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import gradio as gr
import tensorflow as tf
import keras_ocr
import requests
import cv2
import os
import csv
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
import datasets
from datasets import load_dataset, Image
from PIL import Image
from paddleocr import PaddleOCR
from save_data import flag
from transformers import pipeline # Importing the pipeline
"""
Paddle OCR
"""
def ocr_with_paddle(img):
finaltext = ''
ocr = PaddleOCR(lang='en', use_angle_cls=True)
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
"""
# grayscale 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):
text_output = ''
if (img).any():
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)
try:
flag(Method, text_output, img)
except Exception as e:
print(e)
# Generate Text using FLAN-T5 model
text_gen = generate_text_with_flan_t5(text_output)
return text_gen
else:
raise gr.Error("Please upload an image!!!!")
"""
Text Generation using FLAN-T5
"""
def generate_text_with_flan_t5(input_text):
# Load the pre-trained FLAN-T5 model
pipe = pipeline("text2text-generation", model="google/flan-t5-large")
# Use the model to generate a response based on the OCR output
output = pipe(input_text)
return output[0]['generated_text']
"""
Create user interface for OCR demo
"""
image = gr.Image()
method = gr.Radio(["PaddleOCR", "EasyOCR", "KerasOCR"], value="PaddleOCR")
output = gr.Textbox(label="Generated Text")
demo = gr.Interface(
generate_ocr,
[method, image],
output,
title="Optical Character Recognition and Text Generation",
css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}",
article="""<p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at
<a href="mailto:[email protected]" target="_blank">[email protected]</a>
<p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
)
demo.launch()
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