Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Function to compute relevance score
+def get_relevance_score(query, paragraph):
+    inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        scores = model(**inputs).logits.squeeze().item()
+    return round(scores, 4)
+
+# Gradio interface
+interface = gr.Interface(
+    fn=get_relevance_score,
+    inputs=[
+        gr.Textbox(label="Query", placeholder="Enter your search query..."),
+        gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
+    ],
+    outputs=gr.Number(label="Relevance Score"),
+    title="Cross-Encoder Relevance Scoring",
+    description="Enter a query and a document paragraph to get a relevance score using the MS MARCO MiniLM L-12 v2 model."
+)
+
+if __name__ == "__main__":
+    interface.launch()