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| import gradio as gr | |
| from deprem_ocr.ocr import DepremOCR | |
| import json | |
| import csv | |
| import openai | |
| import ast | |
| import os | |
| import numpy as np | |
| from deta import Deta | |
| openai.api_key = os.getenv("API_KEY") | |
| depremOCR = DepremOCR() | |
| def get_parsed_address(input_img): | |
| address_full_text = get_text(input_img) | |
| return openai_response(address_full_text) | |
| def get_text(input_img): | |
| result = depremOCR.apply_ocr(np.array(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() | |