import gradio as gr
import cv2
import easyocr
from PIL import Image
import numpy as np

# Functions for OCR steps
def get_grayscale(image):
    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

def thresholding(src, threshold_value):
    return cv2.threshold(src, threshold_value, 255, cv2.THRESH_BINARY)[1]

def ocr_with_easy(img_path):
    reader = easyocr.Reader(['en'])
    bounds = reader.readtext(img_path, paragraph="False", detail=0)
    bounds = ''.join(bounds)
    return bounds

def process_image(img, steps, threshold_value):
    for step in steps:
        if step == "Grayscale Conversion":
            img = get_grayscale(img)
        elif step == "Thresholding":
            img = thresholding(img, threshold_value)
    cv2.imwrite('processed_image.png', img)
    return 'processed_image.png'

def generate_ocr(img, steps, threshold_value):
    text_output = ''
    if img is not None and img.any():
        processed_image_path = process_image(img, steps, threshold_value)
        text_output = ocr_with_easy(processed_image_path)
    else:
        raise gr.Error("Please upload an image and select the processing steps!")
    return text_output

# Interactive tutorial steps
tutorial_steps = [
    "Grayscale Conversion",
    "Thresholding"
]

# Interactive questions
questions = [
    {
        "question": "What is the first step in OCR?",
        "options": ["Binarization", "Grayscale Conversion", "Edge Detection"],
        "answer": "Grayscale Conversion"
    },
    {
        "question": "What is the purpose of thresholding in OCR?",
        "options": ["To detect edges", "To convert image to grayscale", "To binarize the image"],
        "answer": "To binarize the image"
    },
    {
        "question": "Which library is used for OCR in this app?",
        "options": ["Tesseract", "EasyOCR", "OpenCV"],
        "answer": "EasyOCR"
    },
    {
        "question": "What format is the image saved in after preprocessing?",
        "options": ["JPG", "PNG", "TIFF"],
        "answer": "PNG"
    },
    {
        "question": "What does OCR stand for?",
        "options": ["Optical Character Recognition", "Optical Character Reading", "Optical Code Recognition"],
        "answer": "Optical Character Recognition"
    }
]

def quiz_interface():
    def check_answer(question_idx, selected):
        if questions[question_idx]["answer"] == selected:
            return "Correct!"
        else:
            return "Incorrect. The correct answer is: " + questions[question_idx]["answer"]

    interfaces = []
    for idx, question in enumerate(questions):
        radio = gr.Radio(choices=question["options"], label=question["question"])
        button = gr.Button("Submit")
        output = gr.Textbox(label="Result")

        def create_submit_fn(idx):
            def submit(selected):
                return check_answer(idx, selected)
            return submit

        interfaces.append(gr.Interface(
            create_submit_fn(idx),
            radio,
            output,
            live=True
        ))

    return interfaces

# Explanation text
explanation_text = """
**Welcome to the OCR Tutorial!**
Optical Character Recognition (OCR) is a technology used to convert different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data.
**Steps in the OCR Process:**
1. **Grayscale Conversion:** The first step in OCR is converting the image to grayscale. This simplifies the image and reduces the amount of data the OCR algorithm needs to process.
2. **Thresholding:** This step converts the grayscale image into a binary image, where the text is in black, and the background is in white. This makes it easier for the OCR algorithm to distinguish text from the background.
3. **OCR using EasyOCR:** We use the EasyOCR library to recognize and extract text from the preprocessed image.
**Interactive Tutorial:**
Please upload an image and select the correct order of steps to perform OCR. You can also adjust the threshold value using the slider.
"""

example_image_path = "origin.png"  # Provide the path to your example image

# Load the example image
example_image = np.array(Image.open(example_image_path))

image = gr.Image(value=example_image, label="Upload or edit image for OCR")
steps = gr.CheckboxGroup(choices=tutorial_steps, label="Select and order the steps for OCR")
threshold = gr.Slider(0, 255, value=127, step=1, label="Threshold Value")
output = gr.Textbox(label="OCR Output")
explanation = gr.Markdown(explanation_text)

ocr_app = gr.Interface(
    fn=generate_ocr,
    inputs=[image, steps, threshold],
    outputs=output,
    title="Optical Character Recognition",
    description=explanation_text,
    css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}"
)

quiz_app = gr.TabbedInterface(
    [ocr_app] + quiz_interface(),
    ["OCR Tool"] + [f"Question {i+1}" for i in range(len(questions))],
    title="OCR Tutorial and Quiz"
)

quiz_app.launch()