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import streamlit as st
import json
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 = "p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ"
# Clear other states
for key in ['current_file', 'json_data', 'api_response']:
if key in st.session_state:
del st.session_state[key]
# 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
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
""")
# File upload and processing
uploaded_file = st.file_uploader("Upload JSON file", type=['json'])
if uploaded_file:
try:
file_contents = uploaded_file.read()
st.session_state.current_file = file_contents
st.session_state.json_data = json.loads(file_contents)
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 file: {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) |