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import random
import torch
from rapgpt.config import Config
from rapgpt.encoder import Encoder
from rapgpt.model import HFHubTransformerModel
import gradio as gr
from huggingface_hub import hf_hub_download
if __name__ == "__main__":
artists_tokens = hf_hub_download(
repo_id="hugojarkoff/rapgpt", filename="artists_tokens.txt", repo_type="model"
)
config_file = hf_hub_download(
repo_id="hugojarkoff/rapgpt", filename="config.toml", repo_type="model"
)
with open(artists_tokens, "r") as f:
artists_tokens = {
line.split(":")[0]: int(line.split(":")[1].rstrip("\n")) for line in f
}
config = Config.load_from_toml(config_file)
encoder = Encoder(config=config)
model = HFHubTransformerModel.from_pretrained("hugojarkoff/rapgpt")
def predict(
lyrics_prompt: str,
new_tokens: int,
artist_token: int,
seed: int = 42,
):
# Set Seed
random.seed(seed)
torch.manual_seed(seed)
# Predict
sample_input = encoder.encode_data(lyrics_prompt)
sample_input = torch.tensor(sample_input).unsqueeze(0)
output = model.generate(
x=sample_input,
new_tokens=new_tokens,
artist_token=artist_token,
)
return encoder.decode_data(output[0].tolist())
gradio_app = gr.Interface(
predict,
inputs=[
gr.Textbox(
value="ekip",
label="Lyrics prompt",
info="rapGPT will continue this prompt",
),
gr.Number(
value=100,
maximum=100,
label="New tokens to generate",
info="Number of new tokens to generate (limited to 100)",
),
gr.Dropdown(
value="freeze corleone",
choices=artists_tokens.keys(),
type="index",
label="Artist",
info="Which artist style to generate",
),
gr.Number(
value=42, label="Random seed", info="Change for different results"
),
],
outputs=[gr.TextArea(label="Generated Lyrics")],
title="rapGPT",
)
gradio_app.launch()
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