pages/6_Anomaly_Detection_Production.py

import streamlit as st
import json
import pandas as pd
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 = "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("Energy Production Anomaly Detection")

st.markdown("""
This service analyzes energy consumption patterns to detect anomalies and unusual behavior in your data.

### Features
- Real-time anomaly detection
- Consumption irregularity identification
- Interactive visualization of detected anomalies

""")



# 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 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 Consumption", 
                                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 consumption 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"
                            )
            with col2:
                if st.button("Clear Results", key="clear_button"):
                    st.session_state.api_response = None
                    st.experimental_rerun()

    except Exception as e:
        st.error(f"Error processing file: {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]
                #print(anomaly_df)

                fig.add_trace(go.Scatter(x=anomaly_df['datacellar:timeStamp'], y=anomaly_df['datacellar:value'], mode='markers', marker=dict(color='red'), name='Anomalies'))
                #print(unit)
                # Create visualization with highlighted anomalies
                st.plotly_chart(
                    fig,
                    use_container_width=True
                )
    
    with tabs[1]:
        st.json(st.session_state.api_response)