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import streamlit as st
from datasets import load_dataset
import pandas as pd
def test_v01():
# adding the text that will show in the text box as default
default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!"
sent = st.text_area("Text", default_value, height = 275)
max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)
return num_return_sequences
def test_v02():
dataset = load_dataset("merve/poetry", streaming=True)
print(dataset)
df = pd.DataFrame.from_dict(dataset["train"])
st.write("Most appearing words including stopwords")
# st.bar_chart(words[0:50])
# st.write("Number of poems for each author")
# sns.catplot(x="author", data=df, kind="count", aspect = 4)
# plt.xticks(rotation=90)
# st.pyplot()
return st
test_v02() |