Merge branch 'seperate_utils'

app.py

@@ -1,114 +1,46 @@
-import gradio as gr
 from easyocr import Reader
-from PIL import Image
-import io
-import json
-import csv
+import gradio as gr
 import openai
 import ast
 import os
-from deta import Deta
-
 
-######################
-import requests
-import json
+from openai_api import OpenAI_API
+import utils
 
-import os
-import openai
 
-
-
-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=300,
-          top_p=1.0,
-          # n=1,
-          # logprobs=0,
-          # echo=False,
-          # stop=None,
-          frequency_penalty=0,
-          presence_penalty=0,
-          stop=["\n"],
-          best_of=1)
-
-        return response
-
-########################
-
-openai.api_key = os.getenv('API_KEY')
+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)
+# Submit button
+def get_parsed_address(input_img):
 
-    # 3) create and use as many DBs as you want!
-    users = deta.Base("deprem-ocr")
-    users.insert(data_dict)
+    address_full_text = get_text(input_img)
+    return openai_response(address_full_text)
 
 
+# Open API on change
 def text_dict(input):
     eval_result = ast.literal_eval(input)
-    write_db(eval_result)
+    utils.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']),
+        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 
@@ -129,19 +61,19 @@ def openai_response(ocr_input):
     resp = eval(resp.replace("'{", "{").replace("}'", "}"))
     resp["input"] = ocr_input
     dict_keys = [
-    'city',
-    'distinct',
-    'neighbourhood',
-    'street',
-    'no',
-    'tel',
-    'name_surname',
-    'address',
-    'input',
+        "city",
+        "distinct",
+        "neighbourhood",
+        "street",
+        "no",
+        "tel",
+        "name_surname",
+        "address",
+        "input",
     ]
     for key in dict_keys:
         if key not in resp.keys():
-            resp[key] = ''
+            resp[key] = ""
     return resp
 
 def ner_response(ocr_input):
@@ -157,12 +89,16 @@ def ner_response(ocr_input):
     })
     return output
 
+# 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.")
+    """
+    )
+    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 👇 ")
@@ -183,13 +119,23 @@ with gr.Blocks() as demo:
         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",
+    )
 
-    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")
+    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])
+    open_api_text.change(
+        text_dict,
+        open_api_text,
+        [city, distinct, neighbourhood, street, address, tel, name_surname, no],
+    )
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

openai_api.py

@@ -0,0 +1,30 @@
+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://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

utils.py

@@ -0,0 +1,35 @@
+import cv2
+import csv
+import json
+from deta import Deta
+import os
+
+
+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)