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