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(
"""
""",
unsafe_allow_html=True,
)
st.sidebar.markdown(
"""
GitHub | Project Report
|
""",
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)