization import SourceChangeWarning warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=SourceChangeWarning) # Upload Folder UPLOAD_FOLDER = 'static/temp/uploads' if not os.path.exists(UPLOAD_FOLDER): os.makedirs(UPLOAD_FOLDER) ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} app = Flask(__name__) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER app.config['SECRET_KEY'] = 'supersecretkey' # Added "temp" files cleaning for privacy and file managements. # All temporary files were moved to "output_folders" for review and recovery. # Moving of temp files were called at home page to ensure that new data were being supplied for extractor. @app.route('/', methods=['GET', 'POST']) def index(): try: # Current date and time now = datetime.now() dt_string = now.strftime("%Y%m%d_%H%M%S") # Source folders temp_folder = r'static/temp' inferenced_folder = r'static/temp/inferenced' # Destination folder path destination_folder = os.path.join('output_folders', dt_string) # Create a new folder with timestamp # Move the temp and inferenced folders to the destination folder shutil.move(temp_folder, destination_folder) shutil.move(inferenced_folder, destination_folder) return render_template('index.html', destination_folder=destination_folder) except: return render_template('index.html') def allowed_file(filename): return '.' in filename and \ filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS @app.route('/upload', methods=['GET', 'POST']) def upload_files(): UPLOAD_FOLDER = 'static/temp/uploads' if not os.path.exists(UPLOAD_FOLDER): os.makedirs(UPLOAD_FOLDER) if request.method == 'POST': if 'files[]' not in request.files: resp = jsonify({'message' : 'No file part in the request'}) resp.status_code = 400 return resp files = request.files.getlist('files[]') filenames = [] for file in files: if file and allowed_file(file.filename): filename = secure_filename(file.filename) file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) filenames.append(filename) return redirect(url_for('predict_files', filenames=filenames)) return render_template('index.html') def make_predictions(image_paths): temp = None try: # For Windows OS # temp = pathlib.PosixPath # Save the original state # pathlib.PosixPath = pathlib.WindowsPath # Change to WindowsPath temporarily model_path = Path(r'model/export') learner = load_learner(model_path) predictions = [] for image_path in image_paths: # Open the image using fastai's open_image function image = open_image(image_path) # Make a prediction prediction_class, prediction_idx, probabilities = learner.predict(image) # If you want the predicted class as a string predicted_class_str = str(prediction_class) predictions.append(predicted_class_str) return predictions except Exception as e: return {"error in make_predictions": str(e)} # finally: # pathlib.PosixPath = temp @app.route('/predict/', methods=['GET', 'POST']) def predict_files(filenames): index_url = url_for('index') prediction_results = [] image_paths = eval(filenames) # Convert the filenames string back to a list for filename in image_paths: file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) folder_path = UPLOAD_FOLDER destination_folder = r'static/temp/img_display' if not os.path.exists(destination_folder): os.makedirs(destination_folder) # Get a list of all files in the source folder files = os.listdir(folder_path) # Loop through each file and copy it to the destination folder for file in files: # Construct the full path of the source file source_file_path = os.path.join(folder_path, file) # Construct the full path of the destination file destination_file_path = os.path.join(destination_folder, file) # Copy the file to the destination folder shutil.copy(source_file_path, destination_file_path) if os.path.exists(file_path): # Call make_predictions automatically prediction_result = make_predictions([file_path]) # Pass file_path as a list prediction_results.append(prediction_result[0]) # Append only the first prediction result prediction_results_copy = copy.deepcopy(prediction_results) non_receipt_indices = [] for i, prediction in enumerate(prediction_results): if prediction == 'non-receipt': non_receipt_indices.append(i) # Delete images in reverse order to avoid index shifting for index in non_receipt_indices[::-1]: file_to_remove = os.path.join('static', 'temp', 'uploads', image_paths[index]) if os.path.exists(file_to_remove): os.remove(file_to_remove) return render_template('extractor.html', index_url=index_url, image_paths=image_paths, prediction_results = prediction_results, predictions=dict(zip(image_paths, prediction_results_copy))) @app.route('/get_inference_image') def get_inference_image(): # Assuming the new image is stored in the 'inferenced' folder with the name 'temp_inference.jpg' inferenced_image = 'static/temp/inferenced/temp_inference.jpg' return jsonify(updatedImagePath=inferenced_image), 200 # Return the image path with a 200 status code def process_images(model_path: str, images_path: str) -> None: try: image_files = os.listdir(images_path) images_path = [os.path.join(images_path, image_file) for image_file in image_files] inference_batch = prepare_batch_for_inference(images_path) context = {"model_dir": model_path} handle(inference_batch, context) except Exception as e: print("No Internet connection.") os.makedirs('log', exist_ok=True) logging.basicConfig(filename='log/error_output.log', level=logging.ERROR, format='%(asctime)s %(levelname)s %(name)s %(message)s') logger = logging.getLogger(__name__) logger.error(err) return redirect(url_for('index')) @app.route('/run_inference', methods=['GET']) def run_inference(): try: model_path = r"model" images_path = r"static/temp/uploads/" process_images(model_path, images_path) return redirect(url_for('create_csv')) except Exception as err: return f"Error processing images: {str(err)}", 500 @app.route('/stop_inference', methods=['GET']) def stop_inference(): try: # Get the process ID of the run_inference process run_inference_pid = os.getpid() # Assuming it's running in the same process # Send the SIGTERM signal to gracefully terminate the process os.kill(run_inference_pid, signal.SIGTERM) return render_template('index.html') except ProcessLookupError: logging.warning("run_inference process not found.") except Exception as err: logging.error(f"Error terminating run_inference process: {err}") # Define a function to replace all symbols with periods def replace_symbols_with_period(text): # Replace all non-alphanumeric characters with a period text = re.sub(r'\W+', '.', text) return text @app.route('/create_csv', methods=['GET']) def create_csv(): try: # Path to the folder containing JSON files json_folder_path = r"static/temp/labeled" # Change this to your folder path # Path to the output CSV folder output_folder_path = r"static/temp/inferenced/csv_files" os.makedirs(output_folder_path, exist_ok=True) column_order = [ 'RECEIPTNUMBER', 'MERCHANTNAME', 'MERCHANTADDRESS', 'TRANSACTIONDATE', 'TRANSACTIONTIME', 'ITEMS', 'PRICE', 'TOTAL', 'VATTAX' ] # Save # Iterate through JSON files in the folder for filename in os.listdir(json_folder_path): if filename.endswith(".json"): json_file_path = os.path.join(json_folder_path, filename) with open(json_file_path, 'r', encoding='utf-8') as file: data = json.load(file) all_data = data.get('output', []) # Initialize a dictionary to store labels and corresponding texts for this JSON file label_texts = {} for item in all_data: label = item['label'] text = item['text'].replace('|', '') # Strip the pipe character if label == 'VATTAX' or label == 'TOTAL': text = replace_symbols_with_period(text.replace(' ', '')) # Remove spaces and replace symbols with periods if label == 'TRANSACTIONTIME': # Concatenate all words for 'TRANSACTIONTIME' labels if label in label_texts: label_texts[label][0] += ": " + text # Add a colon and a space before the text else: label_texts[label] = [text] else: if label in label_texts: label_texts[label].append(text) else: label_texts[label] = [text] # Writing data to CSV file with ordered columns csv_file_path = os.path.join(output_folder_path, os.path.splitext(filename)[0] + '.csv') with open(csv_file_path, 'w', encoding='utf-8') as csvfile: csv_writer = csv.DictWriter(csvfile, fieldnames=column_order, delimiter=",") if os.path.getsize(csv_file_path) == 0: csv_writer.writeheader() # Constructing rows for the CSV file num_items = len(label_texts.get('ITEMS', [])) for i in range(num_items): row_data = {} for label in column_order: if label in label_texts: # Check if the label exists in the dictionary if label == 'ITEMS' or label == 'PRICE': if i < len(label_texts.get(label, [])): row_data[label] = label_texts[label][i] else: row_data[label] = '' else: row_data[label] = label_texts[label][0] else: row_data[label] = '' # If the label does not exist, set the value to an empty string csv_writer.writerow(row_data) # Combining contents of CSV files into a single CSV file output_file_path = r"static/temp/inferenced/output.csv" with open(output_file_path, 'w', newline='', encoding='utf-8') as combined_csvfile: combined_csv_writer = csv.DictWriter(combined_csvfile, fieldnames=column_order, delimiter=",") combined_csv_writer.writeheader() # Iterate through CSV files in the folder for csv_filename in os.listdir(output_folder_path): if csv_filename.endswith(".csv"): csv_file_path = os.path.join(output_folder_path, csv_filename) # Read data from CSV file and write to the combined CSV file with open(csv_file_path, 'r', encoding='utf-8') as csv_file: csv_reader = csv.DictReader(csv_file) for row in csv_reader: combined_csv_writer.writerow(row) return '', 204 # Return an empty response with a 204 status code except Exception as e: print(f"An error occurred in create_csv: {str(e)}") return render_template('extractor.html', error_message=str(e)) @app.route('/get_data') def get_data(): return send_from_directory('static/temp/inferenced','output.csv', as_attachment=False) @app.route('/download_csv', methods=['POST']) def download_csv(): try: csv_data = request.data.decode('utf-8') # Get the CSV data from the request return Response( csv_data, mimetype="text/csv", headers={"Content-disposition": "attachment; filename=output.csv"}) except Exception as e: return jsonify({"error": f"Download failed: {str(e)}"}) if __name__ == '__main__': app.run(debug=True)