added default files

pages/1_Short_Term_Consumption.py

@@ -1,16 +1,12 @@
 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 = "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
@@ -18,6 +14,8 @@ 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("Short Term Energy Consumption Forecasting")
 
@@ -31,15 +29,38 @@ Upload your energy consumption data to generate predictions for the near future.
 - Statistical analysis of predictions
 """)
 
+# Default file path
+default_file_path = "samples/1_short_term_consumption.json"  # Adjust this path to your default file
+
 # File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file", type=['json'])
+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")
@@ -65,9 +86,8 @@ if uploaded_file:
                             st.session_state.api_token,
                             "inference_consumption_short_term"
                         )
-
     except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:
@@ -80,7 +100,8 @@ if st.session_state.api_response:
             input_data=st.session_state.json_data
         )
         if response_dfs:
-            del response_dfs['Celsius']
+            if 'Celsius' in 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)
     

pages/2_Long_Term_Consumption.py

@@ -1,15 +1,11 @@
 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 = "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
@@ -17,6 +13,8 @@ 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("Long Term Energy Consumption Forecasting")
 
@@ -30,15 +28,38 @@ Upload your historical consumption data to generate predictions for extended per
 - Statistical analysis of predictions
 """)
 
+# Default file path
+default_file_path = "samples/2_long_term_consumption.json"  # Adjust this path to your default file
+
 # File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file", type=['json'])
+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")
@@ -64,9 +85,8 @@ if uploaded_file:
                             st.session_state.api_token,
                             "inference_consumption_long_term"
                         )
-
     except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:
@@ -79,7 +99,8 @@ if st.session_state.api_response:
             input_data=st.session_state.json_data
         )
         if response_dfs:
-            del response_dfs['Celsius']
+            if 'Celsius' in 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)
     

pages/3_Short_Term_Production.py

@@ -1,15 +1,11 @@
 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 = "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
@@ -17,6 +13,8 @@ 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("Short Term Energy Production Forecasting")
 
@@ -30,15 +28,38 @@ This service provides short-term forecasting of energy production patterns, part
 - Statistical analysis of predictions
 """)
 
+# Default file path
+default_file_path = "samples/3_short_term_production.json"  # Adjust this path to your default file
+
 # File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file", type=['json'])
+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")
@@ -64,9 +85,8 @@ if uploaded_file:
                             st.session_state.api_token,
                             "inference_production_short_term"
                         )
-
     except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:

pages/4_NILM_Analysis.py

@@ -1,15 +1,11 @@
 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 = "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
@@ -17,6 +13,8 @@ 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")
 
@@ -30,15 +28,38 @@ This service provides detailed breakdown of energy consumption by analyzing aggr
 - 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", type=['json'])
+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")
@@ -64,9 +85,8 @@ if uploaded_file:
                             st.session_state.api_token,
                             "inference_nilm"
                         )
-
     except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:

pages/5_Anomaly_Detection_Consumption.py

@@ -1,6 +1,7 @@
 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
@@ -9,11 +10,6 @@ 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
@@ -21,6 +17,8 @@ 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 Consumption Anomaly Detection")
 
@@ -31,20 +29,40 @@ This service analyzes energy consumption patterns to detect anomalies and unusua
 - Real-time anomaly detection
 - Consumption irregularity identification
 - Interactive visualization of detected anomalies
-
 """)
 
-
+# Default file path
+default_file_path = "samples/5_anomaly_detection_consumption.json"  # Adjust this path to your default file
 
 # File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file", type=['json'])
+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")
@@ -84,7 +102,6 @@ if uploaded_file:
                             # 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(
@@ -96,9 +113,8 @@ if uploaded_file:
                 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)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:
@@ -111,17 +127,21 @@ if 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]
+            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)
+                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,
@@ -129,4 +149,4 @@ if st.session_state.api_response:
                 )
     
     with tabs[1]:
-        st.json(st.session_state.api_response)
+        st.json(st.session_state.api_response)

pages/6_Anomaly_Detection_Production.py

@@ -1,6 +1,7 @@
 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
@@ -9,11 +10,6 @@ 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
@@ -21,30 +17,52 @@ 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 consumption patterns to detect anomalies and unusual behavior in your data.
+This service analyzes energy production patterns to detect anomalies and unusual behavior in your data.
 
 ### Features
 - Real-time anomaly detection
-- Consumption irregularity identification
+- 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", type=['json'])
-
+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")
@@ -57,7 +75,7 @@ if uploaded_file:
                     # Show basic statistical analysis
                     col1, col2, col3 = st.columns(3)
                     with col1:
-                        st.metric("Average Consumption", 
+                        st.metric("Average Production", 
                                 f"{df['datacellar:value'].mean():.2f} {unit}")
                     with col2:
                         st.metric("Standard Deviation", 
@@ -80,11 +98,10 @@ if uploaded_file:
                     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..."):
+                        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(
@@ -96,9 +113,8 @@ if uploaded_file:
                 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)}")
+        st.error(f"Error processing data: {str(e)}")
 
 # Display API results
 if st.session_state.api_response:
@@ -111,19 +127,23 @@ if 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]
+            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)
+                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,
@@ -131,4 +151,4 @@ if st.session_state.api_response:
                 )
     
     with tabs[1]:
-        st.json(st.session_state.api_response)
+        st.json(st.session_state.api_response)