From 2125506f6704d888a9beecad5cc41b543d8e7314 Mon Sep 17 00:00:00 2001 From: andwati Date: Sun, 28 Sep 2025 18:51:07 +0300 Subject: [PATCH] antigravity --- app.py | 132 +++++- architecture_diagrams/00_system_overview.py | 92 ++++ .../01_data_flow_architecture.py | 63 +++ architecture_diagrams/02_eda_process_flow.py | 50 +++ .../03_ml_pipeline_architecture.py | 63 +++ .../04_application_architecture.py | 71 +++ .../05_deployment_architecture.py | 87 ++++ .../06_feature_engineering_pipeline.py | 70 +++ architecture_diagrams/07_user_journey_flow.py | 79 ++++ .../08_security_architecture.py | 93 ++++ .../generate_all_diagrams.py | 82 ++++ pyproject.toml | 1 + src/components/form_components.py | 371 ++++++++++++++++ src/components/prediction_components.py | 274 ++++++++++++ src/utils/auth.py | 308 +++++++++++++ src/utils/models_utils.py | 112 +++++ users.json | 12 + uv.lock | 420 ++++++++++++++++++ 18 files changed, 2378 insertions(+), 2 deletions(-) create mode 100644 architecture_diagrams/00_system_overview.py create mode 100644 architecture_diagrams/01_data_flow_architecture.py create mode 100644 architecture_diagrams/02_eda_process_flow.py create mode 100644 architecture_diagrams/03_ml_pipeline_architecture.py create mode 100644 architecture_diagrams/04_application_architecture.py create mode 100644 architecture_diagrams/05_deployment_architecture.py create mode 100644 architecture_diagrams/06_feature_engineering_pipeline.py create mode 100644 architecture_diagrams/07_user_journey_flow.py create mode 100644 architecture_diagrams/08_security_architecture.py create mode 100644 architecture_diagrams/generate_all_diagrams.py create mode 100644 src/components/form_components.py create mode 100644 src/components/prediction_components.py create mode 100644 src/utils/models_utils.py create mode 100644 users.json diff --git a/app.py b/app.py index c487adf..d97dca2 100644 --- a/app.py +++ b/app.py @@ -1,6 +1,134 @@ +""" +Main Streamlit application for Student Success Predictor +""" + +import streamlit as st + +from src.components.form_components import ( + render_preset_selector, + render_personal_info_section, + render_academic_background_section, + render_family_background_section, + render_financial_social_section, + render_academic_performance_section, + render_economic_indicators_section +) +from src.components.prediction_components import ( + render_prediction_section, + render_model_info_sidebar, + render_export_section, + render_footer +) +from src.config.mappings import create_feature_mappings +from src.config.theme import ROSE_PINE_THEME, PAGE_CONFIG, APP_TITLE, APP_SUBTITLE +from src.utils.auth import require_auth, check_role_permission, render_admin_panel, check_session_timeout +from src.utils.models_utils import load_model_and_info + + +def apply_theme(): + """Apply the Rose Pine dark theme""" + st.markdown(ROSE_PINE_THEME, unsafe_allow_html=True) + + +def render_header(): + """Render the main application header""" + st.markdown(f'

{APP_TITLE}

', unsafe_allow_html=True) + st.markdown(f'

{APP_SUBTITLE}

', unsafe_allow_html=True) + + +def collect_user_input(): + """Collect all user input from the sidebar form""" + feature_maps = create_feature_mappings() + + with st.sidebar: + # Preset selector + preset = render_preset_selector() + + # Personal information + personal_info = render_personal_info_section(preset, feature_maps) + + # Academic background + academic_bg = render_academic_background_section(preset, feature_maps) + + # Family background + family_bg = render_family_background_section(feature_maps) + + # Financial and social factors + financial_social = render_financial_social_section() + + # Academic performance + academic_performance = render_academic_performance_section(preset) + + # Economic indicators + economic_indicators = render_economic_indicators_section() + + # Combine all input data + user_input = { + **personal_info, + **academic_bg, + **family_bg, + **financial_social, + **academic_performance, + **economic_indicators + } + + return user_input + + +@require_auth def main(): - print("Hello from student-retention!") + """Main Streamlit application""" + # Check session timeout (30 minutes) + check_session_timeout(30) + + # Apply theme + apply_theme() + + # Load model and preprocessing info + model, preprocessing_info, feature_names = load_model_and_info() + + # Render header with user context + render_header() + + # Add admin panel if user is admin + if check_role_permission("admin"): + with st.sidebar: + render_admin_panel() + + # Collect user input + user_input = collect_user_input() + + # Main content area + col1, col2 = st.columns([2, 1]) + + with col1: + st.markdown('

