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import os
import warnings
import requests
import torch
import streamlit as st
from streamlit_lottie import st_lottie
from transformers import AutoTokenizer, AutoModelWithLMHead
warnings.filterwarnings("ignore")
st.set_page_config(layout='centered', page_title='GPT2-Horoscopes')
def load_lottieurl(url: str):
# https://github.com/tylerjrichards/streamlit_goodreads_app/blob/master/books.py
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_book = load_lottieurl('https://assets2.lottiefiles.com/packages/lf20_WL3aE7.json')
st_lottie(lottie_book, speed=1, height=200, key="initial")
st.markdown('# GPT2-Horoscopes!')
st.markdown("""
Hello! This lovely app lets GPT-2 write awesome horoscopes for you. All you need to do
is select your sign and choose the horoscope category :)
""")
st.markdown("""
*If you are interested in the fine-tuned model, you can visit the [Model Hub](https://huggingface.co/shahp7575/gpt2-horoscopes) or
my [GitHub Repo](https://github.com/shahp7575/gpt2-horoscopes).*
""")
@st.cache(allow_output_mutation=True, max_entries=1)
def download_model():
tokenizer = AutoTokenizer.from_pretrained('shahp7575/gpt2-horoscopes')
model = AutoModelWithLMHead.from_pretrained('shahp7575/gpt2-horoscopes')
return model, tokenizer
model, tokenizer = download_model()
def make_prompt(category):
return f"<|category|> {category} <|horoscope|>"
def generate(prompt, model, tokenizer, temperature, num_outputs, top_k):
sample_outputs = model.generate(prompt,
#bos_token_id=random.randint(1,30000),
do_sample=True,
top_k=top_k,
max_length = 300,
top_p=0.95,
temperature=temperature,
num_return_sequences=num_outputs)
return sample_outputs
with st.beta_container():
horoscope = st.selectbox("Choose Your Sign: ", ('Aquarius', 'Pisces', 'Aries',
'Taurus', 'Gemini', 'Cancer',
'Leo', 'Virgo', 'Libra',
'Scorpio', 'Sagittarius', 'Capricorn'), index=0)
choice = st.selectbox("Choose Category:", ('general', 'career', 'love', 'wellness', 'birthday'),
index=0, )
temp_slider = st.slider("Temperature (Higher Value = More randomness)", min_value=0.01, max_value=1.0, value=0.95)
if st.button('Generate Horoscopes!'):
prompt = make_prompt(choice)
prompt_encoded = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
with st.spinner('Generating...'):
sample_output = generate(prompt_encoded, model, tokenizer, temperature=temp_slider, num_outputs=1, top_k=40)
final_out = tokenizer.decode(sample_output[0], skip_special_tokens=True)
st.write(final_out[len(choice)+2:])
else: pass
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