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import streamlit as st
import requests
import os
import transformers
from transformers import AutoModelWithLMHead, AutoTokenizer, AutoModelForCausalLM, pipeline
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained("samroni/model_gpt")
model = AutoModelForCausalLM.from_pretrained("samroni/model_gpt")
pipe = pipeline('text2text-generation', model="samroni/model_gpt", tokenizer=tokenizer)
prompt = """
word: risk
poem using word: And then the day came,
when the risk
to remain tight
in a bud
was more painful
than the risk
it took
to blossom.
word: bird
poem using word: She sights a bird, she chuckles
She flattens, then she crawls
She runs without the look of feet
Her eyes increase to Balls.
word: """
examples = [["sungai"], ["malam"], ["pepohonan"],["meja"],["tertawa"]]
kata = st.text_input("isikan kata", 0)
b1 = st.button("generate", 0)
hasil = st.text_area("hasil", 0)
def puisi_generate(prompt):
ouputs = pipe(prompt,
num_beams=5,
early_stopping=True,
repetition_penalty=20.0,
num_return_sequences=1)
return ouputs[0]['generated_text']
if b1:
with st.spinner("Generating code..."):
# use threading
puisi_generate(
prompt=prompt,
)
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