Update app.py

app.py

@@ -2,26 +2,17 @@ import streamlit as st
 import json
 from datasets import load_dataset
 
-st.set_page_config(page_title="Large GitHub Issues", layout="wide")
-st.title("Issues with large text")
+st.set_page_config(page_title="Kaggle Notebooks (as scripts) visualization", layout="wide")
+st.title("Kaggle Notebooks (as scripts) visualization")
 
 @st.cache()
 def load_data():
-    ds = load_dataset("loubnabnl/large-text-issues", split="train")
+    ds = load_dataset("loubnabnl/kaggle-scripts", split="train")
     return ds
 
-def print_issue(events):
-    for event in events:
-        st.markdown("""---""")
-        masked_author = f"masked as {event['masked_author']}" if "masked_author" in event else ""
-        st.markdown(f"**Author:** {event['author']} {masked_author}, {event['action']} {event['type']} with title: {event['title']}.\
-        Text size is: **{event['size']}** and Number of lines is: **{event['nb_lines']}**")
-        st.code(f"{event['text']}", language="none")
-
 samples = load_data()
 col1, _ = st.columns([2, 4])
 with col1:
-    index_example = st.number_input(f"Index of the chosen conversation from the existing {len(samples)}", min_value=0, max_value=len(samples)-1, value=0, step=1)
+    index_example = st.number_input(f"Index of the chosen sample from the existing {len(samples)}", min_value=0, max_value=len(samples)-1, value=0, step=1)
 
-st.write(f"Issue size: {samples[index_example]['text_size_no_bots']}\n\n")
-print_issue(samples[index_example]["events"])
+st.code(samples[index_example]["script"])