new gradio app

app.py

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+import gradio as gr
+from sentence_transformers.cross_encoder import CrossEncoder
+
+ce = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2")
+
+def rerank(query, docs_list):
+    pairs = [[query, doc] for doc in docs_list]
+    scores = ce.predict(pairs)
+    sorted_docs = [doc for _, doc in sorted(zip(scores, docs_list), reverse=True)]
+    return sorted_docs, scores.tolist()
+
+app = gr.Interface(
+    fn=rerank,
+    inputs=[gr.Textbox(label="Query"), gr.Textbox(label="Docs (JSON‑list)")],
+    outputs=[gr.Dataframe(type="array", headers=["doc","score"])],
+    api_name="rerank"
+)
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+gradio
+sentence-transformers
+torch