apps/utils.py

import json
import os
import numpy as np
import streamlit as st
import plotly.express as px
import torch
from torchvision.io import read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
from transformers import BertTokenizerFast

class Toc:

    def __init__(self):
        self._items = []
        self._placeholder = None
    
    def title(self, text):
        self._markdown(text, "h1")

    def header(self, text):
        self._markdown(text, "h2", " " * 2)

    def subheader(self, text):
        self._markdown(text, "h3", " " * 4)
    
    def subsubheader(self, text):
        self._markdown(text, "h4", " " * 8)

    def placeholder(self, sidebar=False):
        self._placeholder = st.sidebar.empty() if sidebar else st.empty()

    def generate(self):
        if self._placeholder:
            self._placeholder.markdown("\n".join(self._items), unsafe_allow_html=True)
    
    def _markdown(self, text, level, space=""):
        key = "".join(filter(str.isalnum, text)).lower()

        st.markdown(f"<{level} id='{key}'>{text}</{level}>", unsafe_allow_html=True)
        self._items.append(f"{space}* <a href='#{key}'>{text}</a>")

class Transform(torch.nn.Module):
    def __init__(self, image_size):
        super().__init__()
        self.transforms = torch.nn.Sequential(
            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
            CenterCrop(image_size),
            ConvertImageDtype(torch.float),
            Normalize(
                (0.48145466, 0.4578275, 0.40821073),
                (0.26862954, 0.26130258, 0.27577711),
            ),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        with torch.no_grad():
            x = self.transforms(x)
        return x


transform = Transform(224)


def get_transformed_image(image):
    if image.shape[-1] == 3 and isinstance(image, np.ndarray):
        image = image.transpose(2, 0, 1)
        image = torch.tensor(image)
    return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()


bert_tokenizer = BertTokenizerFast.from_pretrained("bert-base-multilingual-uncased")


def get_text_attributes(text):
    return bert_tokenizer([text], return_token_type_ids=True, return_tensors="np")


def get_top_5_predictions(logits, answer_reverse_mapping=None):
    indices = np.argsort(logits)[-5:]
    values = logits[indices]
    if answer_reverse_mapping is not None:
        labels = [answer_reverse_mapping[str(i)] for i in indices]
    else:
        labels = bert_tokenizer.convert_ids_to_tokens(indices)
    return labels, values


with open("translation_dict.json") as f:
    translate_dict = json.load(f)


def translate_labels(labels, lang_id):
    translated_labels = []
    for label in labels:
        if label == "<unk>":
            translated_labels.append("<unk>")
        elif lang_id == "en":
            translated_labels.append(label)
        else:
            translated_labels.append(translate_dict[label][lang_id])
    return translated_labels


def plotly_express_horizontal_bar_plot(values, labels):
    fig = px.bar(
        x=values,
        y=labels,
        text=[format(value, ".3%") for value in values],
        title="Top-5 Predictions",
        labels={"x": "Scores", "y": "Answers"},
        orientation="h",
    )
    return fig


def read_markdown(path, parent="./sections/"):
    with open(os.path.join(parent, path)) as f:
        return f.read()