import gradio as gr
import cv2
import numpy as np
import pytesseract
import time

def text1(image):
    start_time = time.time()

    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 

    # Apply thresholding to preprocess the image
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

    # Perform OCR on the thresholded image
    text = pytesseract.image_to_string(thresh, lang='eng')

    # Split the OCR output into a list of lines
    lines = [line.strip() for line in text.split('\n') if line.strip()]

    end_time = time.time()
    time_taken = end_time - start_time

    return {
        'text': lines,
        'time_taken': time_taken
    }

def text2(image):   

    start_time = time.time()
  
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   
    
    inverted = cv2.bitwise_not(gray)   

    # apply thresholding to preprocess the image
    thresh = cv2.threshold(inverted, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    
    # text = pytesseract.image_to_string(plate_roi, lang='eng',  config='--psm 6')
    text = pytesseract.image_to_string(thresh, lang='eng')
    
    # split the OCR output into a list of lines
    lines = [line.strip() for line in text.split('\n') if line.strip()]

    end_time = time.time()
    time_taken = end_time - start_time

    return {
        'text': lines,
        'time_taken': time_taken
    }

def text3(image):   

    start_time = time.time()
  
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   
    
    inverted = cv2.bitwise_not(gray)   

    # apply thresholding to preprocess the image
    thresh = cv2.threshold(inverted, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    
    # text = pytesseract.image_to_string(plate_roi, lang='eng',  config='--psm 6')
    text = pytesseract.image_to_string(thresh, lang='eng', config='--psm 6 -c tessedit_char_whitelist=0123456789.')
    
    # split the OCR output into a list of lines
    lines = [line.strip() for line in text.split('\n') if line.strip()]

    end_time = time.time()
    time_taken = end_time - start_time

    return {
        'text': lines,
        'time_taken': time_taken
    }

with gr.Blocks() as demo:
    gr.Markdown("Various OCR (Optical Character Recognition) tools can convert images into text form. Some popular methods include Tesseract OCR, Google Cloud Vision API, and Microsoft Azure Cognitive Services OCR. These tools use advanced algorithms to analyze images, recognize characters, and convert them into editable text, enabling efficient data extraction and document digitization.")
    with gr.Tab("OCR Function 1"):
        image_input1 = gr.inputs.Image()
        text_output1 = gr.outputs.Textbox()       
        button1 = gr.Button("Perform OCR")
    with gr.Tab("OCR Function 2"):
        image_input2 = gr.inputs.Image()
        text_output2 = gr.outputs.Textbox()
        button2 = gr.Button("Perform OCR")
    with gr.Tab("OCR Function 3"):
        image_input3 = gr.inputs.Image()
        text_output3 = gr.outputs.Textbox()
        button3 = gr.Button("Perform OCR")

    button1.click(text1, inputs=image_input1, outputs=text_output1)
    button2.click(text2, inputs=image_input2, outputs=text_output2)
    button3.click(text3, inputs=image_input3, outputs=text_output3)

demo.launch()