test_interface

app.py

@@ -1,101 +1,128 @@
+# 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.
+#     """)
+
+# # 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
+# """)
+
 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.
-    """)
-
-# 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
-""")
+import pickle
+# 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])
+if submitted:
+   # Predict using Logistic Regression
+   y_pred_inf = model.predict(data_inf)
+   st.write('## Iris Variety = '+ str(y_pred_inf))