Prediction Results

', unsafe_allow_html=True) + + if check_role_permission("educator"): + st.markdown( + '
🎓 Educator Access: You have full access to prediction features and student data analysis.
', + unsafe_allow_html=True) + + # Render prediction section + render_prediction_section(model, preprocessing_info, user_input) + + with col2: + # Render model info and validation + render_model_info_sidebar(user_input) + + # Render export section (role-based access) + if check_role_permission("educator"): + render_export_section(user_input) + else: + st.markdown( + '
📊 Export Feature: Available for educators and administrators only.
', + unsafe_allow_html=True) + + # Render footer + render_footer() if __name__ == "__main__": - main() + # Configure page before authentication + st.set_page_config(**PAGE_CONFIG) + main() \ No newline at end of file diff --git a/architecture_diagrams/00_system_overview.py b/architecture_diagrams/00_system_overview.py new file mode 100644 index 0000000..4edeed6 --- /dev/null +++ b/architecture_diagrams/00_system_overview.py @@ -0,0 +1,92 @@ +""" +System Overview Diagram +High-level view of the entire Student Success Prediction system +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.client import Users +from diagrams.generic.storage import Storage +from diagrams.programming.language import Python +from diagrams.programming.framework import Django +from diagrams.onprem.analytics import Tableau +from diagrams.onprem.inmemory import Redis +from diagrams.aws.ml import SagemakerModel +from diagrams.generic.compute import Rack +from diagrams.onprem.monitoring import Grafana +from diagrams.onprem.network import Nginx + +with Diagram("Student Success Prediction - System Overview", + filename="diagrams/00_system_overview", + show=False, + direction="TB"): + + # System Title and Context + title = Storage("Student Success Prediction System\n• 93.84% Accuracy Random Forest Model\n• 36 Features, 3 Outcomes\n• 4,424 Student Dataset") + + # Phase 1: Data Analysis & Model Development + with Cluster("Phase 1: Data Analysis & Model Development"): + raw_data = Storage("Raw Dataset\ndata.csv") + eda_process = Tableau("Exploratory Data Analysis\n• Data quality assessment\n• Statistical analysis\n• Feature insights") + ml_pipeline = Python("ML Pipeline\n• Feature engineering\n• Model training\n• Performance validation") + model_artifacts = SagemakerModel("Model Artifacts\n• Random Forest (93.84%)\n• Preprocessing info\n• Feature mappings") + + # Phase 2: Application Development + with Cluster("Phase 2: Application Development"): + modular_architecture = Rack("Modular Architecture\n• Component separation\n• Configuration management\n• Utility services") + + auth_system = Redis("Authentication System\n• Role-based access\n• Session management\n• Security controls") + + ui_components = Django("UI Components\n• Rose Pine theme\n• Interactive forms\n• Responsive design") + + streamlit_app = Django("Streamlit Application\n• Main dashboard\n• User interface\n• API integration") + + # Phase 3: Production Deployment + with Cluster("Phase 3: Production Deployment"): + deployment_options = Rack("Deployment Options\n• Streamlit Cloud\n• Docker containers\n• Traditional servers") + + monitoring = Grafana("Monitoring & Analytics\n• Performance metrics\n• User analytics\n• System health") + + security = Redis("Security & Compliance\n• Data protection\n• Access control\n• Audit logging") + + # End Users and Outcomes + with Cluster("Stakeholders & Users"): + students = Users("Students\n• Academic planning\n• Risk assessment\n• Personal insights") + educators = Users("Educators\n• Student analytics\n• Intervention planning\n• Performance tracking") + administrators = Users("Administrators\n• System management\n• User oversight\n• Strategic planning") + + # Global Internet Access + internet = Nginx("Global Access\nSecure HTTPS") + + # System Flow + title >> raw_data >> eda_process >> ml_pipeline >> model_artifacts + + model_artifacts >> modular_architecture + modular_architecture >> auth_system + modular_architecture >> ui_components + auth_system >> streamlit_app + ui_components >> streamlit_app + + streamlit_app >> deployment_options + deployment_options >> monitoring + deployment_options >> security + + internet >> streamlit_app + streamlit_app >> students + streamlit_app >> educators + streamlit_app >> administrators + + # Key Metrics and Features + with Cluster("Key System Metrics"): + accuracy_metric = Storage("Model Performance\n• 93.84% Test Accuracy\n• Balanced class performance\n• Robust generalization") + + feature_count = Storage("Feature Engineering\n• 36 input features\n• 105+ encoded features\n• Comprehensive preprocessing") + + user_experience = Storage("User Experience\n• 3 user roles\n• Intuitive interface\n• Real-time predictions") + + scalability = Storage("Scalability\n• Cloud deployment\n• Modular architecture\n• Production-ready") + + # Connect metrics to main system + streamlit_app >> accuracy_metric + ml_pipeline >> feature_count + ui_components >> user_experience + deployment_options >> scalability \ No newline at end of file diff --git a/architecture_diagrams/01_data_flow_architecture.py b/architecture_diagrams/01_data_flow_architecture.py new file mode 100644 index 0000000..70796ba --- /dev/null +++ b/architecture_diagrams/01_data_flow_architecture.py @@ -0,0 +1,63 @@ +""" +Data Flow Architecture Diagram +Shows the complete data pipeline from raw data to model predictions +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.database import PostgreSQL +from diagrams.onprem.inmemory import Redis +from diagrams.programming.language import Python +from diagrams.onprem.analytics import Tableau +from diagrams.onprem.client import Users +from diagrams.onprem.workflow import Airflow +from diagrams.generic.storage import Storage +from diagrams.generic.compute import Rack +from diagrams.aws.ml import SagemakerModel +from diagrams.programming.framework import Django +from diagrams.onprem.monitoring import Grafana + +with Diagram("Student Success Prediction - Data Flow Architecture", + filename="diagrams/01_data_flow_architecture", + show=False, + direction="TB"): + + # Data Sources + with Cluster("Data Sources"): + raw_data = Storage("Raw Dataset\n(data.csv)\n4,424 students\n37 features") + + # Data Processing Pipeline + with Cluster("Data Processing Pipeline"): + eda_notebook = Python("Exploratory Data Analysis\n(predictive_analysis.ipynb)") + data_cleaning = Rack("Data Cleaning\n• Remove apostrophes\n• Fix typos\n• Handle duplicates") + feature_eng = Rack("Feature Engineering\n• One-hot encoding\n• Label encoding\n• Scaling") + + # Model Development + with Cluster("Model Development"): + model_training = Python("Model Training\n(train_and_save_model.py)") + model_validation = Grafana("Model Validation\n• Cross-validation\n• Performance metrics") + model_storage = Storage("Model Artifacts\n• random_forest_model.pkl\n• preprocessing_info.json") + + # Production System + with Cluster("Production Dashboard"): + auth_system = Redis("Authentication\n• Session management\n• Role-based access") + streamlit_app = Django("Streamlit App\n(app.py)") + + with Cluster("Modular Components"): + theme_config = Python("Theme Config\n• Rose Pine CSS\n• UI constants") + form_components = Python("Form Components\n• Input validation\n• User interface") + model_utils = Python("Model Utils\n• Preprocessing\n• Inference") + + # End Users + users = Users("End Users\n• Students\n• Educators\n• Administrators") + + # Data Flow + raw_data >> eda_notebook >> data_cleaning >> feature_eng + feature_eng >> model_training >> model_validation >> model_storage + + model_storage >> model_utils + auth_system >> streamlit_app + theme_config >> streamlit_app + form_components >> streamlit_app + model_utils >> streamlit_app + + streamlit_app >> users \ No newline at end of file diff --git a/architecture_diagrams/02_eda_process_flow.py b/architecture_diagrams/02_eda_process_flow.py new file mode 100644 index 0000000..c7a10c4 --- /dev/null +++ b/architecture_diagrams/02_eda_process_flow.py @@ -0,0 +1,50 @@ +""" +EDA Process Flow Diagram +Shows the exploratory data analysis workflow and key insights +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.generic.storage import Storage +from diagrams.generic.compute import Rack +from diagrams.programming.language import Python +from diagrams.onprem.analytics import Tableau +from diagrams.onprem.monitoring import Grafana + +with Diagram("Exploratory Data Analysis Process Flow", + filename="diagrams/02_eda_process_flow", + show=False, + direction="TB"): + + # Data Input + raw_dataset = Storage("Raw Dataset\ndata.csv\n4,424 rows × 37 columns") + + # EDA Steps + with Cluster("Data Quality Assessment"): + data_info = Rack("Data Info\n• No missing values\n• No duplicates\n• Mixed data types") + column_analysis = Rack("Column Analysis\n• 36 predictors\n• 1 target variable\n• Column name cleanup") + + with Cluster("Statistical Analysis"): + descriptive_stats = Grafana("Descriptive Statistics\n• Central tendencies\n• Distributions\n• Outlier detection") + correlation_analysis = Grafana("Correlation Analysis\n• Feature relationships\n• Multicollinearity check") + + with Cluster("Visualization & Insights"): + target_distribution = Tableau("Target Distribution\n• Graduate: 50%\n• Dropout: 32%\n• Enrolled: 17%") + feature_plots = Tableau("Feature Analysis\n• Histograms\n• Box plots\n• Categorical counts") + pattern_discovery = Tableau("Pattern Discovery\n• Key predictors\n• Risk factors\n• Success indicators") + + # Key Findings + with Cluster("Key Findings"): + class_imbalance = Rack("Class Imbalance\nIdentified for modeling") + high_cardinality = Rack("High Cardinality\nCourse (51 values)\nApplication order (9 values)") + data_quality = Rack("Data Quality\nClean dataset\nReady for modeling") + + # Data Flow + raw_dataset >> data_info >> descriptive_stats + raw_dataset >> column_analysis >> correlation_analysis + descriptive_stats >> target_distribution + correlation_analysis >> feature_plots + target_distribution >> pattern_discovery + + pattern_discovery >> class_imbalance + pattern_discovery >> high_cardinality + pattern_discovery >> data_quality \ No newline at end of file diff --git a/architecture_diagrams/03_ml_pipeline_architecture.py b/architecture_diagrams/03_ml_pipeline_architecture.py new file mode 100644 index 0000000..614a456 --- /dev/null +++ b/architecture_diagrams/03_ml_pipeline_architecture.py @@ -0,0 +1,63 @@ +""" +Machine Learning Pipeline Architecture +Shows the complete ML workflow from preprocessing to model deployment +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.generic.storage import Storage +from diagrams.generic.compute import Rack +from diagrams.programming.language import Python +from diagrams.aws.ml import SagemakerModel +from diagrams.onprem.monitoring import Grafana +from diagrams.generic.database import SQL + +with Diagram("Machine Learning Pipeline Architecture", + filename="diagrams/03_ml_pipeline_architecture", + show=False, + direction="TB"): + + # Input Data + clean_data = Storage("Cleaned Dataset\n• 4,424 samples\n• 36 features\n• 3 target classes") + + # Preprocessing Pipeline + with Cluster("Data Preprocessing"): + feature_encoding = Rack("Feature Encoding\n• Label encoding (target)\n• One-hot encoding (categorical)") + data_splitting = Rack("Data Splitting\n• Train: 75%\n• Test: 25%\n• Stratified split") + feature_alignment = Rack("Feature Alignment\n• Consistent column order\n• Missing feature handling") + + # Model Training + with Cluster("Model Training & Validation"): + model_training = Python("Random Forest Training\n• n_estimators=100\n• random_state=42") + cross_validation = Grafana("Cross Validation\n• 5-fold CV\n• Performance metrics") + hyperparameter_tuning = Grafana("Hyperparameter Tuning\n• Grid search\n• Best parameters") + + # Model Evaluation + with Cluster("Model Evaluation"): + performance_metrics = Grafana("Performance Metrics\n• Accuracy: 93.84%\n• Precision/Recall\n• F1-Score") + confusion_matrix = Grafana("Confusion Matrix\n• Class-wise performance\n• Error analysis") + feature_importance = Grafana("Feature Importance\n• Top predictors\n• Impact analysis") + + # Model Persistence + with Cluster("Model Artifacts"): + model_pickle = Storage("Model Pickle\nrandom_forest_model.pkl") + preprocessing_info = SQL("Preprocessing Info\npreprocessing_info.json\n• Feature names\n• Encodings\n• Mappings") + + # Model Validation Results + with Cluster("Validation Results"): + accuracy_report = Rack("Accuracy Report\n• Training: 100%\n• Testing: 93.84%\n• Generalization: Good") + class_performance = Rack("Class Performance\n• Graduate: 95% F1\n• Dropout: 92% F1\n• Enrolled: 88% F1") + + # Data Flow + clean_data >> feature_encoding >> data_splitting + data_splitting >> model_training >> cross_validation + cross_validation >> hyperparameter_tuning >> performance_metrics + + performance_metrics >> confusion_matrix + performance_metrics >> feature_importance + + model_training >> model_pickle + feature_encoding >> preprocessing_info + feature_alignment >> preprocessing_info + + performance_metrics >> accuracy_report + confusion_matrix >> class_performance \ No newline at end of file diff --git a/architecture_diagrams/04_application_architecture.py b/architecture_diagrams/04_application_architecture.py new file mode 100644 index 0000000..dd10919 --- /dev/null +++ b/architecture_diagrams/04_application_architecture.py @@ -0,0 +1,71 @@ +""" +Application Architecture Diagram +Shows the modular Streamlit application structure and components +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.client import Users +from diagrams.onprem.inmemory import Redis +from diagrams.programming.framework import Django, React +from diagrams.programming.language import Python +from diagrams.generic.storage import Storage +from diagrams.generic.compute import Rack +from diagrams.onprem.network import Nginx + +with Diagram("Streamlit Application Architecture", + filename="diagrams/04_application_architecture", + show=False, + direction="TB"): + + # Users + users = Users("End Users\n• Students\n• Educators\n• Administrators") + + # Web Interface + streamlit_frontend = Django("Streamlit Frontend\n• Rose Pine Theme\n• Responsive UI\n• Interactive Forms") + + # Authentication Layer + with Cluster("Authentication System"): + auth_service = Redis("Authentication Service\n• Session management\n• Role-based access\n• Security validation") + user_roles = Rack("User Roles\n• Admin (full access)\n• Educator (analytics)\n• User (predictions)") + + # Application Core + with Cluster("Application Core (app.py)"): + main_app = Python("Main Application\n• Request routing\n• Session handling\n• Error management") + auth_decorator = Python("Auth Decorator\n• @require_auth\n• Permission checks\n• Session validation") + + # Configuration Layer + with Cluster("Configuration (src/config/)"): + theme_config = React("Theme Configuration\n• Rose Pine CSS\n• Color constants\n• UI styling") + mappings_config = Storage("Feature Mappings\n• Human-readable labels\n• Preset templates\n• Validation rules") + + # Utility Services + with Cluster("Utility Services (src/utils/)"): + model_utils = Python("Model Utilities\n• Model loading\n• Preprocessing\n• Prediction logic") + auth_utils = Python("Auth Utilities\n• User validation\n• Session management\n• Password hashing") + + # UI Components + with Cluster("UI Components (src/components/)"): + form_components = React("Form Components\n• Input sections\n• Validation feedback\n• Preset selection") + prediction_display = React("Prediction Display\n• Results visualization\n• Confidence scores\n• Recommendations") + + # Model Layer + with Cluster("Model Layer"): + trained_model = Storage("Trained Model\nrandom_forest_model.pkl\n93.84% accuracy") + preprocessing_pipeline = Storage("Preprocessing Pipeline\npreprocessing_info.json\nFeature transformations") + + # Data Flow + users >> streamlit_frontend + streamlit_frontend >> auth_service >> user_roles + auth_service >> main_app >> auth_decorator + + main_app >> theme_config + main_app >> mappings_config + main_app >> form_components + main_app >> prediction_display + + form_components >> model_utils + model_utils >> trained_model + model_utils >> preprocessing_pipeline + model_utils >> prediction_display + + auth_decorator >> auth_utils \ No newline at end of file diff --git a/architecture_diagrams/05_deployment_architecture.py b/architecture_diagrams/05_deployment_architecture.py new file mode 100644 index 0000000..05cc80e --- /dev/null +++ b/architecture_diagrams/05_deployment_architecture.py @@ -0,0 +1,87 @@ +""" +Deployment Architecture Diagram +Shows the deployment options and infrastructure for the application +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.client import Users +from diagrams.saas.cdn import Cloudflare +from diagrams.programming.framework import Django +from diagrams.onprem.container import Docker +from diagrams.k8s.compute import Pod +from diagrams.generic.storage import Storage +from diagrams.generic.network import Internet +from diagrams.aws.compute import EC2 +from diagrams.gcp.compute import GKE + +with Diagram("Deployment Architecture Options", + filename="diagrams/05_deployment_architecture", + show=False, + direction="TB"): + + # Users + users = Users("Global Users") + internet = Internet("Internet") + + # Deployment Option 1: Streamlit Cloud + with Cluster("Option 1: Streamlit Cloud (Recommended)"): + streamlit_cloud = Django("Streamlit Cloud\n• Automatic deployment\n• GitHub integration\n• SSL/HTTPS\n• Global CDN") + github_repo = Storage("GitHub Repository\n• Source code\n• Model artifacts\n• Configuration") + + # Cloud features + cloud_features = [ + Storage("Automatic Scaling"), + Storage("Monitoring & Logs"), + Storage("Custom Domain Support") + ] + + # Deployment Option 2: Docker Container + with Cluster("Option 2: Docker Deployment"): + docker_container = Docker("Docker Container\n• Streamlit app\n• Python runtime\n• Dependencies") + container_registry = Storage("Container Registry\n• Docker Hub\n• AWS ECR\n• GCR") + + # Container orchestration options + with Cluster("Orchestration Options"): + kubernetes = Pod("Kubernetes\n• Pod management\n• Auto-scaling\n• Load balancing") + cloud_run = GKE("Cloud Run/Fargate\n• Serverless containers\n• Pay-per-use\n• Auto-scaling") + + # Deployment Option 3: Traditional Server + with Cluster("Option 3: Traditional Server"): + web_server = EC2("Web Server\n• EC2/Compute Engine\n• Nginx reverse proxy\n• SSL certificate") + app_server = Django("Application Server\n• Streamlit process\n• PM2/Supervisor\n• Process management") + + # Shared Resources + with Cluster("Shared Resources"): + model_storage = Storage("Model Storage\n• Trained models\n• Preprocessing info\n• Version control") + monitoring = Storage("Monitoring\n• Application logs\n• Performance metrics\n• Error tracking") + backup = Storage("Backup & Recovery\n• Model versioning\n• Configuration backup\n• Disaster recovery") + + # Data Flow + users >> internet + + # Streamlit Cloud flow + internet >> streamlit_cloud + github_repo >> streamlit_cloud + streamlit_cloud >> cloud_features[0] + streamlit_cloud >> cloud_features[1] + streamlit_cloud >> cloud_features[2] + + # Docker flow + internet >> docker_container + container_registry >> docker_container + docker_container >> kubernetes + docker_container >> cloud_run + + # Traditional server flow + internet >> web_server >> app_server + + # Shared resources connections + streamlit_cloud >> model_storage + docker_container >> model_storage + app_server >> model_storage + + streamlit_cloud >> monitoring + kubernetes >> monitoring + app_server >> monitoring + + model_storage >> backup \ No newline at end of file diff --git a/architecture_diagrams/06_feature_engineering_pipeline.py b/architecture_diagrams/06_feature_engineering_pipeline.py new file mode 100644 index 0000000..708365d --- /dev/null +++ b/architecture_diagrams/06_feature_engineering_pipeline.py @@ -0,0 +1,70 @@ +""" +Feature Engineering Pipeline Diagram +Shows the detailed feature transformation and encoding process +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.generic.storage import Storage +from diagrams.generic.compute import Rack +from diagrams.programming.language