Update app.py

app.py

@@ -1,29 +1,34 @@
 import gradio as gr
 from sentence_transformers import CrossEncoder
 
-# Load CrossEncoder with 3 labels
-model = CrossEncoder("cross-encoder/nli-deberta-v3-base", num_labels=3)
+# Load a Semantic Textual Similarity CrossEncoder
+model = CrossEncoder("cross-encoder/stsb-roberta-large")
 
-def predict_similarity(sentence1, sentence2):
-    # Get probabilities for contradiction, neutral, entailment
-    probs = model.predict([(sentence1, sentence2)], apply_softmax=True)[0]
-    # Similarity = P(neutral) + P(entailment)
-    similarity = float(probs[1] + probs[2])
-    return round(similarity, 4)  # cleaner output
+def predict_similarity(s1, s2):
+    score = model.predict([(s1, s2)])[0]   # already continuous
+    return round(float(score), 4)
 
-# UI layout
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🤖 CrossEncoder NLI → Semantic Similarity")
-    gr.Markdown("Enter two sentences to compute similarity. "
-                "Score = P(neutral)+P(entailment). Range [0–1].")
+    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)")
+    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()