Update app.py

app.py

@@ -7,27 +7,29 @@ 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.drop_duplicates("text")
+    documents["rank"] = reranker.predict([[query, hit] for hit in documents["text"]])
     documents = documents.sort_values(by="rank", ascending=False)
     return documents
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("""# RAG - rerank 
+    gr.Markdown("""# RAG - Augment 
                 
-                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.""")
+                Applies reranking to the retrieved documents using [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2).
+                
+                Part of [AI blueprint](https://github.com/huggingface/ai-blueprint) - a 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
+        label="Documents", headers=["text"], wrap=True, interactive=True
     )
         
     submit_btn = gr.Button("Submit")
     documents_output = gr.Dataframe(
-        label="Documents", headers=["chunk", "rank"], wrap=True
+        label="Documents", headers=["text", "rank"], wrap=True
     )
 
     submit_btn.click(