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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-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")
def retrieve_search_df(question = "今天天气怎么样？", top_k = 10):
question_embedding = encoder.encode(question)
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"]]
example_sample = [
["有哪些有名的显卡？", 3],
["今天天气怎么样？", 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)[["sent" ,"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',
description = 'This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/Context2Dialogue](https://github.com/svjack/Context2Dialogue)</h4></b>\n',
examples=example_sample if example_sample else None,
cache_examples = False
)
demo.launch(server_name=None, server_port=None)
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