forked previous srvice demo

.gitignore

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+.venv/

datacellar-service-demo/.env

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+NILM_API_TOKEN=p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ

datacellar-service-demo/README.md

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+Streamlit app for data cellar services demo

datacellar-service-demo/interface/app.py

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+import streamlit as st
+import json
+import pandas as pd
+import plotly.express as px
+import requests
+from datetime import datetime
+import plotly.graph_objects as go
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN', '')
+
+# Configure the main page
+st.set_page_config(
+    page_title="Energy Data Analysis Dashboard",
+    page_icon="⚡",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Initialize session state variables
+if 'api_token' not in st.session_state:
+    st.session_state.api_token = DEFAULT_TOKEN
+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
+
+# Sidebar configuration
+with st.sidebar:
+    st.markdown("## API Configuration")
+    api_token = st.text_input("API Token", value=st.session_state.api_token, type="password")
+    if api_token:
+        st.session_state.api_token = api_token
+    
+    st.markdown("""
+    ## About
+    This dashboard provides analysis of energy data through various services 
+    including NILM analysis, consumption and production forecasting.
+    """)
+
+# Main page content
+st.title("Energy Data Analysis Dashboard")
+
+# Welcome message and service descriptions
+st.markdown("""
+Welcome to the Energy Data Analysis Dashboard! This platform provides comprehensive tools for analyzing energy consumption and production data.
+
+### Available Services
+
+You can access the following services through the navigation menu on the left:
+
+#### 1. Energy Consumption Forecasting
+- **Short Term**: Predict energy consumption patterns in the near future
+- **Long Term**: Generate long-range consumption forecasts
+
+#### 2. Energy Production Analysis
+- **Short Term Production**: Forecast PV panel energy production
+- **NILM Analysis**: Non-intrusive load monitoring for detailed consumption breakdown
+
+#### 3. Advanced Analytics
+- **Anomaly Detection**: Identify unusual patterns in energy consumption
+
+### Getting Started
+
+1. Select a service from the navigation menu on the left
+2. Upload your energy data file in JSON format
+3. Configure your API token if needed
+4. Run the analysis and explore the results
+
+Each service page provides specific visualizations and analytics tailored to your needs.
+""")
+
+# Add version info and additional resources in an expander
+with st.expander("Additional Information"):
+    st.markdown("""
+    ### Usage Tips
+    - Ensure your data is in the correct JSON format
+    - Keep your API token secure
+    - Use the visualization tools to explore your data
+    - Export results for further analysis
+
+    ### Support
+    For technical support or questions about the services, please contact your system administrator.
+    """)
+
+# Footer
+st.markdown("""
+---
+Made with ❤️ by tLINKS Foundation
+""")

datacellar-service-demo/interface/pages/1_Short_Term_Consumption.py

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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
+from dotenv import load_dotenv
+
+
+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("Short Term Energy Consumption Forecasting")
+
+st.markdown("""
+This service provides short-term forecasting of energy consumption patterns. 
+Upload your energy consumption data to generate predictions for the near future.
+
+### Features
+- Hourly consumption forecasting
+- Interactive visualizations
+- Statistical analysis of predictions
+""")
+
+# 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("Generate Short Term Forecast"):
+                if not st.session_state.api_token:
+                    st.error("Please enter your API token in the sidebar first.")
+                else:
+                    with st.spinner("Generating forecast..."):
+                        st.session_state.api_response = call_api(
+                            st.session_state.current_file,
+                            st.session_state.api_token,
+                            "inference_consumption_short_term"
+                        )
+
+    except Exception as e:
+        st.error(f"Error processing file: {str(e)}")
+
+# Display API results
+if st.session_state.api_response:
+    st.header("Forecast 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:
+            del response_dfs['Celsius']
+            for unit, df in response_dfs.items():
+                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
+    
+    with tabs[1]:
+        st.json(st.session_state.api_response)
+    
+    with tabs[2]:
+        if response_dfs:
+            display_statistics(response_dfs)