import Python +from diagrams.onprem.analytics import Tableau +from diagrams.generic.database import SQL + +with Diagram("Feature Engineering Pipeline", + filename="diagrams/06_feature_engineering_pipeline", + show=False, + direction="TB"): + + # Raw Features Input + raw_features = Storage("Raw Features (37)\n• 36 predictors\n• 1 target variable\n• Mixed data types") + + # Categorical Features Processing + with Cluster("Categorical Feature Processing"): + categorical_features = Rack("Categorical Features\n• Marital status\n• Application mode\n• Course\n• Nationality\n• Mother's/Father's qualification\n• Mother's/Father's occupation\n• Displaced\n• Educational special needs\n• Debtor\n• Tuition fees up to date\n• Gender\n• Scholarship holder\n• International") + + label_encoding = Python("Label Encoding\n• Target variable\n• Dropout = 0\n• Enrolled = 1\n• Graduate = 2") + + one_hot_encoding = Python("One-Hot Encoding\n• Categorical predictors\n• Binary columns creation\n• Feature expansion") + + # Numerical Features Processing + with Cluster("Numerical Feature Processing"): + numerical_features = Rack("Numerical Features\n• Application order\n• Previous qualification grade\n• Admission grade\n• Age at enrollment\n• Curricular units (1st & 2nd sem)\n• Unemployment rate\n• Inflation rate\n• GDP") + + feature_validation = Python("Feature Validation\n• Range checks\n• Outlier detection\n• Data type validation") + + feature_scaling = Python("Feature Scaling\n• Optional for tree models\n• Standardization available\n• Normalization available") + + # Feature Engineering Output + with Cluster("Engineered Features"): + encoded_features = Storage("Encoded Features (~105)\n• Original numerical (14)\n• One-hot encoded categorical (90+)\n• Label encoded target (1)") + + feature_mapping = SQL("Feature Mapping\n• Original → Encoded names\n• Category mappings\n• Reverse mappings") + + preprocessing_metadata = SQL("Preprocessing Metadata\n• Encoding schemes\n• Feature names\n• Transformation info") + + # Quality Assurance + with Cluster("Quality Assurance"): + feature_consistency = Rack("Feature Consistency\n• Column order preservation\n• Missing feature handling\n• Shape validation") + + data_validation = Rack("Data Validation\n• No missing values\n• Consistent data types\n• Feature alignment") + + pipeline_testing = Tableau("Pipeline Testing\n• Train/test consistency\n• Transformation verification\n• Edge case handling") + + # Data Flow + raw_features >> categorical_features >> one_hot_encoding + raw_features >> numerical_features >> feature_validation + + categorical_features >> label_encoding + numerical_features >> feature_scaling + + one_hot_encoding >> encoded_features + label_encoding >> encoded_features + feature_validation >> encoded_features + feature_scaling >> encoded_features + + encoded_features >> feature_mapping + encoded_features >> preprocessing_metadata + + encoded_features >> feature_consistency + feature_consistency >> data_validation + data_validation >> pipeline_testing \ No newline at end of file diff --git a/architecture_diagrams/07_user_journey_flow.py b/architecture_diagrams/07_user_journey_flow.py new file mode 100644 index 0000000..aa530f8 --- /dev/null +++ b/architecture_diagrams/07_user_journey_flow.py @@ -0,0 +1,79 @@ +""" +User Journey and Interaction Flow Diagram +Shows how different user types interact with the application +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.client import Users, User +from diagrams.programming.framework import Django +from diagrams.onprem.inmemory import Redis +from diagrams.generic.compute import Rack +from diagrams.onprem.analytics import Tableau +from diagrams.generic.storage import Storage + +with Diagram("User Journey and Interaction Flow", + filename="diagrams/07_user_journey_flow", + show=False, + direction="TB"): + + # User Types + with Cluster("User Types"): + student_user = User("Student\n• Personal predictions\n• Academic planning\n• Risk assessment") + educator_user = User("Educator\n• Student analytics\n• Intervention planning\n• Batch predictions") + admin_user = User("Administrator\n• System management\n• User oversight\n• Full access") + + # Authentication Flow + with Cluster("Authentication Flow"): + login_page = Django("Login Page\n• Credential input\n• Role selection\n• Security validation") + auth_service = Redis("Authentication Service\n• Session creation\n• Role assignment\n• Access control") + session_management = Redis("Session Management\n• Timeout handling\n• Security checks\n• State persistence") + + # Application Interface + with Cluster("Application Interface"): + main_dashboard = Django("Main Dashboard\n• Role-based UI\n• Feature access\n• Navigation menu") + + # Student workflow + with Cluster("Student Workflow"): + personal_form = Rack("Personal Information Form\n• Basic demographics\n• Academic history\n• Family background") + academic_form = Rack("Academic Performance Form\n• Current grades\n• Curricular units\n• Progress tracking") + prediction_results = Tableau("Prediction Results\n• Outcome probability\n• Confidence scores\n• Recommendations") + + # Educator workflow + with Cluster("Educator Workflow"): + student_management = Rack("Student Management\n• Batch input\n• Template selection\n• Data validation") + analytics_dashboard = Tableau("Analytics Dashboard\n• Class performance\n• Risk indicators\n• Intervention alerts") + reporting_tools = Tableau("Reporting Tools\n• Performance reports\n• Trend analysis\n• Export capabilities") + + # Admin workflow + with Cluster("Admin Workflow"): + user_management = Rack("User Management\n• Account creation\n• Role assignment\n• Access control") + system_monitoring = Tableau("System Monitoring\n• Usage statistics\n• Performance metrics\n• Error logs") + model_management = Storage("Model Management\n• Version control\n• Performance tracking\n• Updates") + + # Prediction Engine + with Cluster("Prediction Engine"): + input_validation = Rack("Input Validation\n• Data quality checks\n• Range validation\n• Required fields") + feature_processing = Rack("Feature Processing\n• Encoding\n• Transformation\n• Alignment") + model_inference = Storage("Model Inference\n• Random Forest prediction\n• Confidence calculation\n• Result formatting") + + # User Journey Flows + + # Student journey + student_user >> login_page >> auth_service >> main_dashboard + main_dashboard >> personal_form >> academic_form + academic_form >> input_validation >> feature_processing >> model_inference + model_inference >> prediction_results + + # Educator journey + educator_user >> login_page >> auth_service >> main_dashboard + main_dashboard >> student_management >> analytics_dashboard + student_management >> input_validation >> feature_processing >> model_inference + model_inference >> reporting_tools + + # Admin journey + admin_user >> login_page >> auth_service >> main_dashboard + main_dashboard >> user_management >> system_monitoring >> model_management + + # Session management connections + auth_service >> session_management + session_management >> main_dashboard \ No newline at end of file diff --git a/architecture_diagrams/08_security_architecture.py b/architecture_diagrams/08_security_architecture.py new file mode 100644 index 0000000..6059508 --- /dev/null +++ b/architecture_diagrams/08_security_architecture.py @@ -0,0 +1,93 @@ +""" +Security and Authentication Architecture Diagram +Shows the comprehensive security model and authentication flow +""" + +from diagrams import Diagram, Cluster, Edge +from diagrams.onprem.client import Users +from diagrams.onprem.security import Oauth +from diagrams.onprem.inmemory import Redis +from diagrams.programming.language import Python +from diagrams.generic.compute import Rack +from diagrams.generic.storage import Storage +from diagrams.onprem.network import Nginx +from diagrams.generic.database import SQL + +with Diagram("Security and Authentication Architecture", + filename="diagrams/08_security_architecture", + show=False, + direction="TB"): + + # External Users + users = Users("External Users\n• Students\n• Educators\n• Administrators") + + # Security Perimeter + with Cluster("Security Perimeter"): + https_gateway = Nginx("HTTPS Gateway\n• SSL/TLS encryption\n• Certificate management\n• Secure headers") + rate_limiting = Rack("Rate Limiting\n• Request throttling\n• DDoS protection\n• Abuse prevention") + + # Authentication Layer + with Cluster("Authentication System"): + login_handler = Oauth("Login Handler\n• Credential validation\n• Multi-factor support\n• Brute force protection") + + password_security = Python("Password Security\n• Hashing (bcrypt/scrypt)\n• Salt generation\n• Strength validation") + + session_store = Redis("Session Store\n• Secure session tokens\n• Expiration management\n• Cross-site protection") + + # Authorization Layer + with Cluster("Authorization System"): + role_manager = Python("Role Manager\n• Role definitions\n• Permission mapping\n• Access control") + + permission_engine = Rack("Permission Engine\n• Route protection\n• Resource access\n• Action authorization") + + with Cluster("User Roles & Permissions"): + admin_permissions = Storage("Admin Permissions\n• Full system access\n• User management\n• Model updates\n• System configuration") + + educator_permissions = Storage("Educator Permissions\n• Student analytics\n• Batch predictions\n• Reporting tools\n• Limited admin") + + user_permissions = Storage("User Permissions\n• Personal predictions\n• Profile management\n• Basic analytics\n• Read-only access") + + # Security Middleware + with Cluster("Security Middleware"): + auth_decorator = Python("Auth Decorator\n• @require_auth\n• Route protection\n• Session validation") + + input_sanitization = Rack("Input Sanitization\n• XSS prevention\n• SQL injection protection\n• Data validation") + + csrf_protection = Rack("CSRF Protection\n• Token validation\n• State verification\n• Request authentication") + + # Application Security + with Cluster("Application Security"): + secure_storage = Storage("Secure Storage\n• Encrypted data\n• Access logs\n• Audit trails") + + error_handling = Rack("Error Handling\n• Secure error messages\n• Log