Update app.py

app.py

@@ -1,19 +1,29 @@
 import gradio as gr
 from sentence_transformers import CrossEncoder
 
-# Load CrossEncoder model
-model = CrossEncoder("cross-encoder/nli-deberta-v3-base")
+# Load CrossEncoder with 3 labels
+model = CrossEncoder("cross-encoder/nli-deberta-v3-base", num_labels=3)
 
 def predict_similarity(sentence1, sentence2):
-    score = model.predict([(sentence1, sentence2)])  # returns numpy array
-    return float(score.squeeze()[0])  # ✅ safe extraction of single float
+    # 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
 
-demo = gr.Interface(
-    fn=predict_similarity,
-    inputs=["text", "text"],
-    outputs="number",
-    title="CrossEncoder (nli-deberta-v3-base)",
-    description="Enter two sentences to compute semantic similarity."
-)
+# 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].")
+
+    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)
 
 demo.launch()