datacellar-service-demo/interface/pages/2_Long_Term_Consumption.py

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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("Long Term Energy Consumption Forecasting")
+
+st.markdown("""
+This service provides long-term forecasting of energy consumption patterns. 
+Upload your historical consumption data to generate predictions for extended periods.
+
+### Features
+- Hourly consumption forecasting
+- Interactive visualizations
+- Statistical analysis of predictions
+""")
+
+# 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("Generate Long Term Forecast"):
+                if not st.session_state.api_token:
+                    st.error("Please enter your API token in the sidebar first.")
+                else:
+                    with st.spinner("Generating long-term forecast..."):
+                        st.session_state.api_response = call_api(
+                            st.session_state.current_file,
+                            st.session_state.api_token,
+                            "inference_consumption_long_term"
+                        )
+
+    except Exception as e:
+        st.error(f"Error processing file: {str(e)}")
+
+# Display API results
+if st.session_state.api_response:
+    st.header("Forecast 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:
+            del response_dfs['Celsius']
+            for unit, df in response_dfs.items():
+                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
+    
+    with tabs[1]:
+        st.json(st.session_state.api_response)
+    
+    with tabs[2]:
+        if response_dfs:
+            display_statistics(response_dfs)

datacellar-service-demo/interface/pages/3_Short_Term_Production.py

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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("Short Term Energy Production Forecasting")
+
+st.markdown("""
+This service provides short-term forecasting of energy production patterns, particularly suited for PV panel systems.
+
+### Features
+- Short-term production forecasting
+- Weather-aware predictions
+- Interactive visualizations
+- Statistical analysis of predictions
+""")
+
+# 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("Generate Production Forecast"):
+                if not st.session_state.api_token:
+                    st.error("Please enter your API token in the sidebar first.")
+                else:
+                    with st.spinner("Generating production forecast..."):
+                        st.session_state.api_response = call_api(
+                            st.session_state.current_file,
+                            st.session_state.api_token,
+                            "inference_production_short_term"
+                        )
+
+    except Exception as e:
+        st.error(f"Error processing file: {str(e)}")
+
+# Display API results
+if st.session_state.api_response:
+    st.header("Production Forecast 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():
+                if unit == "kWh":
+                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
+    
+    with tabs[1]:
+        st.json(st.session_state.api_response)
+    
+    with tabs[2]:
+        if response_dfs:
+            display_statistics(response_dfs)

datacellar-service-demo/interface/pages/4_NILM_Analysis.py

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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)

datacellar-service-demo/interface/pages/5_Anomaly_Detection_Consumption.py

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+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 Consumption 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_consumption_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]
+                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)

datacellar-service-demo/interface/pages/6_Anomaly_Detection_Production.py

@@ -0,0 +1,134 @@
+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)

