re-added app.py

app.py

@@ -1,123 +1,102 @@
-# 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
-# import logging
-
-
-
-
-# # Configure the main page
-# st.set_page_config(
-#     page_title="Energy Data Analysis Dashboard",
-#     page_icon="⚡",
-#     layout="wide",
-#     initial_sidebar_state="expanded"
-# )
-
-# #DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN', '')
-# DEFAULT_TOKEN = 'p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ'
-# print(DEFAULT_TOKEN)
-# logger = logging.getLogger("Data cellar demo")
-
-# logger.info(f"token : {DEFAULT_TOKEN}")
-
-# # 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
+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
+import logging
+
+
+
+
+# Configure the main page
+st.set_page_config(
+    page_title="Energy Data Analysis Dashboard",
+    page_icon="⚡",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+#DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN', '')
+DEFAULT_TOKEN = 'p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ'
+print(DEFAULT_TOKEN)
+logger = logging.getLogger("Data cellar demo")
+
+logger.info(f"token : {DEFAULT_TOKEN}")
+
+# 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.
-#     """)
+    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")
+# 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.
+# 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
+### Available Services
 
-# You can access the following services through the navigation menu on the left:
+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
+#### 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
+#### 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
+#### 3. Advanced Analytics
+- **Anomaly Detection**: Identify unusual patterns in energy consumption
 
-# ### Getting Started
+### 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
+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.
-# """)
+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
+# 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.
-#     """)
+    ### Support
+    For technical support or questions about the services, please contact your system administrator.
+    """)
 
-# # Footer
-# st.markdown("""
-# ---
-# Made with ❤️ by tLINKS Foundation
-# """)
+# Footer
+st.markdown("""
+---
+Made with ❤️ by tLINKS Foundation
+""")
 
-import streamlit as st
-import pandas as pd
-# Load Model
-#model = pickle.load(open('logreg_model.pkl', 'rb'))
-st.title('Iris Variety Prediction')
-# Form
-with st.form(key='form_parameters'):
-   sepal_length = st.slider('Sepal Length', 4.0, 8.0, 4.0)
-   sepal_width = st.slider('Sepal Width', 2.0, 4.5, 2.0)
-   petal_length = st.slider('Petal Length', 1.0, 7.0, 1.0)
-   petal_width = st.slider('Petal Width', 0.1, 2.5, 0.1)
-   st.markdown('---')
-   submitted = st.form_submit_button('Predict')
-# Data Inference
-data_inf = {
-   'sepal.length': sepal_length,
-   'sepal.width': sepal_width,
-   'petal.length': petal_length,
-   'petal.width': petal_width
-}
-data_inf = pd.DataFrame([data_inf])