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Merge pull request #31 from TCS-2021/final-app
Final app
2 parents 488b887 + acd9771 commit 1d6e589

13 files changed

Lines changed: 222 additions & 127 deletions

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‎main.py‎

Lines changed: 151 additions & 0 deletions
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import streamlit as st
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import os
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import sys
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from pathlib import Path
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# Set page configuration first
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st.set_page_config(
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page_title="Analytics Dashboard",
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page_icon="📊",
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layout="wide"
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)
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# Add src directory and subdirectories to sys.path
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project_root = Path(__file__).parent
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src_path = project_root / 'src'
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sys.path.insert(0, str(src_path))
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# Add each application's directory to sys.path
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app_dirs = ['DescriptiveAnalysis', 'PredictiveAnalytics1', 'preprocessing', 'PrescriptiveAnalysis1']
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for app_dir in app_dirs:
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app_path = src_path / app_dir
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if app_path.exists():
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sys.path.insert(0, str(app_path))
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else:
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print(f"Directory {app_dir} not found in src/. Please check the directory structure.")
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# Add PredictiveAnalytics1/Frontend and PrescriptiveAnalysis1 subdirectories to sys.path
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predictive_frontend_path = src_path / 'PredictiveAnalytics1' / 'Frontend'
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if predictive_frontend_path.exists():
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sys.path.insert(0, str(predictive_frontend_path))
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else:
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print(f"Frontend directory not found at {predictive_frontend_path}.")
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prescriptive_frontend_path = src_path / 'PrescriptiveAnalysis1' / 'Frontend'
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if prescriptive_frontend_path.exists():
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sys.path.insert(0, str(prescriptive_frontend_path))
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else:
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print(f"Frontend directory not found at {prescriptive_frontend_path}.")
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prescriptive_backend_path = src_path / 'PrescriptiveAnalysis1' / 'Backend'
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if prescriptive_backend_path.exists():
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sys.path.insert(0, str(prescriptive_backend_path))
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else:
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print(f"Backend directory not found at {prescriptive_backend_path}.")
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# Import main functions from each application
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try:
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from DescriptiveAnalysis.frontend import main as descriptive_main
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except ModuleNotFoundError as e:
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print(f"Error importing DescriptiveAnalysis: {e}")
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descriptive_main = None
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try:
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from PredictiveAnalytics1.Frontend.app import main as predictive_main
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except ModuleNotFoundError as e:
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print(f"Error importing PredictiveAnalytics1: {e}")
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predictive_main = None
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try:
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from preprocessing.app import main as preprocessing_main
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except ModuleNotFoundError as e:
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print(f"Error importing preprocessing: {e}")
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preprocessing_main = None
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try:
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from PrescriptiveAnalysis1.Frontend.main import main as prescriptive_main
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except ModuleNotFoundError as e:
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print(f"Error importing PrescriptiveAnalysis1: {e}")
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prescriptive_main = None
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# Custom CSS for styling
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
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html, body {
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font-family: 'Montserrat', sans-serif;
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color: #333;
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}
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.sidebar .sidebar-content {
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background: linear-gradient(135deg, #f0f4f8, #d9e2ec);
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border-radius: 8px;
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padding: 1rem;
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}
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.stSelectbox > div > label {
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font-size: 1.1rem;
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font-weight: 600;
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color: #2c3e50;
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}
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.main-container {
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background-color: #fff;
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border-radius: 10px;
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box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
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padding: 2rem;
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margin: 1rem;
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}
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h1 {
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color: #2c3e50;
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text-align: center;
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margin-bottom: 2rem;
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}
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</style>
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""", unsafe_allow_html=True)
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def main():
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"""
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Main function to run the Streamlit dashboard application.
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Provides a sidebar to navigate between different analytics applications.
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"""
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st.title("Analytics Dashboard")
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# Display import errors, if any
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if descriptive_main is None:
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st.error("Cannot import DescriptiveAnalysis. Please check the directory structure and files.")
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if predictive_main is None:
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st.error("Cannot import PredictiveAnalytics1. Please check the directory structure and files.")
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if preprocessing_main is None:
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st.error("Cannot import preprocessing. Please check the directory structure and files.")
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if prescriptive_main is None:
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st.error("Cannot import PrescriptiveAnalysis1. Please check the directory structure and files.")
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# Sidebar for application selection
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st.sidebar.title("Navigation")
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app_options = [
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"Descriptive Analysis",
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"Predictive Analytics",
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"Preprocessing",
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"Prescriptive Analysis"
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]
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selected_app = st.sidebar.selectbox("Select Application", app_options)
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# Map selected app to the corresponding main function
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app_functions = {
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"Descriptive Analysis": descriptive_main,
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"Predictive Analytics": predictive_main,
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"Preprocessing": preprocessing_main,
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"Prescriptive Analysis": prescriptive_main
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}
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# Run the selected application's main function
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with st.container():
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st.markdown('<div class="main-container">', unsafe_allow_html=True)
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selected_function = app_functions[selected_app]
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if selected_function is None:
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st.error(f"Cannot run {selected_app}. The module could not be imported. Please check the error messages above.")
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else:
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selected_function()
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st.markdown('</div>', unsafe_allow_html=True)
149+
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if __name__ == "__main__":
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main()

‎src/DescriptiveAnalysis/__init__.py‎

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‎src/DescriptiveAnalysis/frontend.py‎

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@@ -5,42 +5,7 @@
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import streamlit.components.v1 as components
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import pandas as pd
77