datacellar-service-demo/interface/utils.py

@@ -0,0 +1,274 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import requests
+import json
+from datetime import datetime
+
+def get_series_name_and_unit(series, dataset_description):
+    """
+    Extract the name and unit from a time series using its dataset description.
+    
+    Args:
+        series: Dictionary containing series data
+        dataset_description: Dictionary containing dataset field descriptions
+        
+    Returns:
+        tuple: (name, unit) of the series
+    """
+    field_id = series['datacellar:datasetFieldID']
+    field = next((f for f in dataset_description['datacellar:datasetFields'] 
+                 if f['datacellar:datasetFieldID'] == field_id), None)
+    
+    name = field['datacellar:fieldName'] if field else f'Series {field_id}'
+    unit = field['datacellar:type']['datacellar:unitText'] if field else 'Unknown'
+    
+    # Override name if metadata contains loadType
+    if 'datacellar:timeSeriesMetadata' in series:
+        metadata = series['datacellar:timeSeriesMetadata']
+        if 'datacellar:loadType' in metadata:
+            name = metadata['datacellar:loadType']
+    
+    return name, unit
+
+def process_series(series, dataset_description, is_input=False):
+    """
+    Process a single time series into a pandas DataFrame.
+    
+    Args:
+        series: Dictionary containing series data
+        dataset_description: Dictionary containing dataset field descriptions
+        is_input: Boolean indicating if this is input data
+        
+    Returns:
+        tuple: (DataFrame, unit, name) of the processed series
+    """
+    name, unit = get_series_name_and_unit(series, dataset_description)
+    df = pd.DataFrame(series['datacellar:dataPoints'])
+    
+    # Convert timestamp to datetime and ensure values are numeric
+    df['datacellar:timeStamp'] = pd.to_datetime(df['datacellar:timeStamp'])
+    df['datacellar:value'] = pd.to_numeric(df['datacellar:value'], errors='coerce')
+    
+    # Add series identifier
+    df['series_id'] = f'{name} (Input)' if is_input else name
+    
+    return df, unit, name
+
+def load_and_process_data(json_data, input_data=None):
+    """
+    Load and process time series from the JSON data, filtering out empty series.
+    """
+    series_by_unit = {}
+    try:
+        dataset_description = json_data['datacellar:datasetSelfDescription']
+    except:
+        dataset_description = {
+            "@type": "datacellar:DatasetField",
+            "datacellar:datasetFieldID": 0,
+            "datacellar:fieldName": "anomaly",
+            "datacellar:description": "Anomalies",
+            "datacellar:type": {
+                "@type": "datacellar:boolean",
+                "datacellar:unitText": "-"
+            }
+        }
+    
+    # Process output series
+    try:
+        for series in json_data['datacellar:timeSeriesList']:
+            # Check if series has any data points
+            if series.get('datacellar:dataPoints'):
+                df, unit, _ = process_series(series, dataset_description)
+                # Additional check for non-empty DataFrame
+                if not df.empty and df['datacellar:value'].notna().any():
+                    if unit not in series_by_unit:
+                        series_by_unit[unit] = []
+                    series_by_unit[unit].append(df)
+    except Exception as e:
+        st.error(f"Error processing series: {str(e)}")
+    
+    # Process input series if provided
+    if input_data:
+        input_description = input_data['datacellar:datasetSelfDescription']
+        for series in input_data['datacellar:timeSeriesList']:
+            if series.get('datacellar:dataPoints'):
+                df, unit, _ = process_series(series, input_description, is_input=True)
+                if not df.empty and df['datacellar:value'].notna().any():
+                    if unit not in series_by_unit:
+                        series_by_unit[unit] = []
+                    series_by_unit[unit].append(df)
+    
+    # Concatenate and filter out units with no valid data
+    result = {}
+    for unit, dfs in series_by_unit.items():
+        if dfs:  # Check if there are any DataFrames for this unit
+            combined_df = pd.concat(dfs)
+            if not combined_df.empty and combined_df['datacellar:value'].notna().any():
+                result[unit] = combined_df
+    
+    return result
+
+def create_time_series_plot(df, unit, service_type=None,fig=None):
+    """
+    Create visualization for time series data, handling empty series appropriately.
+    """
+    if service_type == "Anomaly Detection":
+
+        if not fig:
+            fig = go.Figure()
+        
+        # Filter for non-empty input data
+        input_data = df[df['series_id'].str.contains('Input')]
+        input_data = input_data[input_data['datacellar:value'].notna()]
+        
+        if not input_data.empty:
+            fig.add_trace(go.Scatter(
+                x=input_data['datacellar:timeStamp'], 
+                y=input_data['datacellar:value'],
+                mode='lines',
+                name='Energy Consumption',
+                line=dict(color='blue')
+            ))
+        
