import pickle

import numpy as np
import pandas as pd
import streamlit as st
import torch
from sentence_transformers.util import semantic_search
from transformers import AutoModel, AutoProcessor

st.title("My CLIP Model Test")
st.markdown(
    "[Unsplash Lite dataset](https://unsplash.com/data)에서 입력 텍스트와 가장 유사한 이미지를 검색합니다."
)


@st.cache(allow_output_mutation=True, show_spinner=False)
def get_dual_encoder_model(name: str):
    with st.spinner("Loading model..."):
        model = AutoModel.from_pretrained(name).eval()
        processor = AutoProcessor.from_pretrained(name)
    return model, processor


@st.cache(allow_output_mutation=True, show_spinner=False)
def get_clip_model(name: str):
    with st.spinner("Loading model..."):
        model = AutoModel.from_pretrained(name).eval()
        processor = AutoProcessor.from_pretrained(name)
    return model, processor


model_list = [
    "Bingsu/clip-vit-base-patch32-ko",
    "Bingsu/clip-vit-large-patch14-ko",
    "openai/clip-vit-base-patch32",
    "openai/clip-vit-base-patch16",
    "openai/clip-vit-large-patch14",
    "Bingsu/vitB32_bert_ko_small_clip",
]
model_type = st.radio("Select model", model_list)

if model_type == "Bingsu/vitB32_bert_ko_small_clip":
    model, processor = get_dual_encoder_model(model_type)
else:
    model, processor = get_clip_model(model_type)

info = pd.read_csv("info.csv")
with open("img_id.pkl", "rb") as f:
    img_id = pickle.load(f)
img_emb = np.load("img_emb.npy")

text = st.text_input("Input Text", value="검은 고양이")
tokens = processor(text=text, return_tensors="pt")

with torch.no_grad():
    text_emb = model.get_text_features(**tokens)

result = semantic_search(text_emb, img_emb, top_k=16)[0]
_result = iter(result)


def get_url() -> str:
    # 몇몇 이미지가 info.csv 데이터에 없습니다.
    while True:
        r = next(_result)
        photo_id = img_id[r["corpus_id"]]
        target_series = info.loc[info["photo_id"] == photo_id, "photo_image_url"]
        if len(target_series) == 0:
            continue
        img_url = target_series.iloc[0]
        return img_url


columns = st.columns(3) + st.columns(3)
for col in columns:
    img_url = get_url()
    col.image(img_url, use_column_width=True)