Update app.py

app.py

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+import os
+import cv2
+import json
+import easyocr
+import datasets
+import socket
+import requests
+import keras_ocr
+
+import numpy as np
+import gradio as gr
+import pandas as pd
+import tensorflow as tf
+import re as r
+
+from PIL import Image
+from datasets import Image
+from datetime import datetime
+from paddleocr import PaddleOCR
+from urllib.request import urlopen
+from huggingface_hub import Repository, upload_file
+
+"""
+Paddle OCR
+"""
+def ocr_with_paddle(img):
+    finaltext = ''
+    ocr = PaddleOCR(lang='en', use_angle_cls=True)
+    # img_path = 'exp.jpeg'
+    result = ocr.ocr(img)
+    
+    for i in range(len(result[0])):
+        text = result[0][i][1][0]
+        finaltext += ' '+ text
+    return finaltext
+
+"""
+Keras OCR
+"""
+def ocr_with_keras(img):
+    output_text = ''
+    pipeline=keras_ocr.pipeline.Pipeline()
+    images=[keras_ocr.tools.read(img)]
+    predictions=pipeline.recognize(images)
+    first=predictions[0]
+    for text,box in first:
+        output_text += ' '+ text
+    return output_text
+
+"""
+easy OCR
+"""
+# gray scale image
+def get_grayscale(image):
+    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# Thresholding or Binarization
+def thresholding(src):
+    return cv2.threshold(src,127,255, cv2.THRESH_TOZERO)[1]
+    
+def ocr_with_easy(img):
+    gray_scale_image=get_grayscale(img)
+    thresholding(gray_scale_image)
+    cv2.imwrite('image.png',gray_scale_image)
+    reader = easyocr.Reader(['th','en'])
+    bounds = reader.readtext('image.png',paragraph="False",detail = 0)
+    bounds = ''.join(bounds)
+    return bounds
+
+"""
+Generate OCR
+"""
+def generate_ocr(Method,input_image):
+    text_output = ''
+    if (input_image).any():
+        print("Method___________________",Method)
+        if Method == 'EasyOCR':
+            text_output = ocr_with_easy(input_image)
+        if Method == 'KerasOCR':
+            text_output = ocr_with_keras(input_image)
+        if Method == 'PaddleOCR':
+            text_output = ocr_with_paddle(input_image)
+        
+        flag(Method,input_image,text_output,ip_address,location)
+        return text_output
+    else:
+        raise gr.Error("Please upload an image!!!!")
+
+image = gr.Image(shape=(300, 300))
+method = gr.Radio(["PaddleOCR","EasyOCR", "KerasOCR"],value="PaddleOCR",elem_id="radio_div")
+output = gr.Textbox(label="Output",elem_id="opbox")
+
+demo = gr.Interface(
+    generate_ocr,
+    [method,image],
+    output,
+    title="Optical Character Recognition",
+    css=".gradio-container {background-color: #C0E1F2} #radio_div {background-color: #ADA5EC; font-size: 40px;} #btn {background-color: #94D68B; font-size: 20px;} #opbox {background-color: #ADA5EC;}",
+    article="""<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> and contact us at 
+                    <a href="mailto:[email protected]" target="_blank">[email protected]</a> And don't forget to check out more interesting 
+                    <a href="https://www.pragnakalp.com/services/natural-language-processing-services/" target="_blank">NLP services</a> we are offering.</p>
+                    <p style='text-align: center;'>Developed by :<a href="https://www.pragnakalp.com" target="_blank"> Pragnakalp Techlabs</a></p>"""
+    
+)
+demo.launch()
+
+HF_TOKEN = os.environ.get("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")
+DATASET_NAME = "ocr-image-to-text"
+
+DATASET_REPO_URL = f"https://huggingface.co/datasets/Mo41/{DATASET_NAME}"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+DATASET_REPO_ID = "Mo41/ocr-image-to-text"
+print("is none?", HF_TOKEN is None)
+REPOSITORY_DIR = "data"
+LOCAL_DIR = 'data_local'
+os.makedirs(LOCAL_DIR,exist_ok=True)
+
+repo = Repository(
+    local_dir="ocr_data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+)
+repo.git_pull()
+
+
+"""
+Save generated details
+"""
+def dump_json(thing,file):
+    with open(file,'w+',encoding="utf8") as f:
+        json.dump(thing,f)
+
+def flag(Method,input_image,text_output,ip_address,location):
+    try:
+        print("saving data------------------------")
+        adversarial_number = 0
+        adversarial_number = 0 if None else adversarial_number
+        metadata_name = datetime.now().strftime('%Y-%m-%d %H-%M-%S')
+        SAVE_FILE_DIR = os.path.join(LOCAL_DIR,metadata_name)
+        os.makedirs(SAVE_FILE_DIR,exist_ok=True)
+        image_output_filename = os.path.join(SAVE_FILE_DIR,'image.png')
+        try:
+            Image.fromarray(input_image).save(image_output_filename)
+            
+        except Exception:
+            raise Exception(f"Had issues saving PIL image to file")
+        # Write metadata.json to file
+        json_file_path = os.path.join(SAVE_FILE_DIR,'metadata.jsonl')
+        metadata= {'id':metadata_name,'method':Method,
+                   'File_name':'image.png','generated_text':text_output,
+                    'ip_address': ip_address,'loc': location}        
+        dump_json(metadata,json_file_path)  
+            
+        # Simply upload the image file and metadata using the hub's
+          upload_file
+        # Upload the image
+        repo_image_path = os.path.join(REPOSITORY_DIR,os.path.join
+                          (metadata_name,'image.png'))
+        
+        _ = upload_file(path_or_fileobj = image_output_filename,
+                    path_in_repo =repo_image_path,
+                    repo_id=DATASET_REPO_ID,
+                    repo_type='dataset',
+                    token=HF_TOKEN
+                ) 
+        # Upload the metadata
+        repo_json_path = os.path.join(REPOSITORY_DIR,os.path.join
+                        (metadata_name,'metadata.jsonl'))
+        _ = upload_file(path_or_fileobj = json_file_path,
+                    path_in_repo =repo_json_path,
+                    repo_id= DATASET_REPO_ID,
+                    repo_type='dataset',
+                    token=HF_TOKEN
+                )        
+        adversarial_number+=1
+        repo.git_pull()    
+        return "*****Logs save successfully!!!!"
+    except Exception as e:
+        return "Error whils saving logs -->"+ str(e)