+            # Handle anomalies
+            anomalies = df[(~df['series_id'].str.contains('Output')) & 
+                          (df['datacellar:value'] == True) &
+                          (df['datacellar:value'].notna())]
+            if not anomalies.empty:
+                anomaly_values = []
+                for timestamp in anomalies['datacellar:timeStamp']:
+                    value = input_data.loc[input_data['datacellar:timeStamp'] == timestamp, 'datacellar:value']
+                    anomaly_values.append(value.iloc[0] if not value.empty else None)
+                
+                # fig.add_trace(go.Scatter(
+                #     x=anomalies['datacellar:timeStamp'],
+                #     y=anomaly_values,
+                #     mode='markers',
+                #     name='Anomalies',
+                #     marker=dict(color='red', size=10)
+                # ))
+        
+        fig.update_layout(
+            title=f'Time Series Data with Anomalies ({unit})',
+            xaxis_title="Time",
+            yaxis_title=f"Value ({unit})",
+            hovermode='x unified',
+            legend_title="Series"
+        )
+        return fig
+    else:
+        # Filter out series with no valid data
+        valid_series = []
+        for series_id in df['series_id'].unique():
+            series_data = df[df['series_id'] == series_id]
+            if not series_data.empty and series_data['datacellar:value'].notna().any():
+                valid_series.append(series_id)
+        
+        # Create plot only for valid series
+        if valid_series:
+            filtered_df = df[df['series_id'].isin(valid_series)]
+            return px.line(
+                filtered_df,
+                x='datacellar:timeStamp',
+                y='datacellar:value',
+                color='series_id',
+                title=f'Time Series Data ({unit})'
+            ).update_layout(
+                xaxis_title="Time",
+                yaxis_title=f"Value ({unit})",
+                hovermode='x unified',
+                legend_title="Series"
+            )
+        else:
+            # Return None or an empty figure if no valid series
+            return None
+
+def display_statistics(dfs_by_unit):
+    """
+    Display statistics only for non-empty series.
+    """
+    for unit, df in dfs_by_unit.items():
+        st.write(f"## Measurements in {unit}")
+        for series_id in df['series_id'].unique():
+            series_data = df[df['series_id'] == series_id]
+            # Check if series has valid data
+            if not series_data.empty and series_data['datacellar:value'].notna().any():
+                st.write(f"### {series_id}")
+                
+                cols = st.columns(4)
+                metrics = [
+                    ("Average", series_data['datacellar:value'].mean()),
+                    ("Max", series_data['datacellar:value'].max()),
+                    ("Min", series_data['datacellar:value'].min()),
+                    ("Total", series_data['datacellar:value'].sum() * 6/3600)
+                ]
+                
+                for col, (label, value) in zip(cols, metrics):
+                    with col:
+                        unit_suffix = "h" if label == "Total" else ""
+                        st.metric(label, f"{value:.2f} {unit}{unit_suffix}")
+
+def call_api(file_content, token, service_endpoint):
+    """
+    Call the analysis API with the provided data.
+    
+    Args:
+        file_content: Binary content of the JSON file
+        token: API authentication token
+        service_endpoint: String indicating which API endpoint to call
+        
+    Returns:
+        dict: JSON response from the API or None if the call fails
+    """
+    try:
+        url = f'https://loki.linksfoundation.com/datacellar/{service_endpoint}'
+        response = requests.post(
+            url,
+            headers={'Authorization': f'Bearer {token}'},
+            files={'input_file': ('data.json', file_content, 'application/json')}
+        )
+        
+        if response.status_code == 401:
+            st.error("Authentication failed. Please check your API token.")
+            return None
+            
+        return response.json()
+    except Exception as e:
+        st.error(f"API Error: {str(e)}")
+        return None
+
+def get_dataset_type(json_data):
+    """
+    Determine the type of dataset from its description.
+    
+    Args:
+        json_data: Dictionary containing the JSON data
+        
+    Returns:
+        str: "production", "consumption", or "other"
+    """
+    desc = json_data.get('datacellar:description', '').lower()
+    if 'production' in desc:
+        return "production"
+    elif 'consumption' in desc:
+        return "consumption"
+    return "other"
+
+def get_forecast_horizon(json_data):
+    """
+    Determine the forecast horizon from dataset description.
+    
+    Args:
+        json_data: Dictionary containing the JSON data
+        
+    Returns:
+        str: "long", "short", or None
+    """
+    desc = json_data.get('datacellar:description', '').lower()
+    if 'long term' in desc:
+        return "long"
+    elif 'short term' in desc:
+        return "short"
+    return None

