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app.py

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+from datasets import load_from_disk, load_dataset
+import pandas as pd
+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')
+from sentence_transformers import SentenceTransformer
+#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)
+    sdf = pd.DataFrame(retrieved_examples)
+    sdf["scores"] = scores
+    return sdf[["sent", "dialogue", "scores"]]
+
+example_sample = [
+    ["这座教堂建在山上", 3],
+    #["第一次世界大战结束了", 5],
+]
+
+def demo_func(prefix, max_length):
+    max_length = max(int(max_length), 3)
+    l = retrieve_search_df(prefix, max_length)[["dialogue"]].values.tolist()
+    assert type(l) == type([])
+    return {
+        "Dialogue Context": l
+    }
+
+demo = gr.Interface(
+        fn=demo_func,
+        inputs=[gr.Text(label = "Prefix"),
+                gr.Number(label = "Top K", value = 10)
+        ],
+        outputs="json",
+        title=f"Chinese Context Dialogue Generator 🐰 sample search demonstration",
+        #description = 'This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/Daliy-Dialogue](https://github.com/svjack/Daliy-Dialogue)</h4></b>\n',
+        examples=example_sample if example_sample else None,
+        cache_examples = False
+    )
+
+demo.launch(server_name=None, server_port=None)

requirements.txt

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+torch
+transformers==4.20.1
+datasets
+faiss-cpu
+sentence-transformers