import gradio as gr import json import csv import openai import ast import os from deta import Deta import numpy as np from ocr import utility from ocr.detector import TextDetector from ocr.recognizer import TextRecognizer # Global Detector and Recognizer args = utility.parse_args() text_recognizer = TextRecognizer(args) text_detector = TextDetector(args) openai.api_key = os.getenv("API_KEY") args = utility.parse_args() text_recognizer = TextRecognizer(args) text_detector = TextDetector(args) def apply_ocr(img): # Detect text regions dt_boxes, _ = text_detector(img) boxes = [] for box in dt_boxes: p1, p2, p3, p4 = box x1 = min(p1[0], p2[0], p3[0], p4[0]) y1 = min(p1[1], p2[1], p3[1], p4[1]) x2 = max(p1[0], p2[0], p3[0], p4[0]) y2 = max(p1[1], p2[1], p3[1], p4[1]) boxes.append([x1, y1, x2, y2]) # Recognize text img_list = [] for i in range(len(boxes)): x1, y1, x2, y2 = map(int, boxes[i]) img_list.append(img.copy()[y1:y2, x1:x2]) img_list.reverse() rec_res, _ = text_recognizer(img_list) # Postprocess total_text = "" for i in range(len(rec_res)): total_text += rec_res[i][0] + " " total_text = total_text.strip() return total_text def get_parsed_address(input_img): address_full_text = get_text(input_img) return openai_response(address_full_text) def get_text(input_img): input_img = np.array(input_img) result = apply_ocr(input_img) print(result) return " ".join(result) def save_csv(mahalle, il, sokak, apartman): adres_full = [mahalle, il, sokak, apartman] with open("adress_book.csv", "a", encoding="utf-8") as f: write = csv.writer(f) write.writerow(adres_full) return adres_full def get_json(mahalle, il, sokak, apartman): adres = {"mahalle": mahalle, "il": il, "sokak": sokak, "apartman": apartman} dump = json.dumps(adres, indent=4, ensure_ascii=False) return dump def write_db(data_dict): # 2) initialize with a project key deta_key = os.getenv("DETA_KEY") deta = Deta(deta_key) # 3) create and use as many DBs as you want! users = deta.Base("deprem-ocr") users.insert(data_dict) def text_dict(input): eval_result = ast.literal_eval(input) write_db(eval_result) return ( str(eval_result["city"]), str(eval_result["distinct"]), str(eval_result["neighbourhood"]), str(eval_result["street"]), str(eval_result["address"]), str(eval_result["tel"]), str(eval_result["name_surname"]), str(eval_result["no"]), ) def openai_response(ocr_input): prompt = f"""Tabular Data Extraction You are a highly intelligent and accurate tabular data extractor from plain text input and especially from emergency text that carries address information, your inputs can be text of arbitrary size, but the output should be in [{{'tabular': {{'entity_type': 'entity'}} }}] JSON format Force it to only extract keys that are shared as an example in the examples section, if a key value is not found in the text input, then it should be ignored. Have only city, distinct, neighbourhood, street, no, tel, name_surname, address Examples: Input: Deprem sırasında evimizde yer alan adresimiz: İstanbul, Beşiktaş, Yıldız Mahallesi, Cumhuriyet Caddesi No: 35, cep telefonu numaram 5551231256, adim Ahmet Yilmaz Output: {{'city': 'İstanbul', 'distinct': 'Beşiktaş', 'neighbourhood': 'Yıldız Mahallesi', 'street': 'Cumhuriyet Caddesi', 'no': '35', 'tel': '5551231256', 'name_surname': 'Ahmet Yılmaz', 'address': 'İstanbul, Beşiktaş, Yıldız Mahallesi, Cumhuriyet Caddesi No: 35'}} Input: {ocr_input} Output: """ response = openai.Completion.create( model="text-davinci-003", prompt=prompt, temperature=0, max_tokens=300, top_p=1, frequency_penalty=0.0, presence_penalty=0.0, stop=["\n"], ) resp = response["choices"][0]["text"] print(resp) resp = eval(resp.replace("'{", "{").replace("}'", "}")) resp["input"] = ocr_input dict_keys = [ "city", "distinct", "neighbourhood", "street", "no", "tel", "name_surname", "address", "input", ] for key in dict_keys: if key not in resp.keys(): resp[key] = "" return resp with gr.Blocks() as demo: gr.Markdown( """ # Enkaz Bildirme Uygulaması """ ) gr.Markdown( "Bu uygulamada ekran görüntüsü sürükleyip bırakarak AFAD'a enkaz bildirimi yapabilirsiniz. Mesajı metin olarak da girebilirsiniz, tam adresi ayrıştırıp döndürür. API olarak kullanmak isterseniz sayfanın en altında use via api'ya tıklayın." ) with gr.Row(): img_area = gr.Image(label="Ekran Görüntüsü yükleyin 👇") ocr_result = gr.Textbox(label="Metin yükleyin 👇 ") open_api_text = gr.Textbox(label="Tam Adres") submit_button = gr.Button(label="Yükle") with gr.Column(): with gr.Row(): city = gr.Textbox(label="İl") distinct = gr.Textbox(label="İlçe") with gr.Row(): neighbourhood = gr.Textbox(label="Mahalle") street = gr.Textbox(label="Sokak/Cadde/Bulvar") with gr.Row(): tel = gr.Textbox(label="Telefon") with gr.Row(): name_surname = gr.Textbox(label="İsim Soyisim") address = gr.Textbox(label="Adres") with gr.Row(): no = gr.Textbox(label="Kapı No") submit_button.click( get_parsed_address, inputs=img_area, outputs=open_api_text, api_name="upload_image", ) ocr_result.change( openai_response, ocr_result, open_api_text, api_name="upload-text" ) open_api_text.change( text_dict, open_api_text, [city, distinct, neighbourhood, street, address, tel, name_surname, no], ) if __name__ == "__main__": demo.launch()