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import json
import random
import requests
from mtranslate import translate
import streamlit as st
MODEL_URL = "https://api-inference.huggingface.co/models/flax-community/papuGaPT2"
PROMPT_LIST = {
"Najsmaczniejszy owoc to...": ["Najsmaczniejszy owoc to "],
"Cześć, mam na imię...": ["Cześć, mam na imię "],
"Największym polskim poetą był...": ["Największym polskim poetą był "],
}
def query(payload, model_url):
data = json.dumps(payload)
print("model url:", model_url)
response = requests.request(
"POST", model_url, headers={}, data=data
)
return json.loads(response.content.decode("utf-8"))
def process(
text: str, model_name: str, max_len: int, temp: float, top_k: int, top_p: float
):
payload = {
"inputs": text,
"parameters": {
"max_new_tokens": max_len,
"top_k": top_k,
"top_p": top_p,
"temperature": temp,
"repetition_penalty": 2.0,
},
"options": {
"use_cache": True,
},
}
return query(payload, model_name)
# Page
st.set_page_config(page_title="papuGaPT2 (Polish GPT-2) Demo")
st.title("papuGaPT2 (Polish GPT-2")
# Sidebar
st.sidebar.subheader("Configurable parameters")
max_len = st.sidebar.number_input(
"Maximum length",
value=100,
help="The maximum length of the sequence to be generated.",
)
temp = st.sidebar.slider(
"Temperature",
value=1.0,
min_value=0.1,
max_value=100.0,
help="The value used to module the next token probabilities.",
)
top_k = st.sidebar.number_input(
"Top k",
value=10,
help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
)
top_p = st.sidebar.number_input(
"Top p",
value=0.95,
help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
)
do_sample = st.sidebar.selectbox(
"Sampling?",
(True, False),
help="Whether or not to use sampling; use greedy decoding otherwise.",
)
# Body
st.markdown(
"""
papuGaPT2 (Polish GPT-2) model trained from scratch on OSCAR dataset.
The models were trained with Jax and Flax using TPUs as part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organised by HuggingFace.
"""
)
model_name = MODEL_name
ALL_PROMPTS = list(PROMPT_LIST.keys()) + ["Custom"]
prompt = st.selectbox("Prompt", ALL_PROMPTS, index=len(ALL_PROMPTS) - 1)
if prompt == "Custom":
prompt_box = "Enter your text here"
else:
prompt_box = random.choice(PROMPT_LIST[prompt])
text = st.text_area("Enter text", prompt_box)
if st.button("Run"):
with st.spinner(text="Getting results..."):
st.subheader("Result")
print(f"maxlen:{max_len}, temp:{temp}, top_k:{top_k}, top_p:{top_p}")
result = process(
text=text,
model_name=model_name,
max_len=int(max_len),
temp=temp,
top_k=int(top_k),
top_p=float(top_p),
)
print("result:", result)
if "error" in result:
if type(result["error"]) is str:
st.write(f'{result["error"]}.', end=" ")
if "estimated_time" in result:
st.write(
f'Please try again in about {result["estimated_time"]:.0f} seconds.'
)
else:
if type(result["error"]) is list:
for error in result["error"]:
st.write(f"{error}")
else:
result = result[0]["generated_text"]
st.write(result.replace("\n", " \n"))
st.text("English translation")
st.write(translate(result, "en", "es").replace("\n", " \n")) |