Spaces:
Running
Running
File size: 1,369 Bytes
45d6b11 2014880 45d6b11 2014880 45d6b11 a6fecbc 45d6b11 2014880 45d6b11 2014880 45d6b11 2014880 45d6b11 2014880 45d6b11 2014880 45d6b11 2014880 45d6b11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import logging
import gradio as gr
import datasets
import sentence_transformers
logging.disable(logging.CRITICAL)
model = sentence_transformers.SentenceTransformer(
"dangvantuan/sentence-camembert-large", device="cuda"
)
dataset = datasets.load_dataset("json", data_files=["./dataset.json"], split="train")
dataset.load_faiss_index("embeddings", "index.faiss")
def search(query: str, k: int):
query_embedding = model.encode(query)
_, retrieved_examples = dataset.get_nearest_examples(
"embeddings",
query_embedding,
k=k,
)
results = []
for text, start, end, title, url in zip(
retrieved_examples["text"],
retrieved_examples["start"],
retrieved_examples["end"],
retrieved_examples["title"],
retrieved_examples["url"],
):
start = start
end = end
result = {
"title": title,
"transcript": f"[{str(start)+' ====> '+str(end)}] {text}",
"link": url
}
results.append(result)
return results
iface = gr.Interface(
fn=search,
inputs=["text", "number"],
outputs=gr.outputs.JSON(),
title="Search Dataset",
description="Search a dataset using Camembert and Faiss.",
example=[
"Enter a query to search for.",
5
]
)
if __name__ == "__main__":
iface.launch()
|