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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, | |
| ) | |