deprem-ocr / app.py
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from PIL import ImageFilter, Image
from easyocr import Reader
import gradio as gr
import numpy as np
import openai
import ast
from transformers import pipeline
import os
from openai_api import OpenAI_API
import utils
openai.api_key = os.getenv("API_KEY")
reader = Reader(["tr"])
def get_text(input_img):
img = Image.fromarray(input_img)
detailed = np.asarray(img.filter(ImageFilter.DETAIL))
result = reader.readtext(detailed, detail=0, paragraph=True)
return " ".join(result)
# Submit button
def get_parsed_address(input_img):
address_full_text = get_text(input_img)
return ner_response(address_full_text)
def save_deta_db(input):
eval_result = ast.literal_eval(input)
utils.write_db(eval_result)
return
def update_component():
return gr.update(value="Gönderildi, teşekkürler.", visible=True)
def clear_textbox(value):
return gr.update(value="")
def text_dict(input):
eval_result = ast.literal_eval(input)
return (
str(eval_result["il"]),
str(eval_result["ilce"]),
str(eval_result["mahalle"]),
str(eval_result["sokak"]),
str(eval_result["Apartman/site"]),
str(eval_result["no"]),
str(eval_result["ad-soyad"]),
str(eval_result["dis kapi no"]),
)
def ner_response(ocr_input):
ner_pipe = pipeline("token-classification","deprem-ml/deprem-ner", aggregation_strategy="first")
predictions = ner_pipe(ocr_input)
resp = {}
for item in predictions:
print(item)
key = item["entity_group"]
resp[key] = item["word"]
resp["input"] = ocr_input
dict_keys = ["il", "ilce", "mahalle", "sokak", "Apartman/site", "no", "ad-soyad", "dis kapi no"]
for key in dict_keys:
if key not in resp.keys():
resp[key] = ""
return resp
# User Interface
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():
with gr.Column():
img_area = gr.Image(label="Ekran Görüntüsü yükleyin 👇")
img_area_button = gr.Button(value="Görüntüyü İşle", label="Submit")
with gr.Column():
text_area = gr.Textbox(label="Metin yükleyin 👇 ", lines=8)
text_area_button = gr.Button(value="Metni Yükle", label="Submit")
open_api_text = gr.Textbox(label="Tam Adres")
with gr.Column():
with gr.Row():
il = gr.Textbox(label="İl", interactive=True, show_progress=False)
ilce = gr.Textbox(label="İlçe", interactive=True, show_progress=False)
with gr.Row():
mahalle = gr.Textbox(
label="Mahalle", interactive=True, show_progress=False
)
sokak = gr.Textbox(
label="Sokak/Cadde/Bulvar", interactive=True, show_progress=False
)
with gr.Row():
no = gr.Textbox(label="Telefon", interactive=True, show_progress=False)
with gr.Row():
ad_soyad = gr.Textbox(
label="İsim Soyisim", interactive=True, show_progress=False
)
apartman = gr.Textbox(label="apartman", interactive=True, show_progress=False)
with gr.Row():
dis_kapi_no = gr.Textbox(label="Kapı No", interactive=True, show_progress=False)
img_area_button.click(
get_parsed_address,
inputs=img_area,
outputs=open_api_text,
api_name="upload-image",
)
text_area_button.click(
ner_response, text_area, open_api_text, api_name="upload-text"
)
open_api_text.change(
text_dict,
open_api_text,
[il, ilce, mahalle, sokak, no, apartman, ad_soyad, dis_kapi_no],
)
ocr_button = gr.Button(value="Sadece OCR kullan")
ocr_button.click(
get_text,
inputs=img_area,
outputs=text_area,
api_name="get-ocr-output",
)
submit_button = gr.Button(value="Veriyi Birimlere Yolla")
submit_button.click(save_deta_db, open_api_text)
done_text = gr.Textbox(label="Done", value="Not Done", visible=False)
submit_button.click(update_component, outputs=done_text)
for txt in [il, ilce, mahalle, sokak, apartman, no, ad_soyad, dis_kapi_no]:
submit_button.click(fn=clear_textbox, inputs=txt, outputs=txt)
if __name__ == "__main__":
demo.launch()