app.py

import streamlit as st
from ui.UIConfigs import *

#Streamlit dashboard------------------------------------------------------------------------------------------
set_page_config(page='home', logo_size=200)
hide_menu_button()
condense_layout()

st.sidebar.success("")
st.markdown("""
<style>
h2 {
    text-align: left;
}
</style>
""", unsafe_allow_html=True)
# st.markdown("""
# <center><h2>VPI-MLOGs Web App</h2></center>
# <b>VPI-MLOGs</b> is a web app designed for log data analysis and visualization. It provides various functions to help users process, analyze, and visualize log data.<br>
# <h4>1. Read LAS Files and Convert to CSV Files</h4>

# <b>VPI-MLOGs</b> allows users to upload LAS files and convert them to CSV format. This feature makes it easy to work with log data in other programs, such as Excel or Python.

# <h4>2. Explore Data Analysis with Multiple Charts</h4>

# <b>VPI-MLOGs</b> provides several exploratory data analysis (EDA) functions, such as detecting missing data, generating histograms, and visualizing distribution. These functions help users understand the structure and characteristics of the log data.<br>

# <h4>3. Training LGBM Model</h4>

# <b>VPI-MLOGs</b> provides a machine learning feature that allows users to train a LGBM (Light Gradient Boosting Machine) model using their log data.

# <h4>4. Prediction</h4>

# <b>VPI-MLOGs</b> Users can also make predictions using the trained model.
# """
st.markdown("""
<center><h2>VPI-MLOGs Web App</h2></center>

**Read LAS Files and Convert to CSV Files**

VPI-MLOGs enables the reading of LAS files, a commonly used format for storing log data. Once uploaded, VPI-MLOGs can convert LAS files to the CSV format, which is more widely compatible with other programs like Excel or Python.

**Explore Data Analysis with Multiple Charts**

VPI-MLOGs offers various exploratory data analysis (EDA) functions to better understand the characteristics of log data. These EDA functions include:

- **Missing Data Detection**: Identifies any missing data points in the log data.
- **Histogram Generation**: Creates graphical representations of data value distributions.
- **Distribution Visualization**: Creates graphical representations showcasing the spread of data values.
- **Outliers Removal**: Identifies any data points that are significantly different from the rest of the data.

**Training LGBM Model**

VPI-MLOGs provides a machine learning feature that enables users to train a LGBM (Light Gradient Boosting Machine) model using their log data. LGBM is a versatile machine learning algorithm suitable for various tasks like classification and regression. Once trained, the LGBM model can be used to make predictions on new data.

**Prediction**

VPI-MLOGs allows users to make predictions using the trained model. These predictions can be applied to new data not present in the training set. This functionality proves beneficial for tasks like identifying potential drilling targets or predicting rock formation properties.

In summary, VPI-MLOGs is a powerful tool for processing, analyzing, and visualizing log data. It offers a diverse range of functions that aid users in gaining a deeper understanding of their data, enabling them to make more informed decisions.
"""
            ,unsafe_allow_html=True)