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 doctr.io import DocumentFile
from doctr.models import ocr_predictor



ocr_model = ocr_predictor(pretrained=True)



"""
Perform OCR with doctr
"""
def ocr_with_doctr(file):
    text_output = ''
    
    # Load the document
    doc = DocumentFile.from_pdf(file)
    
    # Perform OCR
    result = ocr_model(doc)
    
    # Extract text from OCR result
    for page in result.pages:
        for block in page.blocks:
            for line in block.lines:
                text_output += " ".join([word.value for word in line.words]) + "\n"
    
    return text_output

"""
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

def generate_ocr(Method, file):
    text_output = ''
    if isinstance(file, bytes):  # Handle file uploaded as bytes
        file = io.BytesIO(file)

    if file.name.endswith('.pdf'):
        # Perform OCR on the PDF using doctr
        text_output = ocr_with_doctr(file)

    else:
        # Handle image file
        img_np = np.array(Image.open(file))
        text_output = generate_text_from_image(Method, img_np)
    
    return text_output

def generate_text_from_image(Method, img):
    text_output = ''
    if Method == 'EasyOCR':
        text_output = ocr_with_easy(img)
    elif Method == 'KerasOCR':
        text_output = ocr_with_keras(img)
    elif Method == 'PaddleOCR':
        text_output = ocr_with_paddle(img)
    return text_output


import gradio as gr

image_or_pdf = gr.File(label="Upload an image or PDF")
method = gr.Radio(["PaddleOCR", "EasyOCR", "KerasOCR"], value="PaddleOCR")
output = gr.Textbox(label="Output")

demo = gr.Interface(
    generate_ocr,
    [method, image_or_pdf],
    output,
    title="Optical Character Recognition",
    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:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</a> 
                <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
)

demo.launch(show_error=True)