# 🔍 Masked Word Predictor | CPU-only HF Space

import gradio as gr
import pandas as pd
from transformers import pipeline
from transformers.pipelines.base import PipelineException

# 1. Load the fill-mask pipeline once
fill_mask = pipeline("fill-mask", model="distilroberta-base", device=-1)

def predict_mask(sentence: str, top_k: int):
    # 2. Get the actual mask token (e.g. "<mask>")
    mask = fill_mask.tokenizer.mask_token

    # 3. Allow users to type [MASK]
    sentence = sentence.replace("[MASK]", mask)

    # 4. Validate presence of mask
    if mask not in sentence:
        return pd.DataFrame(
            [["Error: please include `[MASK]` in your sentence.", 0.0]],
            columns=["Sequence", "Score"]
        )

    # 5. Run the pipeline safely
    try:
        preds = fill_mask(sentence, top_k=top_k)
    except PipelineException as e:
        return pd.DataFrame([[f"Error: {str(e)}", 0.0]],
                            columns=["Sequence", "Score"])

    # 6. Build a DataFrame from list-of-lists
    rows = [[p["sequence"], round(p["score"], 3)] for p in preds]
    return pd.DataFrame(rows, columns=["Sequence", "Score"])

with gr.Blocks(title="🔍 Masked Word Predictor") as demo:
    gr.Markdown(
        "# 🔍 Masked Word Predictor\n"
        "Enter a sentence with one `[MASK]` token and see the top-K completions."
    )

    with gr.Row():
        sentence = gr.Textbox(
            lines=2,
            placeholder="e.g. The salon’s new color treatment is [MASK].",
            label="Input Sentence"
        )
        top_k = gr.Slider(
            minimum=1, maximum=10, step=1, value=5,
            label="Top K Predictions"
        )

    predict_btn = gr.Button("Predict 🔍", variant="primary")

    results_df = gr.Dataframe(
        headers=["Sequence", "Score"],
        datatype=["str", "number"],
        wrap=True,
        interactive=False,
        label="Predictions"
    )

    predict_btn.click(
        fn=predict_mask,
        inputs=[sentence, top_k],
        outputs=results_df
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")