Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
from datetime import datetime, time

st.title("Timeseries Data Filter")

# File upload
uploaded_file = st.file_uploader("Upload your CSV file", type="csv")

if uploaded_file is not None:
    try:
        # Read the CSV file
        df = pd.read_csv(uploaded_file)
        
        # Check if there's a timestamp column
        timestamp_cols = df.select_dtypes(include=['datetime64']).columns
        if len(timestamp_cols) == 0:
            # Try to identify potential timestamp columns
            potential_timestamp_cols = [col for col in df.columns if 'time' in col.lower() or 'date' in col.lower()]
            if potential_timestamp_cols:
                timestamp_col = st.selectbox(
                    "Select the timestamp column:",
                    potential_timestamp_cols
                )
            else:
                timestamp_col = st.selectbox(
                    "Select the timestamp column:",
                    df.columns
                )
            
            # Convert selected column to datetime
            df[timestamp_col] = pd.to_datetime(df[timestamp_col])
        else:
            timestamp_col = timestamp_cols[0]

        # Show basic information about the dataset
        st.write("Dataset Info:")
        st.write(f"- Total records: {len(df)}")
        st.write(f"- Date range: {df[timestamp_col].min()} to {df[timestamp_col].max()}")
        
        # Time range filter
        st.subheader("Filter by Time of Day")
        col1, col2 = st.columns(2)
        
        with col1:
            start_time = st.time_input('Start time', time(0, 0))
        with col2:
            end_time = st.time_input('End time', time(23, 59))
            
        # Apply time filter
        mask = df[timestamp_col].dt.time.between(start_time, end_time)
        filtered_df = df[mask]
        
        # Show results
        st.subheader("Filtered Dataset")
        st.write(f"Records after filtering: {len(filtered_df)}")
        
        # Display the filtered data
        st.dataframe(filtered_df)
        
        # Download filtered data
        if st.button("Download Filtered Data"):
            csv = filtered_df.to_csv(index=False)
            st.download_button(
                label="Click to Download",
                data=csv,
                file_name="filtered_data.csv",
                mime="text/csv"
            )
            
    except Exception as e:
        st.error(f"Error processing the file: {str(e)}")
else:
    st.info("Please upload a CSV file to begin. The file should contain a timestamp column and sensor readings.")