Create app.py

https://github.com/mito-ds/mito-for-streamlit-demo/blob/main/main.py

app.py

@@ -0,0 +1,45 @@
+from datetime import datetime, timedelta
+
+import pandas as pd
+import streamlit as st
+from mitosheet.streamlit.v1 import spreadsheet
+from mitosheet.streamlit.v1.spreadsheet import _get_mito_backend
+
+st.set_page_config(layout="wide")
+
+@st.cache_data
+def get_tesla_data():
+    df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tesla-stock-price.csv')
+    df = df.drop(0)
+    df['volume'] = df['volume'].astype(float)
+    return df
+
+tesla_data = get_tesla_data()
+
+new_dfs, code = spreadsheet(tesla_data)
+code = code if code else "# Edit the spreadsheet above to generate code"
+st.code(code)
+
+def clear_mito_backend_cache():
+    _get_mito_backend.clear()
+
+# Function to cache the last execution time - so we can clear periodically
+@st.cache_resource
+def get_cached_time():
+    # Initialize with a dictionary to store the last execution time
+    return {"last_executed_time": None}
+
+def try_clear_cache():
+
+    # How often to clear the cache
+    CLEAR_DELTA = timedelta(hours=12)
+
+    current_time = datetime.now()
+    cached_time = get_cached_time()
+
+    # Check if the current time is different from the cached last execution time
+    if cached_time["last_executed_time"] is None or cached_time["last_executed_time"] + CLEAR_DELTA < current_time:
+        clear_mito_backend_cache()
+        cached_time["last_executed_time"] = current_time
+
+try_clear_cache()