app.py

import os
import yaml
import gdown 
import gradio as gr
from predict import PredictTri
from huggingface_hub import hf_hub_download

output_path = "tashkeela-d2.pt"
gdrive_templ = "https://drive.google.com/file/d/{}/view?usp=sharing"
if not os.path.exists(output_path):
    model_gdrive_id = "1FGelqImFkESbTyRsx_elkKIOZ9VbhRuo"
    gdown.download(gdrive_templ.format(model_gdrive_id), output=output_path, quiet=False, fuzzy=True)

output_path = "vocab.vec"
if not os.path.exists(output_path):
    vocab_gdrive_id = "1-0muGvcSYEf8RAVRcwXay4MRex6kmCii"
    gdown.download(gdrive_templ.format(vocab_gdrive_id), output=output_path, quiet=False, fuzzy=True)

if not os.path.exists("td2/tashkeela-ashaar-td2.pt"):
    hf_hub_download(repo_id="munael/Partial-Arabic-Diacritization-TD2", filename="tashkeela-ashaar-td2.pt", local_dir="td2")

with open("config.yaml", 'r', encoding="utf-8") as file:
    config = yaml.load(file, Loader=yaml.FullLoader)

config["train"]["max-sent-len"] = config["predictor"]["window"]
config["train"]["max-token-count"] = config["predictor"]["window"] * 3

predictor = PredictTri(config)

current_model_name = "TD2"
config["model-name"] = current_model_name

def diacritze_full(text, model_name):
    global current_model_name, predictor
    if model_name != current_model_name:
        config["model-name"] = model_name
        current_model_name = model_name
        predictor = PredictTri(config)

    do_hard_mask = None 
    threshold = None 
    predictor.create_dataloader(text, False, do_hard_mask, threshold, model_name)
    diacritized_lines = predictor.predict_partial(do_partial=False, lines=text.split('\n'))
    return diacritized_lines

def diacritze_partial(text, mask_mode, threshold, model_name):
    global current_model_name, predictor
    if model_name != current_model_name:
        config["model-name"] = model_name
        current_model_name = model_name
        predictor = PredictTri(config)

    do_partial = True 
    predictor.create_dataloader(text, do_partial, mask_mode=="Hard", threshold, model_name)
    diacritized_lines = predictor.predict_partial(do_partial=do_partial, lines=text.split('\n'))
    return diacritized_lines

with gr.Blocks(theme=gr.themes.Default(text_size="lg")) as demo:
    gr.Markdown(
    """
    # Partial Diacritization: A Context-Contrastive Inference Approach
    ### Authors: Muhammad ElNokrashy, Badr AlKhamissi
    ### Paper Link: TBD (abstract below)
    """)

    gr.HTML(
        "<img src='./PartialDD.png' style='float:right'/>"
    )

    model_choice = gr.Dropdown(
        choices=["D2", "TD2"], 
        label="Diacritization Model", 
        value=current_model_name
    )
    
    with gr.Tab(label="Full Diacritization"):

        full_input_txt = gr.Textbox(
            placeholder="اكتب هنا", 
            lines=5, 
            label="Input",
            type='text',
            rtl=True,
            text_align='right',
        )

        full_output_txt = gr.Textbox(
            lines=5, 
            label="Output",
            type='text',
            rtl=True,  
            text_align='right',
            show_copy_button=True,
        )

        full_btn = gr.Button(value="Shakkel")
        full_btn.click(diacritze_full, inputs=[full_input_txt, model_choice], outputs=[full_output_txt])

        gr.Examples(
            examples=[
                "ولو حمل من مجلس الخيار ، ولم يمنع من الكلام", "TD2"
            ],
            inputs=full_input_txt,
            outputs=full_output_txt,
            fn=diacritze_full,
            cache_examples=True,
        )

    with gr.Tab(label="Partial Diacritization") as partial_settings:
        with gr.Row():
            masking_mode = gr.Radio(choices=["Hard", "Soft"], value="Hard", label="Masking Mode")
            threshold_slider = gr.Slider(label="Soft Masking Threshold", minimum=0, maximum=1, value=0.1)
        
        partial_input_txt = gr.Textbox(
            placeholder="اكتب هنا", 
            lines=5, 
            label="Input",
            type='text',
            rtl=True,
            text_align='right',
        )

        partial_output_txt = gr.Textbox(
            lines=5, 
            label="Output",
            type='text',
            rtl=True,  
            text_align='right',
            show_copy_button=True,
        )

        partial_btn = gr.Button(value="Shakkel")
        partial_btn.click(diacritze_partial, inputs=[partial_input_txt, masking_mode, threshold_slider, model_choice], outputs=[partial_output_txt])

        gr.Examples(
            examples=[
                ["ولو حمل من مجلس الخيار ، ولم يمنع من الكلام", "Hard", 0, "TD2"],
                ["ولو حمل من مجلس الخيار ، ولم يمنع من الكلام", "Soft", 0.1, "TD2"],
                ["ولو حمل من مجلس الخيار ، ولم يمنع من الكلام", "Soft", 0.01, "TD2"],
            ],
            inputs=[partial_input_txt, masking_mode, threshold_slider],
            outputs=partial_output_txt,
            fn=diacritze_partial,
            cache_examples=True,
        )

        gr.Markdown(
        """
        ### Abstract
        > Diacritization plays a pivotal role in improving readability and disambiguating the meaning of Arabic texts. Efforts have so far focused on marking every eligible character (Full Diacritization). Comparatively overlooked, Partial Diacritzation (PD) is the selection of a subset of characters to be marked to aid comprehension where needed.Research has indicated that excessive diacritic marks can hinder skilled readers---reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization (CCPD)---a novel approach to PD which integrates seamlessly with existing Arabic diacritization systems. CCPD processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality {SR, PDER, HDER, ERE}, essential for establishing this as a machine learning task. Lastly, we introduce TD2, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.
        """)

if __name__ == "__main__":
    demo.queue().launch(
        # share=False,
        # debug=False,
        # server_port=7860,
        # server_name="0.0.0.0",
        # ssl_verify=False,
        # ssl_certfile="cert.pem",
        # ssl_keyfile="key.pem"
    )