app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util

# Load your fine-tuned SBERT model
model = SentenceTransformer("sangambhamare/MarathiSentenceSimilarity")

def compute_similarity(sent1, sent2):
    emb1 = model.encode(sent1, convert_to_tensor=True)
    emb2 = model.encode(sent2, convert_to_tensor=True)
    score = util.pytorch_cos_sim(emb1, emb2).item()
    return f"{score * 100:.2f}%"

# Read full HTML report (with Chart.js charts)
with open("interactive_report.html", "r", encoding="utf-8") as f:
    report_html = f.read()

# Build the Gradio app, setting the browser tab title here
with gr.Blocks(title="Evaluating & Enhancing Marathi Sentence Similarity") as demo:

    # 1) Main titles (centered)
    gr.HTML(
        """
        <div style="text-align:center; margin-bottom:2rem;">
          <h1>Evaluating &amp; Enhancing Marathi Sentence Similarity</h1>
          <p style="text-align:center; font-size:1.1em; margin-top:0;">
            <em>An interactive exploration of adapting AI for a low-resource language.</em>
          </p>
        </div>
        """
    )

    # 2) Centered demo heading
    gr.HTML(
        """
        <div style="text-align:center; margin-bottom:1.5rem;">
          <h2>Marathi Sentence Similarity Demo</h2>
        </div>
        """
    )

    # 3) The demo widgets
    with gr.Row():
        sent1 = gr.Textbox(
            label="वाक्य १ (Sentence 1)",
            lines=2,
            placeholder="तो शाळेत जातो.",
            elem_id="input1"
        )
        sent2 = gr.Textbox(
            label="वाक्य २ (Sentence 2)",
            lines=2,
            placeholder="तो विद्यालयात जातो.",
            elem_id="input2"
        )
    btn = gr.Button("Compute Similarity")
    result = gr.Textbox(label="Similarity Score", interactive=False)
    btn.click(fn=compute_similarity, inputs=[sent1, sent2], outputs=result)

    # 4) Remaining report
    gr.HTML(f"""<div style="width:100%; overflow-x:auto;">{report_html}</div>""")

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