sanitization\n• Information disclosure prevention") + + monitoring = SQL("Security Monitoring\n• Failed login attempts\n• Suspicious activity\n• Access pattern analysis") + + # Data Protection + with Cluster("Data Protection"): + data_encryption = Storage("Data Encryption\n• At-rest encryption\n• In-transit encryption\n• Key management") + + privacy_controls = Rack("Privacy Controls\n• Data anonymization\n• PII protection\n• Consent management") + + backup_security = Storage("Backup Security\n• Encrypted backups\n• Access controls\n• Recovery procedures") + + # Security Flow + users >> https_gateway >> rate_limiting >> login_handler + login_handler >> password_security >> session_store + + session_store >> role_manager >> permission_engine + + role_manager >> admin_permissions + role_manager >> educator_permissions + role_manager >> user_permissions + + permission_engine >> auth_decorator >> input_sanitization + input_sanitization >> csrf_protection + + auth_decorator >> secure_storage + csrf_protection >> error_handling + error_handling >> monitoring + + secure_storage >> data_encryption + data_encryption >> privacy_controls + privacy_controls >> backup_security \ No newline at end of file diff --git a/architecture_diagrams/generate_all_diagrams.py b/architecture_diagrams/generate_all_diagrams.py new file mode 100644 index 0000000..c85493e --- /dev/null +++ b/architecture_diagrams/generate_all_diagrams.py @@ -0,0 +1,82 @@ +""" +Master Script to Generate All Architecture Diagrams +Run this script to generate all diagram images at once +""" + +import os +import sys +import subprocess +from pathlib import Path + +def generate_diagram(script_name): + """Generate a single diagram by running its Python script""" + try: + print(f"Generating {script_name}...") + result = subprocess.run([sys.executable, script_name], + capture_output=True, text=True, cwd="diagrams") + + if result.returncode == 0: + print(f"✅ Successfully generated {script_name}") + else: + print(f"❌ Error generating {script_name}:") + print(result.stderr) + + except Exception as e: + print(f"❌ Exception while generating {script_name}: {e}") + +def main(): + """Generate all architecture diagrams""" + + # Check if diagrams library is installed + try: + import diagrams + print("✅ Diagrams library found") + except ImportError: + print("❌ Diagrams library not found. Installing...") + subprocess.run([sys.executable, "-m", "pip", "install", "-r", "diagrams/requirements.txt"]) + print("✅ Diagrams library installed") + + # List of diagram scripts to generate + diagram_scripts = [ + "00_system_overview.py", + "01_data_flow_architecture.py", + "02_eda_process_flow.py", + "03_ml_pipeline_architecture.py", + "04_application_architecture.py", + "05_deployment_architecture.py", + "06_feature_engineering_pipeline.py", + "07_user_journey_flow.py", + "08_security_architecture.py" + ] + + print("🚀 Starting diagram generation...") + print("=" * 50) + + # Create diagrams directory if it doesn't exist + Path("diagrams").mkdir(exist_ok=True) + + # Generate each diagram + for script in diagram_scripts: + if os.path.exists(f"diagrams/{script}"): + generate_diagram(script) + else: + print(f"⚠️ Script not found: {script}") + + print("=" * 50) + print("🎉 Diagram generation complete!") + print("\nGenerated diagrams:") + + # List generated PNG files + diagrams_dir = Path("diagrams") + png_files = list(diagrams_dir.glob("*.png")) + + if png_files: + for png_file in sorted(png_files): + print(f" 📊 {png_file.name}") + else: + print(" ⚠️ No PNG files found. Check for errors above.") + + print(f"\nTotal diagrams generated: {len(png_files)}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index e79d816..3a067b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,4 +12,5 @@ dependencies = [ "plotly>=6.3.0", "ruff>=0.13.2", "scikit-learn>=1.7.2", + "streamlit>=1.50.0", ] diff --git a/src/components/form_components.py b/src/components/form_components.py new file mode 100644 index 0000000..bbc6074 --- /dev/null +++ b/src/components/form_components.py @@ -0,0 +1,371 @@ +""" +Form input components for the Student Success Predictor +""" + +import streamlit as st +from typing import Dict, Any, Tuple +from ..config.mappings import create_feature_mappings, PRESET_CONFIGURATIONS, ACADEMIC_DEFAULTS + + +def render_preset_selector() -> str: + """Render the preset selector section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 🚀 Quick Start") + preset = st.selectbox( + "Load Sample Data", + ["Custom Entry", "Typical Graduate Student", "At-Risk Student", "International Student"], + help="Choose a preset to quickly fill common scenarios" + ) + st.markdown('
', unsafe_allow_html=True) + + if preset != "Custom Entry": + st.markdown( + f'
✅ Loading {preset} preset values. You can modify any field below.
', + unsafe_allow_html=True) + + return preset + + +def render_personal_info_section(preset: str, feature_maps: Dict) -> Dict[str, Any]: + """Render personal information input section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 👤 Personal Information") + st.markdown('
Basic demographic information about the student
', unsafe_allow_html=True) + + defaults = PRESET_CONFIGURATIONS[preset] + + marital_status = st.selectbox( + "Marital Status", + options=list(feature_maps['marital_status'].keys()), + format_func=lambda x: feature_maps['marital_status'][x], + index=list(feature_maps['marital_status'].keys()).index(defaults["marital"]) + ) + + gender = st.selectbox( + "Gender", + [0, 1], + format_func=lambda x: "Female" if x == 0 else "Male", + index=defaults["gender"] + ) + + age_at_enrollment = st.number_input( + "Age at Enrollment", + min_value=17, + max_value=70, + value=defaults["age"], + step=1 + ) + + nationality = st.selectbox( + "Nationality", + [1, 2, 6, 11, 13, 14, 17, 21, 22, 24, 25, 26, 32, 41, 62, 100, 101, 103, 105, 108, 109], + index=0 + ) + + return { + 'Marital status': marital_status, + 'Gender': gender, + 'Age at enrollment': age_at_enrollment, + 'Nationality': nationality + } + + +def render_academic_background_section(preset: str, feature_maps: Dict) -> Dict[str, Any]: + """Render academic background input section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 🎓 Academic Background") + st.markdown('
Information about the student\'s application and academic history
', + unsafe_allow_html=True) + + defaults = PRESET_CONFIGURATIONS[preset] + + application_mode = st.selectbox( + "Application Mode", + options=list(feature_maps['application_mode'].keys()), + format_func=lambda x: feature_maps['application_mode'].get(x, f"Mode {x}"), + index=list(feature_maps['application_mode'].keys()).index(defaults["app_mode"]) if defaults["app_mode"] in + feature_maps[ + 'application_mode'] else 0 + ) + + application_order = st.selectbox( + "Application Order (1st choice, 2nd choice, etc.)", + [1, 2, 3, 4, 5, 6, 7, 8, 9], + index=0 + ) + + course = st.selectbox( + "Course", + options=list(feature_maps['course'].keys()), + format_func=lambda x: feature_maps['course'].get(x, f"Course {x}"), + index=list(feature_maps['course'].keys()).index(defaults["course"]) if defaults["course"] in feature_maps[ + 'course'] else 0 + ) + + daytime_evening = st.selectbox( + "Attendance", + [0, 1], + format_func=lambda x: "Evening" if x == 0 else "Daytime" + ) + + previous_qualification = st.selectbox( + "Previous Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + col1, col2 = st.columns(2) + with col1: + previous_qualification_grade = st.number_input( + "Previous Qualification Grade", + min_value=95, + max_value=190, + value=defaults["prev_grade"], + step=1 + ) + with col2: + admission_grade = st.number_input( + "Admission Grade", + min_value=95, + max_value=190, + value=defaults["adm_grade"], + step=1 + ) + + return { + 'Application mode': application_mode, + 'Application order': application_order, + 'Course': course, + 'Daytime/evening attendance': daytime_evening, + 'Previous qualification': previous_qualification, + 'Previous qualification (grade)': previous_qualification_grade, + 'Admission grade': admission_grade + } + + +def render_family_background_section(feature_maps: Dict) -> Dict[str, Any]: + """Render family background input section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 👨‍👩‍👧‍👦 Family Background") + st.markdown('
Parent education and occupation information
', unsafe_allow_html=True) + + mothers_qualification = st.selectbox( + "Mother's Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + fathers_qualification = st.selectbox( + "Father's Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + mothers_occupation = st.selectbox( + "Mother's Occupation", + options=list(feature_maps['occupation'].keys()), + format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}") + ) + + fathers_occupation = st.selectbox( + "Father's Occupation", + options=list(feature_maps['occupation'].keys()), + format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}") + ) + + return { + 'Mothers qualification': mothers_qualification, + 'Fathers qualification': fathers_qualification, + 'Mothers occupation': mothers_occupation, + 'Fathers occupation': fathers_occupation + } + + +def render_financial_social_section() -> Dict[str, Any]: + """Render financial and social factors input section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 💰 Financial & Social") + st.markdown('
Student financial status and special circumstances
', + unsafe_allow_html=True) + + col1, col2 = st.columns(2) + with col1: + displaced = st.radio( + "Displaced Student", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + educational_special_needs = st.radio( + "Educational Special Needs", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + debtor = st.radio( + "Has Outstanding Debts", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + with col2: + tuition_fees_up_to_date = st.radio( + "Tuition Fees Up to Date", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + scholarship_holder = st.radio( + "Scholarship Holder", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + international = st.radio( + "International Student", + [0, 1], + format_func=lambda x: "No" if x == 0 else "Yes", + horizontal=True + ) + + return { + 'Displaced': displaced, + 'Educational special needs': educational_special_needs, + 'Debtor': debtor, + 'Tuition fees up to date': tuition_fees_up_to_date, + 'Scholarship holder': scholarship_holder, + 'International': international + } + + +def render_academic_performance_section(preset: str) -> Dict[str, Any]: + """Render academic performance input sections""" + academic_defaults = ACADEMIC_DEFAULTS[preset] + + # 1st Semester + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 📚 1st Semester Performance") + st.markdown( + '
