v10

app.py

@@ -1,11 +1,25 @@
 import streamlit as st
+from datasets import load_dataset
 
-# 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!"
+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)
+    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)
+
+def test_v02():
+    dataset = load_dataset("merve/poetry", streaming=True)
+    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()
+
+test_v02()