app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

def compute_similarity(text1, text2):
    if not text1.strip() or not text2.strip():
        return {
            "similarity_percent": 0,
            "score": 0,
            "message": "Both text fields are required."
        }

    emb1 = model.encode(text1, convert_to_tensor=True)
    emb2 = model.encode(text2, convert_to_tensor=True)

    score = float(util.cos_sim(emb1, emb2))
    percent = round(score * 100, 2)

    return {
        "similarity_percent": percent,
        "score": score,
        "message": "Success"
    }

iface = gr.Interface(
    fn=compute_similarity,
    inputs=[gr.Textbox(lines=5), gr.Textbox(lines=5)],
    outputs="json",
    title="Writenix Similarity Engine"
)

iface.launch()