8-
from backend import generate_data_cubes, process_download
9-
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# Set page configuration and custom CSS styling
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st.set_page_config(layout="wide")
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
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html, body {
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background: linear-gradient(135deg, #f0f4f8, #d9e2ec);
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font-family: 'Montserrat', sans-serif;
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color: #333;
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}
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.main-container {
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background-color: #fff;
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border-radius: 10px;
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box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
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padding: 2rem;
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margin: 1rem;
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}
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.css-1e5imcs, .css-1d391kg {
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background-color: #ffffff !important;
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border-radius: 8px;
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box-shadow: 0px 2px 5px rgba(0,0,0,0.1);
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padding: 1rem;
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}
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.css-1aumxhk {
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font-size: 1.2rem;
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font-weight: 600;
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}
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.css-1d391kg .stTabs {
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background-color: #fff;
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}
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</style>
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""", unsafe_allow_html=True)
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from .backend import generate_data_cubes, process_download
449

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def load_data():
4611
"""
@@ -347,6 +312,39 @@ def main():
347312
2. Drill-Down and Roll-Up
348313
3. Batch Processing
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"""
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# Apply custom CSS styling
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
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320+
html, body {
321+
background: linear-gradient(135deg, #f0f4f8, #d9e2ec);
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font-family: 'Montserrat', sans-serif;
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color: #333;
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}
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.main-container {
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background-color: #fff;
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border-radius: 10px;
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box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
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padding: 2rem;
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margin: 1rem;
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}
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.css-1e5imcs, .css-1d391kg {
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background-color: #ffffff !important;
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border-radius: 8px;
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box-shadow: 0px 2px 5px rgba(0,0,0,0.1);
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padding: 1rem;
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}
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.css-1aumxhk {
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font-size: 1.2rem;
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font-weight: 600;
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}
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.css-1d391kg .stTabs {
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background-color: #fff;
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}
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</style>
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""", unsafe_allow_html=True)
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st.title("Data Warehouse & Cube Generator")
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352350
# Load data from CSV file
@@ -387,4 +385,4 @@ def main():
387385
batch_processing()
388386

389387
if __name__ == "__main__":
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main()
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main()

‎src/PredictiveAnalytics1/Backend/__init__.py‎

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‎src/PredictiveAnalytics1/Frontend/__init__.py‎

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‎src/PredictiveAnalytics1/Frontend/app.py‎

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import pandas as pd
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import numpy as np
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import os
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import sys
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from pathlib import Path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from Backend.backend import (
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load_data, preprocess_data, apply_pca, determine_problem_type, train_model)
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from ..Backend.backend import load_data, preprocess_data, apply_pca, determine_problem_type, train_model
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from sklearn.preprocessing import LabelEncoder
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from sklearn.cluster import DBSCAN, SpectralClustering
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import silhouette_score
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st.set_page_config(page_title="Model Comparison Tool", layout="wide")
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@st.cache_data
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def cached_load_data(file_path):
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return load_data(file_path)
@@ -127,7 +120,11 @@ def main():
127120
else:
128121
current_file = Path(__file__).resolve()
129122
dataset_dir = current_file.parents[3] / 'Datasets' / 'predictive-analytics-1'
130-
dataset_files = [f for f in os.listdir(dataset_dir) if f.endswith(".csv")]
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if not dataset_dir.exists():
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st.warning("Dataset directory not found. Please upload a CSV file.")
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dataset_files = []
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else:
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dataset_files = [f for f in os.listdir(dataset_dir) if f.endswith(".csv")]
131128

132129
if dataset_files:
133130
selected_dataset = st.selectbox("Select a dataset", dataset_files)
@@ -267,13 +264,11 @@ def main():
267264

268265
st.dataframe(comparison_df.set_index('Model').T)
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271267
st.subheader("Performance Visualization")
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chart_data = comparison_df.melt(id_vars=['Model'], var_name='Metric', value_name='Score')
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chart_data['Score'] = pd.to_numeric(chart_data['Score'], errors='coerce')
274270
chart_data = chart_data.dropna(subset=['Score'])
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277272
if not chart_data.empty:
278273
st.bar_chart(chart_data, x='Metric', y='Score', color='Model', stack=False)
279274
else:

‎src/PredictiveAnalytics1/__init__.py‎

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‎src/PrescriptiveAnalysis1/Backend/__init__.py‎

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‎src/PrescriptiveAnalysis1/Frontend/__init__.py‎

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‎src/PrescriptiveAnalysis1/Frontend/main.py‎

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import streamlit as st
2-
import sys
3-
import os
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import pandas as pd
3+
import os
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import time
65
from collections import defaultdict
7-
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
8-
from Backend.gspan import run_gspan_analysis, construct_dfs_code, load_graphs_from_json
9-
from Backend.apriori_graph import parse_graph_file, apriori_graph_mining
10-
from Backend.gsp import preprocess_sequences_ordered, gsp_algorithm
11-
from Backend.apriori import run_apriori_analysis
12-
from Backend.fp_growth import run_fp_growth_analysis
6+
7+
# Relative imports for Backend modules
8+
from ..Backend.gspan import run_gspan_analysis, construct_dfs_code, load_graphs_from_json
9+
from ..Backend.apriori_graph import parse_graph_file, apriori_graph_mining
10+
from ..Backend.gsp import preprocess_sequences_ordered, gsp_algorithm
11+
from ..Backend.apriori import run_apriori_analysis
12+
from ..Backend.fp_growth import run_fp_growth_analysis
1313

1414
def apriori_graph_mining_app():
1515
st.title("Apriori-Based Graph Mining")
16-
uploaded_file = st.file_uploader("Upload your graph dataset file ", type=['txt'], key="apriori_file")
16+
uploaded_file = st.file_uploader("Upload your graph dataset file", type=['txt'], key="apriori_file")
1717
if uploaded_file is not None:
1818
graphs = parse_graph_file(uploaded_file)
1919
st.write(f"Number of graphs loaded: {len(graphs)}")

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