import io
import csv
import sys
import pickle
from collections import Counter
import numpy as np
import gradio as gr
import gdown
import torchvision
from torchvision.datasets import ImageFolder
from PIL import Image

from SimSearch import FaissCosineNeighbors, SearchableTrainingSet
from ExtractEmbedding import QueryToEmbedding
from CHMCorr import chm_classify_and_visualize
from visualization import plot_from_reranker_output

csv.field_size_limit(sys.maxsize)

concat = lambda x: np.concatenate(x, axis=0)

# # Embeddings
# gdown.cached_download(
#     url="https://static.taesiri.com/chm-corr/embeddings.pickle",
#     path="./embeddings.pickle",
#     quiet=False,
#     md5="002b2a7f5c80d910b9cc740c2265f058",
# )

# # embeddings
# # gdown.download(id="116CiA_cXciGSl72tbAUDoN-f1B9Frp89")

# # labels
# gdown.download(id="1SDtq6ap7LPPpYfLbAxaMGGmj0EAV_m_e")

# # CUB training set
# gdown.cached_download(
#     url="https://static.taesiri.com/chm-corr/CUB_train.zip",
#     path="./CUB_train.zip",
#     quiet=False,
#     md5="1bd99e73b2fea8e4c2ebcb0e7722f1b1",
# )

# # EXTRACT training set
# torchvision.datasets.utils.extract_archive(
#     from_path="CUB_train.zip",
#     to_path="data/",
#     remove_finished=False,
# )

# # CHM Weights
# gdown.cached_download(
#     url="https://static.taesiri.com/chm-corr/pas_psi.pt",
#     path="pas_psi.pt",
#     quiet=False,
#     md5="6b7b4d7bad7f89600fac340d6aa7708b",
# )


# Caluclate Accuracy
with open(f"./embeddings.pickle", "rb") as f:
    Xtrain = pickle.load(f)
# FIXME: re-run the code to get the embeddings in the right format
with open(f"./labels.pickle", "rb") as f:
    ytrain = pickle.load(f)

searcher = SearchableTrainingSet(Xtrain, ytrain)
searcher.build_index()

# Extract label names
training_folder = ImageFolder(root="./data/train/")
id_to_bird_name = {
    x[1]: x[0].split("/")[-2].replace(".", " ") for x in training_folder.imgs
}


def search(query_image, draw_arcs, searcher=searcher):
    query_embedding = QueryToEmbedding(query_image)
    scores, indices, labels = searcher.search(query_embedding, k=50)

    result_ctr = Counter(labels[0][:20]).most_common(5)

    top1_label = result_ctr[0][0]
    top_indices = []

    for a, b in zip(labels[0][:20], indices[0][:20]):
        if a == top1_label:
            top_indices.append(b)

    gallery_images = [training_folder.imgs[int(X)][0] for X in top_indices[:5]]
    predicted_labels = {id_to_bird_name[X[0]]: X[1] / 20.0 for X in result_ctr}

    # CHM Prediction
    kNN_results = (top1_label, result_ctr[0][1], gallery_images)
    support_files = [training_folder.imgs[int(X)][0] for X in indices[0]]
    support_labels = [training_folder.imgs[int(X)][1] for X in indices[0]]

    support = [support_files, support_labels]

    chm_output = chm_classify_and_visualize(
        query_image, kNN_results, support, training_folder
    )

    fig = plot_from_reranker_output(chm_output, draw_arcs=draw_arcs)

    # Resize the output

    img_buf = io.BytesIO()
    fig.savefig(img_buf, format="jpg")
    image = Image.open(img_buf)
    width, height = image.size
    new_width = width
    new_height = height

    left = (width - new_width) / 2
    top = (height - new_height) / 2
    right = (width + new_width) / 2
    bottom = (height + new_height) / 2

    viz_image = image.crop((left + 540, top + 40, right - 492, bottom - 100))

    return viz_image, predicted_labels


blocks = gr.Blocks()

with blocks:
    gr.Markdown(""" # CHM-Corr DEMO""")
    gr.Markdown(""" ### Parameters: N=50, k=20 - Using ResNet50 features""")

    # with gr.Row():
    input_image = gr.Image(type="filepath")
    with gr.Column():
        arcs_checkbox = gr.Checkbox(label="Draw Arcs")
    run_btn = gr.Button("Classify")

    # with gr.Column():
    gr.Markdown(""" ### CHM-Corr Output """)
    viz_plot = gr.Image(type="pil")
    gr.Markdown(""" ### kNN Predicted Labels """)
    predicted_labels = gr.Label(label="kNN Prediction")

    run_btn.click(
        search,
        inputs=[input_image, arcs_checkbox],
        outputs=[viz_plot, predicted_labels],
    )

if __name__ == "__main__":
    blocks.launch(
        debug=True,
        enable_queue=True,
    )