Duplicate from deprem-ml/deprem-ocr

Co-authored-by: Mert Cobanov <mertcobanov@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Deprem Ocr 2
+emoji: 👀
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 3.17.0
+app_file: app.py
+pinned: false
+duplicated_from: deprem-ml/deprem-ocr
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from easyocr import Reader
+from PIL import Image
+import io
+import json
+import csv
+import openai
+import ast
+import os
+from deta import Deta
+
+
+openai.api_key = os.getenv('API_KEY')
+reader = Reader(["tr"])
+
+
+def get_parsed_address(input_img):
+
+    address_full_text = get_text(input_img)
+    return openai_response(address_full_text)
+
+
+def preprocess_img(inp_image):
+    gray = cv2.cvtColor(inp_image, cv2.COLOR_BGR2GRAY)
+    gray_img = cv2.bitwise_not(gray)
+    return gray_img
+
+
+def get_text(input_img):
+    result = reader.readtext(input_img, detail=0)
+    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()

requirements.txt

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+openai
+Pillow
+easyocr
+gradio
+deta