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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() |