Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+# Load model and tokenizer
+model_name = 'cross-encoder/ms-marco-MiniLM-L6-v2'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.eval()
+
+# Define inference function
+def get_similarity(question, answer):
+    features = tokenizer(question, answer, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        score = model(**features).logits
+        return float(score[0][0])  # Convert tensor to float
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=get_similarity,
+    inputs=[
+        gr.Textbox(label="Question"),
+        gr.Textbox(label="Answer")
+    ],
+    outputs=gr.Number(label="Similarity Score"),
+    title="Cross-Encoder QA Relevance"
+)
+
+iface.launch()