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import pickle
import gradio as gr
from datasets import load_dataset
from transformers import AutoModel, AutoFeatureExtractor
seed = 42
# Only runs once when the script is first run.
with open("index_768.pickle", "rb") as handle:
index = pickle.load(handle)
# Load model for computing embeddings.
feature_extractor = AutoFeatureExtractor.from_pretrained("sasha/autotrain-butterfly-similarity-2490576840")
model = AutoModel.from_pretrained("sasha/autotrain-butterfly-similarity-2490576840")
# Candidate images.
dataset = load_dataset("sasha/butterflies_10k_names_multiple")
ds = dataset["train"]
def query(image, top_k=4):
inputs = feature_extractor(image, return_tensors="pt")
model_output = model(**inputs)
embedding = model_output.pooler_output.detach()
results = index.query(embedding, k=top_k)
inx = results[0][0].tolist()
images = ds.select(inx)["image"]
captions = ds.select(inx)["name"]
images_with_captions = [(i, c) for i, c in zip(images,captions)]
return images_with_captions
title = "Find my Butterfly 🦋"
description = "Use this Space to find your butterfly, based on the [iNaturalist butterfly dataset](https://huggingface.co/datasets/huggan/inat_butterflies_top10k)!"
gr.Interface(
query,
inputs=[gr.Image(type="pil")],
outputs=gr.Gallery().style(grid=[2], height="auto"),
title=title,
description=description,
examples=[["elton.jpg"],["ken.jpg"],["gaga.jpg"],["taylor.jpg"]],
).launch()
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