app.py

import stanza
import gradio as gr
import os
import base64

# Load local model directory
MODEL_DIR = "viv/UD_Greek-GUD"

# Initialize Stanza pipeline
nlp = stanza.Pipeline(
    lang='el',
    package='gud',
    processors='tokenize,pos,lemma,depparse',
    dir=MODEL_DIR,
    use_gpu=False,
    verbose=True
)



# Generate HTML table with POS and lemma annotations
def analyze_text_as_html(text):
    doc = nlp(text)
    rows = []
    for sent in doc.sentences:
        for word in sent.words:
            lemma_style = "font-weight:bold;"
            pos_color = {
                "NOUN": "background-color:lightblue;",
                "VERB": "background-color:lightgreen;",
                "ADJ": "background-color:lightcoral;",
                "ADV": "background-color:khaki;"
            }.get(word.upos, "")

            row = f"""
                <tr>
                    <td>{word.text}</td>
                    <td style="{lemma_style}">{word.lemma}</td>
                    <td style="{pos_color}">{word.upos}</td>
                </tr>
            """
            rows.append(row)

    html_table = f"""
    <table style='border-collapse: collapse; width: 100%;'>
        <thead>
            <tr>
                <th>Λέξη</th>
                <th>Λήμμα</th>
                <th>Μέρος του λόγου</th>
            </tr>
        </thead>
        <tbody>
            {''.join(rows)}
        </tbody>
    </table>
    """
    return html_table

# Generate dependency relations text
def show_dependencies(text, with_labels=True):
    doc = nlp(text)
    visuals = []
    for sentence in doc.sentences:
        arrows = []
        for word in sentence.words:
            head = "ROOT" if word.head == 0 else sentence.words[word.head - 1].text
            if with_labels:
                arrows.append(f"{word.text} → {head} ({word.deprel})")
            else:
                if word.head != 0:
                    arrows.append(f"{word.text} → {head}")
        visuals.append("\n".join(arrows))
    return "\n\n".join(visuals)

# Gradio interface
with gr.Blocks() as demo:
    gr.HTML(f"""
        <div style='display: flex; align-items: center; gap: 20px; margin-bottom: 20px;'>
            <h1 style='font-size: 28px; font-weight: bold; margin: 0;'>Demo Parse</h1>
        </div>
    """)

    gr.Markdown("## Ανάλυση Κειμένου με το Μοντέλο GUD (Νέα Ελληνική)")
    input_text = gr.Textbox(lines=4, label="Εισαγωγή Κειμένου")

    with gr.Column():
        pos_output = gr.HTML(label="Ανάλυση (με χρώματα)")
        dep_output = gr.Textbox(label="Σχέσεις Εξάρτησης (με ετικέτες)", lines=8)
        simple_dep_output = gr.Textbox(label="Σχέσεις Εξάρτησης (χωρίς ετικέτες)", lines=8)

    analyze_button = gr.Button("Ανάλυσε")

    def process(text):
        return (
            analyze_text_as_html(text),
            show_dependencies(text, with_labels=True),
            show_dependencies(text, with_labels=False),
        )

    analyze_button.click(
        fn=process,
        inputs=input_text,
        outputs=[pos_output, dep_output, simple_dep_output]
    )

demo.launch()