Update app.py

app.py

@@ -4,15 +4,15 @@ import os
 import gradio as gr
 
 #ds_with_embeddings = load_dataset("svjack/bloom-dialogue-generate-ds-zh", split="train")
-ds_with_embeddings = load_dataset("svjack/context-dialogue-generate-ds-zh", split="train")
-ds_with_embeddings.add_faiss_index(column='embeddings')
+ds_with_embeddings = load_dataset("svjack/context-dialogue-generate-ds-zh-v1", split="train")
+ds_with_embeddings.add_faiss_index(column='L_emb')
 from sentence_transformers import SentenceTransformer
-#encoder = SentenceTransformer("sentence-transformers/LaBSE")
-encoder = SentenceTransformer("sentence-transformers/clip-ViT-B-32-multilingual-v1")
+encoder = SentenceTransformer("sentence-transformers/LaBSE")
+#encoder = SentenceTransformer("sentence-transformers/clip-ViT-B-32-multilingual-v1")
 
 def retrieve_search_df(question = "这座教堂建在山上", top_k = 10):
     question_embedding = encoder.encode(question)
-    scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', question_embedding, k=top_k)
+    scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('L_emb', question_embedding, k=top_k)
     sdf = pd.DataFrame(retrieved_examples)
     sdf["scores"] = scores
     return sdf[["sent", "dialogue", "scores"]]