pages/4_NILM_Analysis.py

import streamlit as st
import json
import os
from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api

if 'api_token' not in st.session_state:
    st.session_state.api_token = DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN')

page_id = 4
if 'current_page' not in st.session_state:
    st.session_state.current_page = page_id
elif st.session_state.current_page != page_id:
    # Clear API response when switching to this page
    if 'api_response' in st.session_state:
        st.session_state.api_response = None
    # Update current page
    st.session_state.current_page = page_id

# Initialize session state variables
if 'current_file' not in st.session_state:
    st.session_state.current_file = None
if 'json_data' not in st.session_state:
    st.session_state.json_data = None
if 'api_response' not in st.session_state:
    st.session_state.api_response = None
if 'using_default_file' not in st.session_state:
    st.session_state.using_default_file = True

st.title("Non-Intrusive Load Monitoring (NILM) Analysis")

st.markdown("""
This service provides detailed breakdown of energy consumption by analyzing aggregate power measurements.

### Features
- Appliance-level energy consumption breakdown
- Load pattern identification
- Device usage analysis
- Detailed consumption insights
""")

# Default file path
default_file_path = "samples/4_NILM.json"  # Adjust this path to your default file

# File upload and processing
uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])

# Load default file if no file is uploaded and using_default_file is True
if uploaded_file is None and st.session_state.using_default_file:
    if os.path.exists(default_file_path):
        st.info(f"Using default file: {default_file_path}")
        with open(default_file_path, 'r') as f:
            file_contents = f.read()
            if st.session_state.current_file != file_contents:
                st.session_state.current_file = file_contents
                st.session_state.json_data = json.loads(file_contents)
    else:
        st.warning(f"Default file not found at: {default_file_path}")
        st.session_state.using_default_file = False

# If a file is uploaded, process it
if uploaded_file:
    st.session_state.using_default_file = False
    try:
        file_contents = uploaded_file.read()
        st.session_state.current_file = file_contents
        st.session_state.json_data = json.loads(file_contents)
    except Exception as e:
        st.error(f"Error processing file: {str(e)}")

# Process and display data if available
if st.session_state.json_data:
    try:
        dfs = load_and_process_data(st.session_state.json_data)
        if dfs:
            st.header("Input Data")
            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
            
            with tabs[0]:
                for unit, df in dfs.items():
                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
            
            with tabs[1]:
                st.json(st.session_state.json_data)
                
            with tabs[2]:
                display_statistics(dfs)
            
            if st.button("Run NILM Analysis"):
                if not st.session_state.api_token:
                    st.error("Please enter your API token in the sidebar first.")
                else:
                    with st.spinner("Performing NILM analysis..."):
                        st.session_state.api_response = call_api(
                            st.session_state.current_file,
                            st.session_state.api_token,
                            "inference_nilm"
                        )
    except Exception as e:
        st.error(f"Error processing data: {str(e)}")

# Display API results
if st.session_state.api_response:
    st.header("NILM Analysis Results")
    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
    
    with tabs[0]:
        response_dfs = load_and_process_data(
            st.session_state.api_response,
            input_data=st.session_state.json_data
        )
        if response_dfs:
            for unit, df in response_dfs.items():
                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
                
            # Add appliance-specific visualizations
            st.subheader("Appliance-Level Breakdown")
            # Additional NILM-specific visualizations could be added here
    
    with tabs[1]:
        st.json(st.session_state.api_response)
    
    with tabs[2]:
        if response_dfs:
            display_statistics(response_dfs)