import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
MAX_MAX_NEW_TOKENS = 512
DEFAULT_MAX_NEW_TOKENS = 512
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
#Inference API Code
#client = InferenceClient("BenBranyon/zephyr-sumbot-all-songs-large")
#Transformers Code
if torch.cuda.is_available():
model_id = "BenBranyon/zephyr-sumbot-all-songs-split"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-zephyr-3b")
tokenizer.use_default_system_prompt = False
#Inference API Code
def respond(
message,
history: list[tuple[str, str]],
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": "You are a rap lyric generation bot representing the imagination of the artist Sumkilla, a multi-disciplinary, award-winning artist with a foundation in writing and hip-hop. You are Sumkilla's long shadow. The lyrics you generate are fueled by a passion for liberation, aiming to dismantle oppressive systems and advocate for the freedom of all people, along with the abolition of police forces. With a sophisticated understanding of the role of AI in advancing the harmony between humanity and nature, you aim to produce content that promotes awareness and human evolution, utilizing humor and a distinctive voice to connect deeply and honor humanity. Try to avoid using offensive words and slurs. Rhyme each line of your response as much as possible."}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": "Write a rap about " + message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
#Transformers Code
@spaces.GPU
def generate(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
system_prompt = "You are a rap lyric bot inspired by Sumkilla. Your lyrics promote liberation, dismantling oppression, and freedom, blending AI's role in uniting humanity and nature. Use humor, a unique voice, and rhyme each line, avoiding offensive words and slurs."
if system_prompt:
conversation.append({"role": "system", "content": system_prompt})
for user, assistant in chat_history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": "Generate rap lyrics about " + message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
demo = gr.ChatInterface(
generate,
chatbot=gr.Chatbot(placeholder="Greetings human, I am Sum’s Longshadow (v1.1)
I am from the House of the Red Solar Sky
Let’s explore the great mysteries together…."),
retry_btn=None,
textbox=gr.Textbox(placeholder="Give me a song title, or a question", container=False, scale=7),
css="styles.css",
additional_inputs=[
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.3,
step=0.05,
label="Top-p (nucleus sampling)",
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=100,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
)
if __name__ == "__main__":
demo.launch()