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import argparse
import logging
import datasets
import gradio as gr
logging.disable(logging.CRITICAL)
model_name = "dangvantuan/sentence-camembert-large"
model = gr.load(model_name)
dataset = datasets.load_dataset("json", data_files=["./data/dataset.json"], split="train")
dataset.load_faiss_index("embeddings", "index.faiss")
def search(query, k):
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(
search,
inputs=[
gr.inputs.Textbox(label="Query"),
gr.inputs.Number(label="K", default=3, min_value=1, max_value=10),
],
outputs=[
gr.outputs.Textbox(label="Title"),
gr.outputs.Textbox(label="Transcript"),
gr.outputs.Textbox(label="Link"),
],
title="Camembert and Faiss-powered Search Engine",
description="Search through a dataset using Camembert and Faiss",
theme="light",
layout="vertical",
)
iface.launch()
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