import json
import math
import random
import os
import streamlit as st

st.set_page_config(page_title="HuggingArtists")


st.title("HuggingArtists")
st.sidebar.markdown(
    """
<style>
.aligncenter {
    text-align: center;
}
</style>
<p class="aligncenter">
    <img src="https://raw.githubusercontent.com/AlekseyKorshuk/huggingartists/master/img/logo.jpg" width="420" />
</p>
""",
    unsafe_allow_html=True,
)
st.sidebar.markdown(
    """
<style>
.aligncenter {
    text-align: center;
}
</style>

<p style='text-align: center'>
<a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank">GitHub</a> | <a href="https://wandb.ai/huggingartists/huggingartists/reportlist" target="_blank">Project Report</a>
</p>

<p class="aligncenter">
    <a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank"> 
        <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social"/>
    </a>  <a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" target="_blank"> 
        <img src="https://colab.research.google.com/assets/colab-badge.svg"/>
    </a>
</p>
    """,
    unsafe_allow_html=True,
)


st.sidebar.header("SETTINGS")
num_sequences = st.sidebar.number_input(
    "Number of sequences to generate",
    min_value=1,
    value=5,
    help="The amount of generated texts",
)
min_length = st.sidebar.number_input(
    "Minimum length of the sequence",
    min_value=1,
    value=100,
    help="The minimum length of the sequence to be generated",
)
max_length= st.sidebar.number_input(
    "Maximum length of the sequence",
    min_value=1,
    value=160,
    help="The maximum length of the sequence to be generated",
)
temperature = st.sidebar.slider(
    "Temperature",
    min_value=0.0,
    max_value=3.0,
    step=0.01,
    value=1.0,
    help="The value used to module the next token probabilities",
)
top_p = st.sidebar.slider(
    "Top-P",
    min_value=0.0,
    max_value=1.0,
    step=0.01,
    value=0.95,
    help="If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
)

top_k= st.sidebar.number_input(
    "Top-K",
    min_value=0,
    value=50,
    step=1,
    help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
)

caption = (
    "`HuggingArtists` - Train a model to generate lyrics"
    ""
    "In HuggingArtists, we can generate lyrics by a specific artist. This was made by fine-tuning a pre-trained HuggingFace Transformer on parsed datasets from Genius."
)
st.markdown(caption)