import os
from doctr.io import DocumentFile
from doctr.models import ocr_predictor, from_hub
import gradio as gr

os.environ['USE_TORCH'] = '1'
reco_model_zgh = from_hub('ayymen/crnn_mobilenet_v3_large_zgh')
predictor_zgh = ocr_predictor(reco_arch=reco_model_zgh, pretrained=True)

reco_model = from_hub('ayymen/crnn_mobilenet_v3_large_tifinagh')
predictor = ocr_predictor(reco_arch=reco_model, pretrained=True)

title = "Tifinagh OCR"
description = """Upload an image to get the OCR results!
Thanks to @iseddik for the data!"""

def ocr(img, script):
    img.save("out.jpg")
    doc = DocumentFile.from_images("out.jpg")
    output = predictor_zgh(doc) if script == "Tifinagh-IRCAM" else predictor(doc)
    res = ""
    for obj in output.pages:
        for obj1 in obj.blocks:
            for obj2 in obj1.lines:
                for obj3 in obj2.words:
                    res = res + " " + obj3.value
                res = res + "\n"
            res = res + "\n"
    _output_name = "RESULT_OCR.txt"
    open(_output_name, 'w', encoding="utf-8").close() # clear file
    with open(_output_name, "w", encoding="utf-8", errors="ignore") as f:
        f.write(res)
        print("Writing into file")
    return res, _output_name

demo = gr.Interface(fn=ocr,
                    inputs=[
                        gr.Image(type="pil"),
                        gr.Dropdown(choices=['Tifinagh-IRCAM', 'Tifinagh'], label="Script", value="Tifinagh-IRCAM")
                    ],
                    outputs=[
                        gr.Textbox(lines=10, label="Full Text"),
                        gr.File(label="Download OCR Results")
                    ],
                    title=title,
                    description=description,
                    examples=[
                        ["Examples/3.png", "Tifinagh-IRCAM"],
                        ["Examples/2.jpg", "Tifinagh-IRCAM"],
                        ["Examples/1.jpg", "Tifinagh-IRCAM"]
                    ]
                    )

demo.launch(debug=True)