final app initial commit

main.py

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

src/DescriptiveAnalysis/__init__.py

@@ -0,0 +1 @@
+

src/DescriptiveAnalysis/frontend.py

@@ -5,42 +5,7 @@
 import streamlit.components.v1 as components
 import pandas as pd
 
-from backend import generate_data_cubes, process_download
-
-# Set page configuration and custom CSS styling
-st.set_page_config(layout="wide")
-
-st.markdown("""
-    <style>
-    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
-
-    html, body {
-        background: linear-gradient(135deg, #f0f4f8, #d9e2ec);
-        font-family: 'Montserrat', sans-serif;
-        color: #333;
-    }
-    .main-container {
-        background-color: #fff;
-        border-radius: 10px;
-        box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
-        padding: 2rem;
-        margin: 1rem;
-    }
-    .css-1e5imcs, .css-1d391kg {
-        background-color: #ffffff !important;
-        border-radius: 8px;
-        box-shadow: 0px 2px 5px rgba(0,0,0,0.1);
-        padding: 1rem;
-    }
-    .css-1aumxhk {
-        font-size: 1.2rem;
-        font-weight: 600;
-    }
-    .css-1d391kg .stTabs {
-        background-color: #fff;
-    }
-    </style>
-""", unsafe_allow_html=True)
+from .backend import generate_data_cubes, process_download
 
 def load_data():
     """
@@ -347,6 +312,39 @@ def main():
       2. Drill-Down and Roll-Up
       3. Batch Processing
     """
+    # Apply custom CSS styling
+    st.markdown("""
+    <style>
+    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
+
+    html, body {
+        background: linear-gradient(135deg, #f0f4f8, #d9e2ec);
+        font-family: 'Montserrat', sans-serif;
+        color: #333;
+    }
+    .main-container {
+        background-color: #fff;
+        border-radius: 10px;
+        box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
+        padding: 2rem;
+        margin: 1rem;
+    }
+    .css-1e5imcs, .css-1d391kg {
+        background-color: #ffffff !important;
+        border-radius: 8px;
+        box-shadow: 0px 2px 5px rgba(0,0,0,0.1);
+        padding: 1rem;
+    }
+    .css-1aumxhk {
+        font-size: 1.2rem;
+        font-weight: 600;
+    }
+    .css-1d391kg .stTabs {
+        background-color: #fff;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
     st.title("Data Warehouse & Cube Generator")
 
     # Load data from CSV file
@@ -387,4 +385,4 @@ def main():
         batch_processing()
 
 if __name__ == "__main__":
-    main()
+    main()

src/PredictiveAnalytics1/Backend/__init__.py

@@ -0,0 +1 @@
+

src/PredictiveAnalytics1/Frontend/__init__.py

@@ -0,0 +1 @@
+

src/PredictiveAnalytics1/Frontend/app.py

@@ -4,20 +4,13 @@
 import pandas as pd
 import numpy as np
 import os
-import sys
 from pathlib import Path
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-from Backend.backend import (
-    load_data, preprocess_data, apply_pca, determine_problem_type, train_model)
+from ..Backend.backend import load_data, preprocess_data, apply_pca, determine_problem_type, train_model
 from sklearn.preprocessing import LabelEncoder
 from sklearn.cluster import DBSCAN, SpectralClustering
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import silhouette_score
 
-
-st.set_page_config(page_title="Model Comparison Tool", layout="wide")
-
-
 @st.cache_data
 def cached_load_data(file_path):
     return load_data(file_path)
@@ -127,7 +120,11 @@ def main():
     else:
         current_file = Path(__file__).resolve()
         dataset_dir = current_file.parents[3] / 'Datasets' / 'predictive-analytics-1'
-        dataset_files = [f for f in os.listdir(dataset_dir) if f.endswith(".csv")]
+        if not dataset_dir.exists():
+            st.warning("Dataset directory not found. Please upload a CSV file.")
+            dataset_files = []
+        else:
+            dataset_files = [f for f in os.listdir(dataset_dir) if f.endswith(".csv")]
 
         if dataset_files:
             selected_dataset = st.selectbox("Select a dataset", dataset_files)
@@ -267,13 +264,11 @@ def main():
 
                 st.dataframe(comparison_df.set_index('Model').T)
 
-
                 st.subheader("Performance Visualization")
                 chart_data = comparison_df.melt(id_vars=['Model'], var_name='Metric', value_name='Score')
                 chart_data['Score'] = pd.to_numeric(chart_data['Score'], errors='coerce')
                 chart_data = chart_data.dropna(subset=['Score'])
 
-
                 if not chart_data.empty:
                     st.bar_chart(chart_data, x='Metric', y='Score', color='Model', stack=False)
                 else:

src/PredictiveAnalytics1/__init__.py

@@ -0,0 +1 @@
+

src/PrescriptiveAnalysis1/Backend/__init__.py

@@ -0,0 +1 @@
+

src/PrescriptiveAnalysis1/Frontend/__init__.py

@@ -0,0 +1 @@
+

src/PrescriptiveAnalysis1/Frontend/main.py

@@ -1,19 +1,19 @@
 import streamlit as st
-import sys
-import os
 import pandas as pd
+import os
 import time
 from collections import defaultdict
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from Backend.gspan import run_gspan_analysis, construct_dfs_code, load_graphs_from_json
-from Backend.apriori_graph import parse_graph_file, apriori_graph_mining
-from Backend.gsp import preprocess_sequences_ordered, gsp_algorithm
-from Backend.apriori import run_apriori_analysis
-from Backend.fp_growth import run_fp_growth_analysis  
+
+# Relative imports for Backend modules
+from ..Backend.gspan import run_gspan_analysis, construct_dfs_code, load_graphs_from_json
+from ..Backend.apriori_graph import parse_graph_file, apriori_graph_mining
+from ..Backend.gsp import preprocess_sequences_ordered, gsp_algorithm
+from ..Backend.apriori import run_apriori_analysis
+from ..Backend.fp_growth import run_fp_growth_analysis
 
 def apriori_graph_mining_app():
     st.title("Apriori-Based Graph Mining")
-    uploaded_file = st.file_uploader("Upload your graph dataset file ", type=['txt'], key="apriori_file")
+    uploaded_file = st.file_uploader("Upload your graph dataset file", type=['txt'], key="apriori_file")
     if uploaded_file is not None:
         graphs = parse_graph_file(uploaded_file)
         st.write(f"Number of graphs loaded: {len(graphs)}")

src/PrescriptiveAnalysis1/__init__.py

@@ -0,0 +1 @@
+

src/preprocessing/__init__.py

@@ -0,0 +1 @@
+