import gradio as gr
from sentence_transformers import CrossEncoder

# Load a Semantic Textual Similarity CrossEncoder
model = CrossEncoder("cross-encoder/stsb-roberta-large")

def predict_similarity(s1, s2):
    score = model.predict([(s1, s2)])[0]   # already continuous
    return round(float(score), 4)

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("## 🔎 Semantic Textual Similarity (CrossEncoder)")
    gr.Markdown("Score ranges 0 (unrelated) → 1 (identical meaning).")

    with gr.Row():
        s1 = gr.Textbox(label="Sentence 1", placeholder="Type first sentence...")
        s2 = gr.Textbox(label="Sentence 2", placeholder="Type second sentence...")

    btn = gr.Button("Compute Similarity 🚀")
    out = gr.Number(label="Similarity Score (0–1)")

    btn.click(fn=predict_similarity, inputs=[s1, s2], outputs=out)

    gr.Examples(
        examples=[
            ["I am a boy", "I am a girl"],
            ["I am happy today", "I am feeling a bit curious and joyful today"],
            ["I am going", "I am moving from you"],
            ["The sun is hot", "Ice cream is cold"],
        ],
        inputs=[s1, s2],
    )

demo.launch()