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| from modules.racist_text_query import racist_text_query | |
| from modules.bigotry_dict import bigotry_dict | |
| from modules.OCR import tiff_to_ocr | |
| from modules.racist_chatgpt_analysis import racist_chatgpt_analysis | |
| from modules.locate import locate | |
| from modules.pagenum import crop_image | |
| import os | |
| import pandas as pd | |
| def racism_threshold(file_dir): | |
| # Create the new folder for cropped images | |
| cropped_images_dir = os.path.join(file_dir, 'deed page number') | |
| if not os.path.exists(cropped_images_dir): | |
| os.makedirs(cropped_images_dir) | |
| data = [] | |
| for images in os.listdir(file_dir): | |
| if images.endswith(".tif") or images.endswith(".tiff"): | |
| image_path = os.path.join(file_dir, images) | |
| # run ocr on images | |
| text = tiff_to_ocr(image_path) | |
| result1 = racist_chatgpt_analysis(text) | |
| result2 = racist_text_query(text, bigotry_dict) | |
| a, b, c = locate(text) | |
| # Define the output path for the cropped image in the new folder | |
| cropped_image_name = "cropped_" + images | |
| cropped_image_path = os.path.join(cropped_images_dir, cropped_image_name) | |
| # Crop the image and save it to the new folder | |
| crop_image(image_path, cropped_image_path) | |
| image_path_formatted = cropped_image_path | |
| #.replace(' ', '%20') | |
| hyperlink_formula = f'file://{image_path_formatted}' | |
| # fail safe page number detection | |
| page = tiff_to_ocr(cropped_image_path) | |
| fail_safe_page = [] | |
| result = page.split("\n") | |
| for word in result: | |
| # checks for possible page numbers | |
| if word.isdigit() == True: | |
| fail_safe_page.append(word) | |
| if result1 or result2: | |
| print(images, a, b, c) | |
| if len(fail_safe_page) != 0: | |
| a.append(fail_safe_page) | |
| data.append([images, a, b[0], c[0], hyperlink_formula]) | |
| else: | |
| print(images + " : Not Racist") | |
| # data.append([images, a, b[0], c[0], hyperlink_formula]) | |
| # Include the hyperlink in the DataFrame columns | |
| df = pd.DataFrame(data, columns=['File Name', 'Probable Page Number', 'Date', 'Book Number', "Page Link"]) | |
| df.index += 1 | |
| df.to_csv(os.path.join(file_dir, 'Racist Deeds.csv'), index=True) | |
| df.to_excel(os.path.join(file_dir, 'Racist Deeds.xlsx'), index=True) | |
| racism_threshold('folderpath') | |