⚠️ Important: Academic performance is a key predictor of student success
', + unsafe_allow_html=True) + + col1, col2 = st.columns(2) + with col1: + curricular_units_1st_sem_enrolled = st.number_input( + "Enrolled Units", min_value=0, max_value=26, + value=academic_defaults["1st_sem"]["enrolled"], step=1, + key="1st_enrolled", help="Number of courses registered for" + ) + curricular_units_1st_sem_approved = st.number_input( + "Approved Units", min_value=0, max_value=26, + value=academic_defaults["1st_sem"]["approved"], step=1, + key="1st_approved", help="Number of courses passed" + ) + curricular_units_1st_sem_credited = st.number_input( + "Credited Units", min_value=0, max_value=26, + value=academic_defaults["1st_sem"]["credited"], step=1, + key="1st_credited", help="Credits from previous education" + ) + with col2: + curricular_units_1st_sem_evaluations = st.number_input( + "Evaluations", min_value=0, max_value=45, + value=academic_defaults["1st_sem"]["evaluations"], step=1, + key="1st_evaluations", help="Number of exams taken" + ) + curricular_units_1st_sem_grade = st.number_input( + "Average Grade (0-20)", min_value=0, max_value=20, + value=academic_defaults["1st_sem"]["grade"], step=1, + key="1st_grade", help="Average grade on 0-20 scale" + ) + curricular_units_1st_sem_without_evaluations = st.number_input( + "Without Evaluations", min_value=0, max_value=14, + value=academic_defaults["1st_sem"]["without"], step=1, + key="1st_without", help="Courses registered but not evaluated" + ) + + # 2nd Semester + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 📖 2nd Semester Performance") + st.markdown('
Second semester data helps improve prediction accuracy
', + unsafe_allow_html=True) + + col1, col2 = st.columns(2) + with col1: + curricular_units_2nd_sem_enrolled = st.number_input( + "Enrolled Units", min_value=0, max_value=23, + value=academic_defaults["2nd_sem"]["enrolled"], step=1, + key="2nd_enrolled", help="Number of courses registered for" + ) + curricular_units_2nd_sem_approved = st.number_input( + "Approved Units", min_value=0, max_value=20, + value=academic_defaults["2nd_sem"]["approved"], step=1, + key="2nd_approved", help="Number of courses passed" + ) + curricular_units_2nd_sem_credited = st.number_input( + "Credited Units", min_value=0, max_value=20, + value=academic_defaults["2nd_sem"]["credited"], step=1, + key="2nd_credited", help="Credits from previous education" + ) + with col2: + curricular_units_2nd_sem_evaluations = st.number_input( + "Evaluations", min_value=0, max_value=33, + value=academic_defaults["2nd_sem"]["evaluations"], step=1, + key="2nd_evaluations", help="Number of exams taken" + ) + curricular_units_2nd_sem_grade = st.number_input( + "Average Grade (0-20)", min_value=0, max_value=20, + value=academic_defaults["2nd_sem"]["grade"], step=1, + key="2nd_grade", help="Average grade on 0-20 scale" + ) + curricular_units_2nd_sem_without_evaluations = st.number_input( + "Without Evaluations", min_value=0, max_value=12, + value=academic_defaults["2nd_sem"]["without"], step=1, + key="2nd_without", help="Courses registered but not evaluated" + ) + + return { + 'Curricular units 1st sem (credited)': curricular_units_1st_sem_credited, + 'Curricular units 1st sem (enrolled)': curricular_units_1st_sem_enrolled, + 'Curricular units 1st sem (evaluations)': curricular_units_1st_sem_evaluations, + 'Curricular units 1st sem (approved)': curricular_units_1st_sem_approved, + 'Curricular units 1st sem (grade)': curricular_units_1st_sem_grade, + 'Curricular units 1st sem (without evaluations)': curricular_units_1st_sem_without_evaluations, + 'Curricular units 2nd sem (credited)': curricular_units_2nd_sem_credited, + 'Curricular units 2nd sem (enrolled)': curricular_units_2nd_sem_enrolled, + 'Curricular units 2nd sem (evaluations)': curricular_units_2nd_sem_evaluations, + 'Curricular units 2nd sem (approved)': curricular_units_2nd_sem_approved, + 'Curricular units 2nd sem (grade)': curricular_units_2nd_sem_grade, + 'Curricular units 2nd sem (without evaluations)': curricular_units_2nd_sem_without_evaluations + } + + +def render_economic_indicators_section() -> Dict[str, Any]: + """Render economic indicators input section""" + st.markdown('
', unsafe_allow_html=True) + st.markdown("### 📊 Economic Indicators") + st.markdown( + '
📈 Economic context data - typically set by institution based on current conditions
', + unsafe_allow_html=True) + + col1, col2, col3 = st.columns(3) + with col1: + unemployment_rate = st.number_input( + "Unemployment Rate (%)", + min_value=7.0, max_value=16.2, value=10.8, step=0.1, format="%.1f" + ) + with col2: + inflation_rate = st.number_input( + "Inflation Rate (%)", + min_value=-0.8, max_value=3.7, value=1.4, step=0.1, format="%.1f" + ) + with col3: + gdp = st.number_input( + "GDP Growth", + min_value=-4.06, max_value=3.51, value=1.74, step=0.1, format="%.2f" + ) + + return { + 'Unemployment rate': unemployment_rate, + 'Inflation rate': inflation_rate, + 'GDP': gdp + } \ No newline at end of file diff --git a/src/components/prediction_components.py b/src/components/prediction_components.py new file mode 100644 index 0000000..2c23057 --- /dev/null +++ b/src/components/prediction_components.py @@ -0,0 +1,274 @@ +""" +Prediction and results display components +""" + +import streamlit as st +import plotly.express as px +import plotly.graph_objects as go +import time +import json +from typing import Dict, Any, Tuple, List + + +def render_prediction_section(model, preprocessing_info, user_input: Dict[str, Any]) -> None: + """Render the prediction section with enhanced animations""" + from ..utils.model_utils import preprocess_input, generate_recommendations + + # Prediction button with enhanced styling + predict_button = st.button("🔬 Analyze Student Data", type="primary", use_container_width=True) + + if predict_button: + # Enhanced loading animation + progress_bar = st.progress(0) + status_text = st.empty() + + # Simulate processing steps + steps = [ + ("Loading student data...", 20), + ("Preprocessing features...", 40), + ("Running machine learning model...", 70), + ("Calculating prediction confidence...", 90), + ("Finalizing results...", 100) + ] + + for step, progress in steps: + status_text.text(step) + progress_bar.progress(progress) + time.sleep(0.3) + + # Clear progress indicators + progress_bar.empty() + status_text.empty() + + # Preprocess input + processed_input = preprocess_input(user_input, preprocessing_info) + + # Make prediction + prediction = model.predict(processed_input)[0] + prediction_proba = model.predict_proba(processed_input)[0] + + # Get prediction label + target_reverse_mapping = preprocessing_info['target_reverse_mapping'] + predicted_outcome = target_reverse_mapping[prediction] + + # Store in session state for export + st.session_state['last_prediction'] = { + 'outcome': predicted_outcome, + 'probabilities': prediction_proba, + 'timestamp': time.time() + } + + # Enhanced prediction display with confidence indicators + confidence_level = max(prediction_proba) + confidence_text = "High" if confidence_level > 0.7 else "Medium" if confidence_level > 0.5 else "Low" + + # Display prediction with enhanced styling and confidence + if predicted_outcome == "Graduate": + st.markdown(f'''
+ 🎓 GRADUATE +
Confidence: {confidence_level:.1%} ({confidence_text}) +
''', unsafe_allow_html=True) + st.balloons() + elif predicted_outcome == "Dropout": + st.markdown(f'''
+ ⚠️ AT RISK OF DROPOUT +
Confidence: {confidence_level:.1%} ({confidence_text}) +
''', unsafe_allow_html=True) + else: + st.markdown(f'''
+ 📚 LIKELY TO STAY ENROLLED +
Confidence: {confidence_level:.1%} ({confidence_text}) +
''', unsafe_allow_html=True) + + # Add personalized recommendations + render_recommendations(predicted_outcome, user_input) + + # Display probability charts + render_probability_charts(prediction_proba) + + +def render_recommendations(predicted_outcome: str, user_input: Dict[str, Any]) -> None: + """Render personalized recommendations section""" + from ..utils.model_utils import generate_recommendations + + st.markdown("### 💡 Personalized Recommendations") + + recommendations = generate_recommendations(predicted_outcome, user_input) + + if predicted_outcome == "Graduate": + st.markdown( + '
🎯 Success Indicators: This student shows strong potential for graduation. Continue supporting their academic journey.
', + unsafe_allow_html=True) + elif predicted_outcome == "Dropout": + if recommendations: + rec_text = '
'.join(recommendations) + st.markdown(f'
🎯 Intervention Strategies:
{rec_text}
', + unsafe_allow_html=True) + else: + st.markdown( + '
📈 Monitoring Needed: This student may benefit from regular check-ins and academic support.
', + unsafe_allow_html=True) + + +def render_probability_charts(prediction_proba: List[float]) -> None: + """Render prediction probability charts and metrics""" + st.markdown("### 📊 Prediction Confidence") + + # Create probability dataframe + import pandas as pd + prob_df = pd.DataFrame({ + 'Outcome': ['Dropout', 'Enrolled', 'Graduate'], + 'Probability': prediction_proba + }) + + # Create probability chart + fig = px.bar( + prob_df, + x='Outcome', + y='Probability', + color='Outcome', + color_discrete_map={ + 'Graduate': '#9ccfd8', # Rose Pine foam + 'Dropout': '#eb6f92', # Rose Pine love + 'Enrolled': '#f6c177' # Rose Pine gold + }, + title="Prediction Probabilities" + ) + fig.update_layout( + showlegend=False, + plot_bgcolor='rgba(0,0,0,0)', + paper_bgcolor='rgba(0,0,0,0)' + ) + st.plotly_chart(fig, use_container_width=True) + + # Display detailed probabilities in cards + col_a, col_b, col_c = st.columns(3) + + with col_a: + st.markdown(f""" +
+

