import gradio as gr
import cv2
import numpy as np
from PIL import Image
from EasyOpticalCharacterRecognition import process_image

# Wrapper function for Gradio interface
def infer(image):
    img = np.array(image)
    annotated_img, result_text = process_image(img)
    return Image.fromarray(annotated_img), result_text

# Custom CSS for light blue background and bordered elements
custom_css = """
body 
{
    background-color: #e6f2ff;
}
.gradio-container 
{
    border-radius: 12px;
    padding: 20px;
    border: 2px solid #007acc;
}
.gr-input, .gr-output 
{
    border: 1px solid #007acc;
    border-radius: 10px;
}
"""

# Gradio Interface
demo = gr.Interface(
    fn=infer,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=[
        gr.Image(type="pil", label="Annotated Image"),
        gr.Textbox(label="Detected Text and Classification")
    ],
    title="🧠 OCR & Text Type Classifier",
    description="Application detect text from images and classifier classify text into Computerized & Handwritten Text.",
    theme="soft",  # Optional: you can also try "default" or "huggingface"
    css=custom_css
)
demo.launch()