app

app.py

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+
+import gradio as gr
+import torch
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+model = SentenceTransformer(
+    "sentence-transformers/sentence-t5-base",
+    device="cuda" if torch.cuda.is_available() else "cpu"
+)
+
+
+def get_metrics(vec1, vec2):
+    sim = float(cosine_similarity(vec1, vec2)[0][0])
+
+    scs = abs((sim) ** 3)
+    m = {
+        "cosine_similarity": round(sim, 4),
+        "scs": round(scs, 4)
+    }
+    return m
+
+
+def compute(text1, text2):
+
+    texts = [text1, text2]
+
+    embeddings = model.encode(
+        texts,
+        show_progress_bar=False,
+        convert_to_numpy=True,
+        normalize_embeddings=True,
+    )
+
+    return get_metrics(embeddings[0].reshape(1, -1), embeddings[1].reshape(1, -1))
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        text1 = gr.Textbox(label="Enter Text 1")
+        text2 = gr.Textbox(label="Enter Text 2")
+
+    with gr.Column():
+        submit_btn = gr.Button("Submit")
+
+        output = gr.JSON(
+            label="Score",
+        )
+
+    # # callback ---
+    submit_btn.click(
+        fn=compute,
+        inputs=[text1, text2],
+        outputs=output
+    )
+
+
+demo.launch()

requirements.txt

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+sentence-transformers
+sentencepiece
+scikit-learn