datacellar-service-demo/requirements.txt

@@ -0,0 +1,5 @@
+streamlit>=1.28.0
+pandas>=2.1.0
+plotly>=5.17.0
+requests>=2.31.0
+python-dotenv>=1.0.0

samples/1_short_term_consumption.json

@@ -0,0 +1,792 @@
+{
+    "@context": {
+        "datacellar": "http://datacellar.org/"
+    },
+    "@type": "datacellar:Dataset",
+    "datacellar:name": "Short Term Energy Consumption Forecasting Data",
+    "datacellar:description": "Short term energy consumption forecasting sample data",
+    "datacellar:datasetSelfDescription": {
+        "@type": "datacellar:DatasetDescription",
+        "datacellar:datasetMetadataTypes": [
+            "datacellar:GeoLocalizedDataset"
+        ],
+        "datacellar:datasetFields": [
+            {
+                "@type": "datacellar:DatasetField",
+                "datacellar:datasetFieldID": 1,
+                "datacellar:fieldName": "outdoorTemperature",
+                "datacellar:description": "Temperature readings",
+                "datacellar:type": {
+                    "@type": "datacellar:FieldType",
+                    "datacellar:unitText": "Celsius",
+                    "datacellar:averagable": true,
+                    "datacellar:summable": false,
+                    "datacellar:anonymizable": false
+                }
+            },
+            {
+                "@type": "datacellar:DatasetField",
+                "datacellar:datasetFieldID": 2,
+                "datacellar:fieldName": "consumedPower",
+                "datacellar:description": "Power consumption readings",
+                "datacellar:type": {
+                    "@type": "datacellar:FieldType",
+                    "datacellar:unitText": "kWh",
+                    "datacellar:averagable": true,
+                    "datacellar:summable": false,
+                    "datacellar:anonymizable": false
+                }
+            }
+        ]
+    },
+    "datacellar:timeSeriesList": [
+        {
+            "@type": "datacellar:TimeSeries",
+            "datacellar:datasetFieldID": 1,
+            "datacellar:startDate": "2006-12-17 01:00:00",
+            "datacellar:endDate": "2006-12-20 00:00:00",
+            "datacellar:granularity": "1 hour",
+            "datacellar:dataPoints": [
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 01:00:00",
+                    "datacellar:value": -8.335
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 02:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 03:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 04:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 05:00:00",
+                    "datacellar:value": -9.445
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 06:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 07:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 08:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 09:00:00",
+                    "datacellar:value": -8.335
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 10:00:00",
+                    "datacellar:value": -17.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 11:00:00",
+                    "datacellar:value": 1.665
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 12:00:00",
+                    "datacellar:value": 3.335
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 13:00:00",
+                    "datacellar:value": 4.445
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 14:00:00",
+                    "datacellar:value": 6.11
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 15:00:00",
+                    "datacellar:value": 6.11
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 16:00:00",
+                    "datacellar:value": 6.11
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 17:00:00",
+                    "datacellar:value": 6.11
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 18:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 19:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 20:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 21:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 22:00:00",
+                    "datacellar:value": 2.5
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-17 23:00:00",
+                    "datacellar:value": 2.5
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 00:00:00",
+                    "datacellar:value": 2.5
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 01:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 02:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 03:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 04:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 05:00:00",
+                    "datacellar:value": 2.5
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 06:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 07:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 08:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 09:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 10:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 11:00:00",
+                    "datacellar:value": 2.78
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 12:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 13:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 14:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 15:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 16:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 17:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 18:00:00",
+                    "datacellar:value": 5.0
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 19:00:00",
+                    "datacellar:value": 4.445
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 20:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 21:00:00",
+                    "datacellar:value": 3.89
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 22:00:00",
+                    "datacellar:value": 3.335
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-18 23:00:00",
+                    "datacellar:value": 2.22
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-19 00:00:00",
+                    "datacellar:value": 1.11
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-19 01:00:00",
+                    "datacellar:value": 1.665
+                },
+                {
+                    "@type": "datacellar:DataPoint",
+                    "datacellar:timeStamp": "2006-12-19 02:00:00",
+                    "datacellar:value": 1.665
+                },
+                {
+                    "@type": "datacellar:DataPoint",
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+}

samples/2_long_term_consumption.json

@@ -0,0 +1,2064 @@
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+}

samples/6_anomaly_detection_production.json

@@ -0,0 +1,1855 @@
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+}