app.py

import streamlit as st
import json
from datasets import load_dataset

st.set_page_config(page_title="Kaggle Notebooks inspection", layout="wide")

st.markdown("<h1 style='text-align: center; color: #00BFFF;'>Kaggle Notebooks inspection 🔍</h1>", unsafe_allow_html=True)

st.markdown("Here you can inspect Kaggle notebooks that were converted to python scripts and deduplicated.")
@st.cache()
def load_data(upvote=0):
    ds = load_dataset("loubnabnl/subset_kaggle_scripts", split="train")
    ds = ds.filter(lambda x: x["upvotes"] >= upvote)
    return ds

def show_extra_info(e):
    kv = json.loads(e["kversion"])[0]
    try:
        data_v = json.loads(e["dataset_versions"])[0]
    except:
        data_v = ""
    if data_v:
        data_title = data_v["Title"]
        import numpy as np
        description = data_v["Description"] if str(data_v["Description"]) != 'nan' else "<empty_description>"
        data_text = f"<br>**📚 Dataset description:**<br>Title: **{data_title}**, described as: {description}."
    else:
        data_text = ""
    
    text = f"The title of the notebook is: **{kv['Title']}** and it has **{kv['TotalVotes']} ⬆️ upvotes**.{data_text}"
    return text


vote = st.sidebar.slider("Minimum notebook ⬆️ upvotes", min_value=0, max_value=100, step=1, value=0)
samples = load_data(vote)
index_example = st.sidebar.number_input(f"Choose a sample from the existing {len(samples)} notebooks:", min_value=0, max_value=max(0, len(samples)-1), value=0, step=1)

st.markdown(show_extra_info(samples[index_example]), unsafe_allow_html=True)
st.code(samples[index_example]["script"])