mirror of
https://github.com/andwati/predictive_analysis.git
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117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
"""
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Main Streamlit application for Student Success Predictor
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Modular version with separated components
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"""
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import streamlit as st
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import sys
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from pathlib import Path
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# Add src directory to path for imports
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src_path = Path(__file__).parent
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sys.path.append(str(src_path))
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from src.config.theme import ROSE_PINE_THEME, PAGE_CONFIG, APP_TITLE, APP_SUBTITLE
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from src.config.mappings import create_feature_mappings
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from src.utils.model_utils import load_model_and_info
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from src.components.form_components import (
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render_preset_selector,
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render_personal_info_section,
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render_academic_background_section,
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render_family_background_section,
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render_financial_social_section,
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render_academic_performance_section,
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render_economic_indicators_section
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)
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from src.components.prediction_components import (
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render_prediction_section,
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render_model_info_sidebar,
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render_export_section,
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render_footer
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)
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def apply_theme():
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"""Apply the Rose Pine dark theme"""
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st.markdown(ROSE_PINE_THEME, unsafe_allow_html=True)
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def render_header():
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"""Render the main application header"""
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st.markdown(f'<h1 class="main-header">{APP_TITLE}</h1>', unsafe_allow_html=True)
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st.markdown(f'<p class="subtitle">{APP_SUBTITLE}</p>', unsafe_allow_html=True)
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def collect_user_input():
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"""Collect all user input from the sidebar form"""
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feature_maps = create_feature_mappings()
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with st.sidebar:
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# Preset selector
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preset = render_preset_selector()
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# Personal information
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personal_info = render_personal_info_section(preset, feature_maps)
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# Academic background
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academic_bg = render_academic_background_section(preset, feature_maps)
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# Family background
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family_bg = render_family_background_section(feature_maps)
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# Financial and social factors
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financial_social = render_financial_social_section()
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# Academic performance
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academic_performance = render_academic_performance_section(preset)
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# Economic indicators
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economic_indicators = render_economic_indicators_section()
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# Combine all input data
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user_input = {
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**personal_info,
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**academic_bg,
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**family_bg,
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**financial_social,
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**academic_performance,
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**economic_indicators
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}
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return user_input
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def main():
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"""Main Streamlit application"""
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# Configure page
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st.set_page_config(**PAGE_CONFIG)
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# Apply theme
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apply_theme()
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# Load model and preprocessing info
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model, preprocessing_info, feature_names = load_model_and_info()
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# Render header
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render_header()
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# Collect user input
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user_input = collect_user_input()
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# Main content area
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col1, col2 = st.columns([2, 1])
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with col1:
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st.markdown('<h2 class="sub-header">Prediction Results</h2>', unsafe_allow_html=True)
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# Render prediction section
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render_prediction_section(model, preprocessing_info, user_input)
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with col2:
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# Render model info and validation
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render_model_info_sidebar(user_input)
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# Render export section
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render_export_section(user_input)
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# Render footer
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render_footer()
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if __name__ == "__main__":
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main() |