DepremOCR (PaddleOCR optimized inference pipeline)

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.gitignore

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README.md

@@ -1,12 +1,12 @@
 ---
-title: Deprem OCR
+title: Deprem Ocr 2
 emoji: 👀
 colorFrom: green
 colorTo: blue
 sdk: gradio
 sdk_version: 3.17.0
 app_file: app.py
-pinned: true
+pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,81 +1,153 @@
-from PIL import ImageFilter, Image
-from easyocr import Reader
 import gradio as gr
-import numpy as np
+from deprem_ocr.ocr import DepremOCR
+import json
+import csv
 import openai
 import ast
-from transformers import pipeline
 import os
+import cv2
+import numpy as np
+from deta import Deta
 
-from openai_api import OpenAI_API
-import utils
 
-openai.api_key = os.getenv("API_KEY")
-reader = Reader(["tr"])
+######################
+import requests
+import json
 
+import os
+import openai
 
-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)
+
+class OpenAI_API:
+    def __init__(self):
+        self.openai_api_key = ""
+
+    def single_request(self, address_text):
+
+        openai.api_type = "azure"
+        openai.api_base = "https://damlaopenai.openai.azure.com/"
+        openai.api_version = "2022-12-01"
+        openai.api_key = os.getenv("API_KEY")
+
+        response = openai.Completion.create(
+            engine="Davinci-003",
+            prompt=address_text,
+            temperature=0.9,
+            max_tokens=256,
+            top_p=1.0,
+            n=1,
+            logprobs=0,
+            echo=False,
+            stop=None,
+            frequency_penalty=0,
+            presence_penalty=0,
+            best_of=1,
+        )
+
+        return response
+
+
+########################
+
+openai.api_key = os.getenv("API_KEY")
+depremOCR = DepremOCR()
 
 
-# Submit button
 def get_parsed_address(input_img):
 
     address_full_text = get_text(input_img)
-    return ner_response(address_full_text)
+    return openai_response(address_full_text)
 
 
-def save_deta_db(input):
-    eval_result = ast.literal_eval(input)
-    utils.write_db(eval_result)
-    return
+def preprocess_img(inp_image):
+    gray = cv2.cvtColor(inp_image, cv2.COLOR_BGR2GRAY)
+    gray_img = cv2.bitwise_not(gray)
+    return gray_img
 
 
-def update_component():
-    return gr.update(value="Gönderildi, teşekkürler.", visible=True)
+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 clear_textbox(value):
-    return gr.update(value="")
+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["il"]),
-        str(eval_result["ilce"]),
-        str(eval_result["mahalle"]),
-        str(eval_result["sokak"]),
-        str(eval_result["Apartman/site"]),
+        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"]),
-        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 = {}
+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:
+        """
 
-    for item in predictions:
-        print(item)
-        key = item["entity_group"]
-        resp[key] = item["word"]
-    
+    openai_client = OpenAI_API()
+    response = openai_client.single_request(ocr_input)
+    resp = response["choices"][0]["text"]
+    print(resp)
+    resp = eval(resp.replace("'{", "{").replace("}'", "}"))
     resp["input"] = ocr_input
-    dict_keys = ["il", "ilce", "mahalle", "sokak", "Apartman/site", "no", "ad-soyad", "dis kapi no"]
+    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
 
 
-# User Interface
 with gr.Blocks() as demo:
     gr.Markdown(
         """
@@ -86,68 +158,42 @@ with gr.Blocks() as demo:
         "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")
-
+        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():
-            il = gr.Textbox(label="İl", interactive=True, show_progress=False)
-            ilce = gr.Textbox(label="İlçe", interactive=True, show_progress=False)
+            city = gr.Textbox(label="İl")
+            distinct = gr.Textbox(label="İlçe")
         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
-            )
+            neighbourhood = gr.Textbox(label="Mahalle")
+            street = gr.Textbox(label="Sokak/Cadde/Bulvar")
         with gr.Row():
-            no = gr.Textbox(label="Telefon", interactive=True, show_progress=False)
+            tel = gr.Textbox(label="Telefon")
         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)
+            name_surname = gr.Textbox(label="İsim Soyisim")
+            address = gr.Textbox(label="Adres")
         with gr.Row():
-            dis_kapi_no = gr.Textbox(label="Kapı No", interactive=True, show_progress=False)
+            no = gr.Textbox(label="Kapı No")
 
-    img_area_button.click(
+    submit_button.click(
         get_parsed_address,
         inputs=img_area,
         outputs=open_api_text,
-        api_name="upload-image",
+        api_name="upload_image",
     )
 
-    text_area_button.click(
-        ner_response, text_area, open_api_text, api_name="upload-text"
+    ocr_result.change(
+        openai_response, ocr_result, 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",
+        [city, distinct, neighbourhood, street, address, tel, name_surname, no],
     )
-    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()
+    demo.launch()

db_utils.py

@@ -1,41 +0,0 @@
-from deta import Deta  # Import Deta
-from pprint import pprint
-import os
-
-deta_key = os.getenv("DETA_KEY")
-deta = Deta(deta_key)
-db = deta.Base("deprem-ocr")
-
-
-def get_users_by_city(city_name, limit=10):
-
-    user = db.fetch({"city": city_name.capitalize()}, limit=limit).items
-    return user
-
-
-def get_all():
-    res = db.fetch()
-    all_items = res.items
-
-    # fetch until last is 'None'
-    while res.last:
-        res = db.fetch(last=res.last)
-        all_items += res.items
-    return all_items
-
-
-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)
-    print("Pushed to db")
-
-
-def get_latest_row(last):
-    all_items = get_all()
-    latest_items = all_items[-last:]
-    return latest_items

openai_api.py

@@ -1,31 +0,0 @@
-import openai
-import os
-
-
-class OpenAI_API:
-    def __init__(self):
-        self.openai_api_key = ""
-
-    def single_request(self, address_text):
-
-        openai.api_type = "azure"
-        openai.api_base = "https://afet-org.openai.azure.com/"
-        openai.api_version = "2022-12-01"
-        openai.api_key = os.getenv("API_KEY")
-
-        response = openai.Completion.create(
-            engine="afet-org",
-            prompt=address_text,
-            temperature=0.0,
-            max_tokens=500,
-            top_p=1,
-            # n=1,
-            # logprobs=0,
-            # echo=False,
-            stop=["\n"],
-            frequency_penalty=0,
-            presence_penalty=0,
-            # best_of=1,
-        )
-
-        return response

requirements.txt

@@ -1,6 +1,14 @@
+paddlepaddle
+opencv-python
+Pillow
+numpy==1.23.3
+pandas
+imutils
+Cython
+imgaug
+pyclipper
+deprem_ocr
 openai
 Pillow
-easyocr
 gradio
-deta
-transformers
+deta

utils.py

@@ -1,53 +0,0 @@
-import cv2
-import csv
-import json
-from deta import Deta
-import os
-import requests
-
-
-def preprocess_img(inp_image):
-    gray = cv2.cvtColor(inp_image, cv2.COLOR_BGR2GRAY)
-    gray_img = cv2.bitwise_not(gray)
-    return gray_img
-
-
-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 ner_response(ocr_input):
-    API_URL = "https://api-inference.huggingface.co/models/deprem-ml/deprem-ner"
-    headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
-
-    def query(payload):
-        response = requests.post(API_URL, headers=headers, json=payload)
-        return response.json()
-
-    output = query(
-        {
-            "inputs": ocr_input,
-        }
-    )
-    return output