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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> | |
</p> | |
<p class="aligncenter"> | |
<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 = ( | |
"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("`HuggingArtists` - Train a model to generate lyrics 🎵") | |
st.markdown(caption) | |
artist_name = st.text_input("Artist name:", "Eminem") | |
start = st.text_input("Beginning of the song:", "But for me to rap like a computer") | |