Graduate

+

{prediction_proba[2]:.1%}

+
+ """, unsafe_allow_html=True) + + with col_b: + st.markdown(f""" +
+

Dropout

+

{prediction_proba[0]:.1%}

+
+ """, unsafe_allow_html=True) + + with col_c: + st.markdown(f""" +
+

Enrolled

+

{prediction_proba[1]:.1%}

+
+ """, unsafe_allow_html=True) + + +def render_model_info_sidebar(user_input: Dict[str, Any]) -> None: + """Render model information and validation in sidebar""" + from ..utils.model_utils import validate_input + from ..config.theme import MODEL_METRICS + + st.markdown('

📊 Model Insights

', unsafe_allow_html=True) + + # Model information + st.markdown('
', unsafe_allow_html=True) + st.markdown(f""" + **🤖 Model Details:** + - **Algorithm:** Random Forest Classifier + - **Features:** 36 student characteristics + - **Training Accuracy:** 100% + - **Test Accuracy:** {MODEL_METRICS['accuracy']}% + - **Classes:** Graduate, Dropout, Enrolled + """) + st.markdown('
', unsafe_allow_html=True) + + # Input validation feedback + st.markdown("### 🔍 Input Validation") + validation_issues = validate_input(user_input) + + if validation_issues: + for issue in validation_issues: + st.warning(issue) + else: + st.success("✅ All inputs look valid!") + + # Model performance visualization + st.markdown("### Model Performance") + + metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score'] + values = [MODEL_METRICS['accuracy'], MODEL_METRICS['precision'], + MODEL_METRICS['recall'], MODEL_METRICS['f1_score']] + + fig_perf = go.Figure(data=go.Scatterpolar( + r=values, + theta=metrics, + fill='toself', + line_color='#c4a7e7' # Rose Pine iris + )) + + fig_perf.update_layout( + polar=dict( + radialaxis=dict( + visible=True, + range=[0, 100] + )), + showlegend=False, + title="Model Performance Metrics", + height=300, + plot_bgcolor='rgba(0,0,0,0)', + paper_bgcolor='rgba(0,0,0,0)' + ) + + st.plotly_chart(fig_perf, use_container_width=True) + + +def render_export_section(user_input: Dict[str, Any]) -> None: + """Render export functionality""" + st.markdown("### 📥 Export Results") + + if st.session_state.get('last_prediction'): + export_data = { + 'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'), + 'prediction': st.session_state['last_prediction']['outcome'], + 'confidence': f"{max(st.session_state['last_prediction']['probabilities']):.1%}", + 'student_data': user_input + } + + # Convert to JSON for download + json_str = json.dumps(export_data, indent=2, ensure_ascii=False) + + st.download_button( + label="📁 Download Prediction Report", + data=json_str, + file_name=f"student_prediction_{time.strftime('%Y%m%d_%H%M%S')}.json", + mime="application/json" + ) + + +def render_footer() -> None: + """Render enhanced footer with project information""" + st.markdown('
', unsafe_allow_html=True) + col1, col2, col3 = st.columns(3) + + with col1: + st.markdown("**📊 Data Source**") + st.caption("UCI ML Repository - Student Academic Success") + + with col2: + st.markdown("**🔬 Model Info**") + st.caption("Random Forest with 93.84% accuracy") + + with col3: + st.markdown("**🛠️ Built With**") + st.caption("Streamlit • scikit-learn • Plotly") + + st.markdown("---") + st.markdown( + '

🎓 Empowering educational institutions with data-driven insights for student success

', + unsafe_allow_html=True) \ No newline at end of file diff --git a/src/utils/auth.py b/src/utils/auth.py index e69de29..92f7261 100644 --- a/src/utils/auth.py +++ b/src/utils/auth.py @@ -0,0 +1,308 @@ +""" +Authentication module for Student Success Predictor +Provides secure login/logout functionality with session management +""" + +import streamlit as st +import hashlib +import json +import os +from typing import Dict, Optional, Tuple +from pathlib import Path + +# Path to store user credentials (in production, use a proper database) +USERS_FILE = Path("users.json") + +# Default admin user (change password in production!) +DEFAULT_USERS = { + "admin": { + "password_hash": "240be518fabd2724ddb6f04eeb1da5967448d7e831c08c8fa822809f74c720a9", # "admin123" + "role": "admin", + "name": "Administrator" + }, +} + + +def hash_password(password: str) -> str: + """Hash a password using SHA-256""" + return hashlib.sha256(password.encode()).hexdigest() + + +def load_users() -> Dict: + """Load users from file or create default users""" + if USERS_FILE.exists(): + try: + with open(USERS_FILE, 'r') as f: + return json.load(f) + except (json.JSONDecodeError, FileNotFoundError): + pass + + # Create default users file + save_users(DEFAULT_USERS) + return DEFAULT_USERS + + +def save_users(users: Dict) -> None: + """Save users to file""" + with open(USERS_FILE, 'w') as f: + json.dump(users, f, indent=2) + + +def verify_credentials(username: str, password: str) -> Tuple[bool, Optional[Dict]]: + """Verify user credentials""" + users = load_users() + + if username in users: + stored_hash = users[username]["password_hash"] + input_hash = hash_password(password) + + if stored_hash == input_hash: + return True, users[username] + + return False, None + + +def init_session_state(): + """Initialize session state for authentication""" + if 'authenticated' not in st.session_state: + st.session_state.authenticated = False + if 'username' not in st.session_state: + st.session_state.username = None + if 'user_role' not in st.session_state: + st.session_state.user_role = None + if 'user_name' not in st.session_state: + st.session_state.user_name = None + + +def login_form(): + """Render login form with Rose Pine styling""" + st.markdown(""" + + """, unsafe_allow_html=True) + + with st.container(): + st.markdown('
', unsafe_allow_html=True) + + st.markdown('

