v5

app.py

@@ -1,21 +1,11 @@
-print("Hello")
-# import streamlit as st
-# from transformers import pipeline
-# from PIL import Image
-#
-# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-#
-# st.title("Hot Dog? Or Not?")
-#
-# file_name = st.file_uploader("Upload a hot dog candidate image")
-#
-# if file_name is not None:
-#     col1, col2 = st.columns(2)
-#
-#     image = Image.open(file_name)
-#     col1.image(image, use_column_width=True)
-#     predictions = pipeline(image)
-#
-#     col2.header("Probabilities")
-#     for p in predictions:
-#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+import streamlit as st
+
+# 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)