app.py

import gradio as gr
import requests
import tensorflow as tf
import keras_ocr
import cv2
import os
import numpy as np
import pandas as pd
import huggingface_hub
from huggingface_hub import Repository
from datetime import datetime
import scipy.ndimage.interpolation as inter
import easyocr
from PIL import Image
from paddleocr import PaddleOCR
import socket
# from send_email_user import send_user_email
from huggingface_hub import HfApi
import smtplib

# HF_TOKEN = os.getenv('HF_TOKEN')
# print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$",type(HF_TOKEN))
# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN,'OCR-image-to-text',True,True)


DATASET_REPO_URL = "https://huggingface.co/datasets/pragnakalp/OCR-img-to-text"
DATA_FILENAME = "ocr_data.csv"
DATA_FILE = os.path.join("ocr_data", DATA_FILENAME)
HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_REPO_ID = "pragnakalp/OCR-img-to-text"
print("is none?", HF_TOKEN is None)
try:
    hf_hub_download(
        repo_id=DATASET_REPO_ID,
        filename=DATA_FILENAME,
        cache_dir=DATA_DIRNAME,
        force_filename=DATA_FILENAME
    )
except:
    print("file not found")

repo = Repository(
    local_dir="ocr_data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

def get_device_ip_address():

    if os.name == "nt":
        result = "Running on Windows"
        hostname = socket.gethostname()
        result += "\nHostname:  " + hostname
        host = socket.gethostbyname(hostname)
        result += "\nHost-IP-Address:" + host
        return result
    elif os.name == "posix":
        gw = os.popen("ip -4 route show default").read().split()
        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        s.connect((gw[2], 0))
        ipaddr = s.getsockname()[0]
        gateway = gw[2]
        host = socket.gethostname()
        result = "\nIP address:\t\t" + ipaddr  + "\r\nHost:\t\t" + host
        return result
    else:
        result = os.name + " not supported yet."
        return result

            
"""
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,img):
    try:
        text_output = ''

        print("Method___________________",Method)
        if Method == 'EasyOCR':
            text_output = ocr_with_easy(img)
        if Method == 'KerasOCR':
            text_output = ocr_with_keras(img)
        if Method == 'PaddleOCR':
            text_output = ocr_with_paddle(img)

        with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=["method", "image", "generated_text"])
            writer.writerow(
                {"method": Method, "image": img, "generated_text": text_output}
            )
        commit_url = repo.push_to_hub()
        print(commit_url)
        
        # save_details(Method,text_output,img)
        # sender="[email protected]"
        # password="httscgatatbbxxur"
        # reciever="[email protected]"

        # s = smtplib.SMTP('smtp.gmail.com', 587)
        # s.starttls()
        # s.ehlo()
        # s.login(sender,password)

        # message = """Subject : Appointment Booking\n\n
        #         Hello,
        # Your OCR generated successfully"""
        # s.sendmail(sender, reciever, message)
        # s.quit()
        # mailsend=1
        # print("Send mail successfully")
        return text_output
    
    except Exception as e:
        print("Error in ocr generation ==>",e)
        text_output = "Something went wrong"
    return text_output
"""
Save generated details
"""
def save_details(Method,text_output,img):
    # print("//////////")
    hostname = get_device_ip_address()
    # url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_image_to_text'
    # url = 'http://pragnakalpdev35.pythonanywhere.com/HF_space_image_to_text'
    # myobj = {'Method': Method,'text_output':text_output,'img':img.tolist(),'hostname':hostname}
    # x = requests.post(url, json = myobj)
    
#     method = []
#     img_path = []
#     text = []
#     input_img = ''
#     hostname = ''
#     picture_path = "image.jpg"    
#     curr_datetime = datetime.now().strftime('%Y-%m-%d %H-%M-%S')
#     if text_output:
#         splitted_path = os.path.splitext(picture_path)
#         modified_picture_path = splitted_path[0] + curr_datetime + splitted_path[1]
#         cv2.imwrite("myimage.jpg", img)
#         with open('savedata.txt', 'w') as f:
#             print("write test")
#             f.write("testdata")
#         print("write Successfully")
#         # img = Image.open(r"/home/user/app/")
#         # img.save(modified_picture_path)
#         input_img = modified_picture_path
#         try:
#             df = pd.read_csv("AllDetails.csv")
#             df2 = {'method': Method, 'input_img': input_img, 'generated_text': text_output}
#             df = df.append(df2, ignore_index = True)
#             df.to_csv("AllDetails.csv", index=False)
#         except:
#             method.append(Method)
#             img_path.append(input_img)
#             text.append(text_output)
#             dict = {'method': method, 'input_img': img_path, 'generated_text': text}
#             df = pd.DataFrame(dict,index=None)
#             df.to_csv("AllDetails.csv")

        
    return send_user_email()
    # return x

"""
Create user interface for OCR demo
"""

image = gr.Image(shape=(224, 224),elem_id="img_div")
method = gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],value="PaddleOCR",elem_id="radio_div")
output = gr.Textbox(label="Output")

demo = gr.Interface(
    generate_ocr,
    [method,image],
    output,
    title="Optical Character Recognition",
    description="Try OCR with different methods",
    css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}",
    allow_flagging = "manual",
 #    flagging_dir = "flagged",
	# flagging_callback=hf_writer
)
demo.launch(enable_queue = False)