te.recipe.rewrite-ai-search.0.19.0.source-code.get_centers.py Maven / Gradle / Ivy
#
# Copyright 2024 the original author or authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from transformers import AutoModel, AutoTokenizer, logging # 4.29.2
import gradio as gr # 3.23.0
logging.set_verbosity_error()
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import json
from sklearn.metrics import pairwise_distances_argmin_min
def string_to_float_array(str, delimiter=";"):
return [float(f) for f in str.split(delimiter)]
def get_centers(embeddings, number_of_centers):
number_of_centers = int(number_of_centers)
embeddings = json.loads(embeddings)
df = pd.DataFrame({'embedding': [embedding for embedding in embeddings]})
df.drop_duplicates("embedding", inplace=True)
embds = np.array(list(df["embedding"]))
k = number_of_centers
if k==-1:
k = max(10, int(len(df)/200))
if k > len(df):
k = len(df)
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10).fit(embds)
# Find the closest data points to each centroid
closest, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_, embds)
return str(closest.tolist())
gr.Interface(fn=get_centers, inputs=["text", "number"], outputs="text").launch(server_port=7876)