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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
# @st.cache(allow_output_mutation=True)
# def get_model():
# # load base LLM model and tokenizer
# model_id = "niclasfw/schlager-bot-004"
# tokenizer = AutoTokenizer.from_pretrained(model_id)
# model = AutoModelForCausalLM.from_pretrained(
# model_id,
# # low_cpu_mem_usage=True,
# # torch_dtype=torch.float16,
# # load_in_4bit=True,
# )
# return tokenizer, model
# tokenizer, model = get_model()
model_id = "niclasfw/bloom-1b5-schlager-bot-004"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline(task="text-generation", model=model_id, tokenizer=model_id)
st.title('Schlager Bot')
user_input = st.text_area('Enter verse (minimum of 15 words): ')
button = st.button('Generate Lyrics')
if user_input and button:
prompt = f"""### Instruction:
Benuzte den gegebenen Input um ein Schlager Lied zu schreiben.
### Input:
{user_input}
### Response:
"""
output = generator(prompt, do_sample=True, max_new_tokens=500, top_p=0.75, temperature=0.95, top_k=15)
# st.write("Prompt: ", user_input)
# input = tokenizer(prompt, padding=True, return_tensors="pt")
# generate_ids = model.generate(input.input_ids, max_length=500, top_p=0.75, temperature=0.95, top_k=15)
# output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
# input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)
# outputs = model.generate(input_ids=input_ids, pad_token_id=tokenizer.eos_token_id, max_new_tokens=500, do_sample=True, top_p=0.75, temperature=0.95, top_k=15)
st.write(output)
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