import io
import os
import requests
import zipfile
import natsort
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from pathlib import Path
from stqdm import stqdm
import streamlit as st
from jax import numpy as jnp
import transformers
from transformers import AutoTokenizer
from torchvision.transforms import Compose, CenterCrop, Normalize, Resize, ToTensor
from torchvision.transforms.functional import InterpolationMode
from modeling_hybrid_clip import FlaxHybridCLIP

import utils


@st.cache(hash_funcs={FlaxHybridCLIP: lambda _: None})
def get_model():
    return FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian")


@st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None})
def get_tokenizer():
    return AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-uncased", cache_dir="./", use_fast=True)


@st.cache(suppress_st_warning=True)
def download_images():
    # from sentence_transformers import SentenceTransformer, util
    img_folder = "photos/"
    if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
        os.makedirs(img_folder, exist_ok=True)

        photo_filename = "unsplash-25k-photos.zip"
        if not os.path.exists(photo_filename):  # Download dataset if does not exist
            print(f"Downloading {photo_filename}...")
            response = requests.get(f"http://sbert.net/datasets/{photo_filename}", stream=True)
            total_size_in_bytes= int(response.headers.get('content-length', 0))
            block_size = 1024 #1 Kb
            progress_bar = stqdm(total=total_size_in_bytes)  # , unit='iB', unit_scale=True
            content = io.BytesIO()
            for data in response.iter_content(block_size):
                progress_bar.update(len(data))
                content.write(data)
            progress_bar.close()
            z = zipfile.ZipFile(content)
            # content.close()
            print("Extracting the dataset...")
            z.extractall(path=img_folder)
    print("Done.")


@st.cache()
def get_image_features():
    return jnp.load("static/features/features.npy")
    
    
def read_markdown_file(markdown_file):
    return Path(markdown_file).read_text()


"""

# 👋 Ciao!

# CLIP Italian Demo 
## HF-Flax Community Week

In this demo you can search for images in the Unsplash 25k Photos dataset.

🤌 Italian mode on! 🤌
  
"""

query = st.text_input("Insert an italian query text here...")
if query:
    with st.spinner("Computing in progress..."):
        model = get_model()
        download_images()

        image_features = get_image_features()

        model = get_model()
        tokenizer = get_tokenizer()

        image_size = model.config.vision_config.image_size

        val_preprocess = Compose(
            [
                Resize([image_size], interpolation=InterpolationMode.BICUBIC),
                CenterCrop(image_size),
                ToTensor(),
                Normalize(
                    (0.48145466, 0.4578275, 0.40821073),
                    (0.26862954, 0.26130258, 0.27577711),
                ),
            ]
        )

        dataset = utils.CustomDataSet("photos/", transform=val_preprocess)

        image_paths = utils.find_image(
            query, model, dataset, tokenizer, image_features, n=2
        )

    st.image(image_paths)

intro_markdown = read_markdown_file("readme.md")
st.markdown(intro_markdown, unsafe_allow_html=True)