"""
Thanks to Freddy Boulton (https://github.com/freddyaboulton) for helping with this.
"""


import pickle

import gradio as gr
from datasets import load_dataset
from transformers import AutoModel

from similarity_utils import BuildLSHTable

seed = 42

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

# Load model for computing embeddings.
model_ckpt = "nateraw/vit-base-beans"
model = AutoModel.from_pretrained(model_ckpt)
lsh_builder = BuildLSHTable(model)
lsh_builder.lsh = loaded_lsh

# Candidate images.
dataset = load_dataset("beans")
candidate_dataset = dataset["train"].shuffle(seed=seed)


def query(image, top_k):
    results = lsh_builder.query(image)

    # Should be a list of string file paths for gr.Gallery to work
    images = []
    # List of labels for each image in the gallery
    labels = []

    candidates = []
    overlaps = []

    for idx, r in enumerate(sorted(results, key=results.get, reverse=True)):
        if idx == top_k:
            break
        image_id, label = r.split("_")[0], r.split("_")[1]
        candidates.append(candidate_dataset[int(image_id)]["image"])
        labels.append(label)
        overlaps.append(results[r])

    candidates.insert(0, image)
    labels.insert(0, label)

    for i, candidate in enumerate(candidates):
        filename = f"{i}.png"
        candidate.save(filename)
        images.append(filename)

    # The gallery component can be a list of tuples, where the first element is a path to a file
    # and the second element is an optional caption for that image
    return list(zip(images, labels))


# You can set the type of gr.Image to be PIL, numpy or str (filepath)
# Not sure what the best for this demo is.
gr.Interface(
    query,
    inputs=[gr.Image(), gr.Slider(value=5, minimum=1, maximum=10, step=1)],
    outputs=gr.Gallery(),
).launch()