app.py

import pickle
import gradio as gr
from datasets import load_dataset
from transformers import AutoModel, AutoFeatureExtractor
import wikipedia
import pynndescent
import numpy as np

# Only runs once when the script is first run.
with open("slugs_1024_new.pickle", "rb") as handle:
    index = pickle.load(handle)
'''
embs= np.load('slugs_embeddings_1024k.npy', 'r')
index = pynndescent.NNDescent(embs, metric="cosine")
index.prepare()
'''

# Load model for computing embeddings.
feature_extractor = AutoFeatureExtractor.from_pretrained("sasha/autotrain-sea-slug-similarity-2498977005")
model = AutoModel.from_pretrained("sasha/autotrain-sea-slug-similarity-2498977005")

# Candidate images.
dataset = load_dataset("sasha/australian_sea_slugs")
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()
    logits = results[1][0].tolist()
    images = ds.select(inx)["image"]
    captions = ds.select(inx)["label"]
    images_with_captions = [(i, c) for i, c in zip(images,captions)]
    labels_with_probs = dict(zip(captions,logits))
    labels_with_probs = {k: 1- v for k, v in labels_with_probs.items()}
    try:
    	description = wikipedia.summary(captions[0], sentences = 1)
    	description = "### " + description
    	url = wikipedia.page(captions[0]).url
    	url = " You can learn more about your slug [here](" + str(url) + ")!"
    	description = description + url
    except:
    	description = "### Sea slugs, or Nudibranchs, are marine invertebrates that often live in reefs underwater. They have an enormous variation in body shape, color, and size."
    	url = "https://en.wikipedia.org/wiki/Sea_slug"
    	url = " You can learn more about sea slugs [here](" + str(url) + ")!"
    	description = description + url
    return images_with_captions, labels_with_probs, description
    
    

with gr.Blocks() as demo:
	gr.Markdown("# Which Sea Slug Am I ? 🐌")
	gr.Markdown("## Use this Space to find your sea slug, based on the [Nudibranchs of the Sunshine Coast Australia dataset](https://huggingface.co/datasets/sasha/australian_sea_slugs)!")
	with gr.Row():
		with gr.Column(min_width= 900):
			inputs = gr.Image(shape=(800, 1600))
			btn = gr.Button("Find my sea slug 🐌!")
			description = gr.Markdown()
			
		with gr.Column():
			outputs=gr.Gallery().style(grid=[2], height="auto")
			labels = gr.Label()
			
	gr.Markdown("### Image Examples")
	gr.Examples(
	examples=["elton.jpg", "ken.jpg", "gaga.jpg", "taylor.jpg"],
	inputs=inputs,
	outputs=[outputs,labels],
	fn=query,
	cache_examples=True,
	)
	btn.click(query, inputs, [outputs, labels, description])
	
demo.launch()