Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import CrossEncoder
+import pandas as pd
+
+reranker = CrossEncoder("sentence-transformers/all-MiniLM-L12-v2")
+
+
+def rerank_documents(query: str, documents: pd.DataFrame) -> pd.DataFrame:
+    documents = documents.copy()
+    documents = documents.drop_duplicates("chunk")
+    documents["rank"] = reranker.predict([[query, hit] for hit in documents["chunk"]])
+    documents = documents.sort_values(by="rank", ascending=False)
+    return documents
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""# RAG Hub Datasets 
+                
+                Part of [smol blueprint](https://github.com/davidberenstein1957/smol-blueprint) - a smol blueprint for AI development, focusing on practical examples of RAG, information extraction, analysis and fine-tuning in the age of LLMs.""")
+
+    query_input = gr.Textbox(
+        label="Query", placeholder="Enter your question here...", lines=3
+    )
+    documents_input = gr.Dataframe(
+        label="Documents", headers=["chunk"], wrap=True, interactive=True
+    )
+        
+    submit_btn = gr.Button("Submit")
+    documents_output = gr.Dataframe(
+        label="Documents", headers=["chunk", "rank"], wrap=True
+    )
+
+    submit_btn.click(
+        fn=rerank_documents,
+        inputs=[query_input, documents_input],
+        outputs=[documents_output],
+    )
+
+demo.launch()