import pickle
import gradio as gr
from datasets import load_dataset
from transformers import AutoModel, AutoFeatureExtractor


# Only runs once when the script is first run.
with open("slugs_index_1024_cosine.pickle", "rb") as handle:
    index = pickle.load(handle)

# 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()}
    return images_with_captions, labels_with_probs


with gr.Blocks() as demo:
	gr.Markdown("# Find my Sea Slug 🐌")
	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 🐌!")
		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])
	
demo.launch()