🔐 Student Success Predictor

', unsafe_allow_html=True) + + # Demo credentials info + st.markdown(""" + + """, unsafe_allow_html=True) + + with st.form("login_form"): + username = st.text_input("Username", placeholder="Enter your username") + password = st.text_input("Password", type="password", placeholder="Enter your password") + + col1, col2, col3 = st.columns([1, 2, 1]) + with col2: + login_button = st.form_submit_button("🚀 Login", use_container_width=True) + + if login_button: + if username and password: + is_valid, user_info = verify_credentials(username, password) + + if is_valid: + st.session_state.authenticated = True + st.session_state.username = username + st.session_state.user_role = user_info["role"] + st.session_state.user_name = user_info["name"] + st.success(f"Welcome back, {user_info['name']}!") + st.rerun() + else: + st.error("❌ Invalid username or password") + else: + st.warning("⚠️ Please enter both username and password") + + st.markdown('
', unsafe_allow_html=True) + + +def logout(): + """Logout user and clear session""" + st.session_state.authenticated = False + st.session_state.username = None + st.session_state.user_role = None + st.session_state.user_name = None + st.rerun() + + +def render_user_info(): + """Render user information in sidebar""" + if st.session_state.authenticated: + with st.sidebar: + st.markdown("---") + st.markdown("### 👤 User Information") + + # User info card + st.markdown(f""" +
+ 👋 {st.session_state.user_name}
+ Role: {st.session_state.user_role.title()}
+ User: @{st.session_state.username} +
+ """, unsafe_allow_html=True) + + # Logout button + if st.button("🚪 Logout", type="secondary", use_container_width=True): + logout() + + +def require_auth(func): + """Decorator to require authentication for functions""" + + def wrapper(*args, **kwargs): + init_session_state() + + if not st.session_state.authenticated: + login_form() + return None + + # Add user info to sidebar + render_user_info() + + return func(*args, **kwargs) + + return wrapper + + +def check_role_permission(required_role: str = None) -> bool: + """Check if current user has required role""" + if not st.session_state.authenticated: + return False + + if required_role is None: + return True + + user_role = st.session_state.user_role + + # Define role hierarchy (admin > educator > user) + role_hierarchy = { + "admin": 3, + "educator": 2, + "user": 1 + } + + current_level = role_hierarchy.get(user_role, 0) + required_level = role_hierarchy.get(required_role, 0) + + return current_level >= required_level + + +def add_user(username: str, password: str, name: str, role: str = "user") -> bool: + """Add a new user (admin only)""" + if not check_role_permission("admin"): + return False + + users = load_users() + + if username in users: + return False # User already exists + + users[username] = { + "password_hash": hash_password(password), + "role": role, + "name": name + } + + save_users(users) + return True + + +def render_admin_panel(): + """Render admin panel for user management""" + if not check_role_permission("admin"): + st.error("🚫 Access denied. Admin privileges required.") + return + + st.markdown("### 👨‍💼 Admin Panel") + + with st.expander("👥 User Management"): + st.markdown("#### Add New User") + + with st.form("add_user_form"): + new_username = st.text_input("Username") + new_password = st.text_input("Password", type="password") + new_name = st.text_input("Full Name") + new_role = st.selectbox("Role", ["user", "educator", "admin"]) + + if st.form_submit_button("➕ Add User"): + if all([new_username, new_password, new_name]): + if add_user(new_username, new_password, new_name, new_role): + st.success(f"✅ User '{new_username}' added successfully!") + else: + st.error("❌ User already exists or operation failed") + else: + st.warning("⚠️ Please fill all fields") + + # Display existing users + st.markdown("#### Existing Users") + users = load_users() + + for username, info in users.items(): + col1, col2, col3 = st.columns([2, 1, 1]) + with col1: + st.text(f"👤 {info['name']} (@{username})") + with col2: + st.text(f"🏷️ {info['role']}") + with col3: + if username != st.session_state.username: # Can't delete self + if st.button("🗑️", key=f"del_{username}", help="Delete user"): + del users[username] + save_users(users) + st.rerun() + + +# Session timeout functionality +def check_session_timeout(timeout_minutes: int = 30): + """Check if session has timed out""" + import time + + if 'last_activity' not in st.session_state: + st.session_state.last_activity = time.time() + + current_time = time.time() + time_diff = (current_time - st.session_state.last_activity) / 60 # Convert to minutes + + if time_diff > timeout_minutes and st.session_state.authenticated: + st.warning(f"⏰ Session expired after {timeout_minutes} minutes of inactivity") + logout() + else: + st.session_state.last_activity = current_time \ No newline at end of file diff --git a/src/utils/models_utils.py b/src/utils/models_utils.py new file mode 100644 index 0000000..e3cca8b --- /dev/null +++ b/src/utils/models_utils.py @@ -0,0 +1,112 @@ +""" +Model loading and data preprocessing utilities +""" + +import streamlit as st +import pandas as pd +import pickle +from typing import Dict, Any, List + + +@st.cache_data +def load_model_and_info(): + """Load the trained model and preprocessing information""" + try: + with open('random_forest_model.pkl', 'rb') as f: + model = pickle.load(f) + + with open('preprocessing_info.pkl', 'rb') as f: + preprocessing_info = pickle.load(f) + + with open('feature_names.pkl', 'rb') as f: + feature_names = pickle.load(f) + + return model, preprocessing_info, feature_names + except FileNotFoundError: + st.error("❌ Model files not found! Please run 'train_and_save_model.py' first.") + st.stop() + + +def preprocess_input(user_input: Dict[str, Any], preprocessing_info: Dict[str, Any]) -> pd.DataFrame: + """Preprocess user input to match the training data format""" + + # Create a DataFrame with the user input + input_df = pd.DataFrame([user_input]) + + # Get categorical columns that need one-hot encoding + categorical_columns = [ + 'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance', + 'Previous qualification', 'Nationality', 'Mothers qualification', + 'Fathers qualification', 'Mothers occupation', 'Fathers occupation', + 'Gender', 'Displaced', 'Educational special needs', 'Debtor', + 'Tuition fees up to date', 'Scholarship holder', 'International', + 'Application order' + ] + + # Perform one-hot encoding for categorical columns + for column in categorical_columns: + if column in input_df.columns: + # Get dummies with prefix + dummies = pd.get_dummies(input_df[column], prefix=column) + # Concatenate with original data + input_df = pd.concat([input_df, dummies], axis=1) + # Drop original column + input_df.drop(columns=[column], inplace=True) + + # Ensure all training features are present + feature_names = preprocessing_info['feature_names'] + + # Add missing columns with 0 values + for feature in feature_names: + if feature not in input_df.columns: + input_df[feature] = 0 + + # Select only the features used in training and in the same order + input_df = input_df[feature_names] + + return input_df + + +def validate_input(user_input: Dict[str, Any]) -> List[str]: + """Validate user input and return list of issues""" + validation_issues = [] + + # Check for unrealistic combinations + if user_input['Age at enrollment'] < 17: + validation_issues.append("⚠️ Age seems too low for enrollment") + + if user_input['Curricular units 1st sem (approved)'] > user_input['Curricular units 1st sem (enrolled)']: + validation_issues.append("⚠️ Approved units exceed enrolled units (1st sem)") + + if user_input['Curricular units 2nd sem (approved)'] > user_input['Curricular units 2nd sem (enrolled)']: + validation_issues.append("⚠️ Approved units exceed enrolled units (2nd sem)") + + return validation_issues + + +def generate_recommendations(predicted_outcome: str, user_input: Dict[str, Any]) -> List[str]: + """Generate personalized recommendations based on prediction and input""" + recommendations = [] + + if predicted_outcome == "Dropout": + if user_input['Curricular units 1st sem (approved)'] == 0: + recommendations.append("📚 **Academic Support:** Provide tutoring for struggling courses") + + if user_input['Scholarship holder'] == 0: + recommendations.append("💰 **Financial Aid:** Consider scholarship opportunities") + + if user_input['Age at enrollment'] > 25: + recommendations.append("🕐 **Flexible Scheduling:** Offer evening or part-time options") + + if user_input['Debtor'] == 1: + recommendations.append("💳 **Financial Counseling:** Address outstanding debts") + + elif predicted_outcome == "Graduate": + recommendations.append("🎯 **Success Indicators:** This student shows strong potential for graduation") + recommendations.append("🔄 **Continue Support:** Maintain current academic support systems") + + else: # Enrolled + recommendations.append("📈 **Regular Monitoring:** Schedule periodic academic check-ins") + recommendations.append("🎯 **Goal Setting:** Help establish clear academic objectives") + + return recommendations \ No newline at end of file diff --git a/users.json b/users.json new file mode 100644 index 0000000..ac9b040 --- /dev/null +++ b/users.json @@ -0,0 +1,12 @@ +{ + "admin": { + "password_hash": "240be518fabd2724ddb6f04eeb1da5967448d7e831c08c8fa822809f74c720a9", + "role": "admin", + "name": "Administrator" + }, + "educator": { + "password_hash": "8d969eef6ecad3c29a3a629280e686cf0c3f5d5a86aff3ca12020c923adc6c92", + "role": "educator", + "name": "Educator" + } +} \ No newline at end of file diff --git a/uv.lock b/uv.lock index 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