app.py

import gradio as gr
from ocr_tamil.ocr import OCR
import torch

ocr_detect = OCR(detect=True,enable_cuda=False)
ocr_recognize = OCR(detect=False,enable_cuda=False)


def predict(image_path,mode):
    if mode == "recognize":
        texts = ocr_recognize.predict(image_path)
    else:
        texts = ocr_detect.predict(image_path)
        texts = [" ".join(texts[0])]

    texts = texts[0]
    return texts


image_examples = ["11.jpg","4.jpg","0.jpg","tamil_handwritten.jpg","1.jpg","2.jpg","3.jpg","5.jpg",
                  "6.jpg","7.jpg","10.jpg","14.jpg"]

mode_examples = ["recognize","recognize","detect","detect","recognize","recognize","recognize"
                 ,"recognize","recognize","recognize","recognize"]

input_1 = gr.Image(type="numpy")
input_2 = gr.Radio(["recognize", "detect"], label="mode", 
                   info="Only Text recognition or need both Text detection + recognition")

examples = [[i,j] for i,j in zip(image_examples,mode_examples)]

gr.Interface(
    predict,
    inputs=[input_1,input_2],
    outputs=gr.Textbox(label="Extracted Text",interactive=False,
                       show_copy_button=True),
    title="OCR TAMIL",
    examples=examples
).launch()