Spaces:
Sleeping
Sleeping
File size: 6,377 Bytes
866cee0 36ff46c 866cee0 0ca6b56 866cee0 0571917 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 0571917 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c 866cee0 36ff46c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
import streamlit as st
import json
import pandas as pd
import os
from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
import plotly.express as px
import plotly.graph_objects as go
if 'api_token' not in st.session_state:
st.session_state.api_token = os.getenv('NILM_API_TOKEN')
page_id = 6
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("Energy Production Anomaly Detection")
st.markdown("""
This service analyzes energy production patterns to detect anomalies and unusual behavior in your data.
### Features
- Real-time anomaly detection
- Production irregularity identification
- Interactive visualization of detected anomalies
""")
# Default file path
default_file_path = "samples/6_anomaly_detection_production.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 Analysis")
tabs = st.tabs(["Visualization", "Statistics", "Raw Data"])
with tabs[0]:
for unit, df in dfs.items():
st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
# Show basic statistical analysis
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Average Production",
f"{df['datacellar:value'].mean():.2f} {unit}")
with col2:
st.metric("Standard Deviation",
f"{df['datacellar:value'].std():.2f} {unit}")
with col3:
st.metric("Total Samples",
len(df))
with tabs[1]:
display_statistics(dfs)
with tabs[2]:
st.json(st.session_state.json_data)
# Add analysis options
st.subheader("Anomaly Detection")
col1, col2 = st.columns(2)
with col1:
if st.button("Detect Anomalies", key="detect_button"):
if not st.session_state.api_token:
st.error("Please enter your API token in the sidebar first.")
else:
with st.spinner("Analyzing production patterns..."):
# Add sensitivity and window_size to the request
modified_data = st.session_state.json_data.copy()
# Convert back to JSON and call API
modified_content = json.dumps(modified_data).encode('utf-8')
st.session_state.api_response = call_api(
modified_content,
st.session_state.api_token,
"inference_production_ad"
)
except Exception as e:
st.error(f"Error processing data: {str(e)}")
# Display API results
if st.session_state.api_response:
st.header("Anomaly Detection Results")
tabs = st.tabs(["Anomaly Visualization", "Raw Results"])
with tabs[0]:
response_dfs = load_and_process_data(
st.session_state.api_response,
input_data=st.session_state.json_data
)
if response_dfs:
anomalies = response_dfs['boolean']
anomalies = anomalies[anomalies['datacellar:value']==True]
del response_dfs['boolean']
for unit, df in response_dfs.items():
fig = create_time_series_plot(df, unit, service_type="Anomaly Detection")
# Get df values for anomalies
anomaly_df = df.iloc[anomalies['datacellar:timeStamp'].index]
fig.add_trace(go.Scatter(
x=anomaly_df['datacellar:timeStamp'],
y=anomaly_df['datacellar:value'],
mode='markers',
marker=dict(color='red'),
name='Anomalies'
))
# Create visualization with highlighted anomalies
st.plotly_chart(
fig,
use_container_width=True
)
with tabs[1]:
st.json(st.session_state.api_response) |