Create app.py

app.py

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+import os
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import dask.dataframe as dd
+from dotenv import load_dotenv
+from itertools import combinations
+from collections import defaultdict
+
+# Load environment variables
+load_dotenv()
+
+# Configuration from environment variables
+FILE_UPLOAD_LIMIT = int(os.getenv('FILE_UPLOAD_LIMIT', 200))
+EXECUTION_TIME_LIMIT = int(os.getenv('EXECUTION_TIME_LIMIT', 300))
+RESOURCE_LIMIT = int(os.getenv('RESOURCE_LIMIT', 1024))  # in MB
+DATA_DIR = os.getenv('DATA_DIR', './data')
+CONFIG_FLAG = os.getenv('CONFIG_FLAG', 'default')
+
+# Main application logic
+def main():
+    st.title("CyberOps Dashboard")
+
+    # Sidebar for user inputs
+    st.sidebar.header("Options")
+
+    # Option to select a CSV file
+    uploaded_file = st.sidebar.file_uploader("Select a CSV file:", type=["csv"])
+
+    if uploaded_file:
+        @st.cache_data
+        def load_csv(file):
+            return pd.read_csv(file)
+
+        @st.cache_data
+        def load_dask_csv(file):
+            return dd.read_csv(file)
+
+        if os.path.getsize(uploaded_file) < RESOURCE_LIMIT * 1024 * 1024:
+            df = load_csv(uploaded_file)
+        else:
+            df = load_dask_csv(uploaded_file)
+
+        if not df.empty:
+            st.write("Data Preview:")
+            st.dataframe(df.compute() if isinstance(df, dd.DataFrame) else df)
+
+            # Select columns for plotting
+            x_column = st.sidebar.selectbox('Select X-axis:', df.columns)
+            y_column = st.sidebar.selectbox('Select Y-axis:', df.columns)
+
+            # Plotting
+            fig, ax = plt.subplots()
+            ax.plot(df[x_column], df[y_column], marker='o')
+            ax.set_xlabel(x_column)
+            ax.set_ylabel(y_column)
+            ax.set_title(f"{y_column} vs {x_column}")
+            st.pyplot(fig)
+
+            # Combinatorial analysis
+            col_combinations = st.sidebar.multiselect('Select columns for combinations:', df.columns)
+            if col_combinations:
+                st.write("Column Combinations:")
+                comb = list(combinations(col_combinations, 2))
+                st.write(comb)
+
+            # Grouping and aggregation
+            group_by_column = st.sidebar.selectbox('Select column to group by:', df.columns)
+            if group_by_column:
+                grouped_df = df.groupby(group_by_column).agg(list)
+                st.write("Grouped Data:")
+                st.dataframe(grouped_df.compute() if isinstance(grouped_df, dd.DataFrame) else grouped_df)
+
+if __name__ == "__main__":
+    main()