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import streamlit as st
import sys
import os
import pandas as pd
import time
# Streamlit page setup
st.set_page_config(
page_title="AutoML",
page_icon="🛸",
layout="wide",
initial_sidebar_state="expanded",
menu_items={"Get Help": None, "Report a bug": None, "About": None},
)
# Add project root and src to Python path
sys.path.extend([
os.path.dirname(os.path.abspath(__file__)), # Project root
os.path.join(os.path.dirname(os.path.abspath(__file__)), "src")
])
# Import loading FIRST before any components
from src.ui.loading import show_loading_state
# Import CSS loader FIRST
from src.ui.css import load_css
# Load CSS immediately after imports
load_css()
# Cached resource loading with TTL to refresh components periodically
@st.cache_resource(ttl=3600) # Cache for 1 hour
def load_components():
"""Cache component imports to avoid reloading on every rerun"""
from src import (
show_footer,
visualize_data,
show_welcome_page,
show_overview_page,
clean_csv,
model_training_tab,
display_ai_insights,
display_model_evaluation
)
return (show_footer, visualize_data,
show_welcome_page, show_overview_page, clean_csv,
model_training_tab, display_ai_insights, display_model_evaluation)
# Cached header rendering
@st.cache_data(ttl=86400) # Cache for 24 hours
def render_header():
"""Cache static header HTML"""
return """
<div class='app-header' style='padding: 1rem 0; margin-bottom: 2rem; text-align: center;'>
<h1 class='app-title' style='margin: 0;'>AutoML</h1>
<p class='app-tagline' style='margin-top: 0;'>Automated Machine Learning Made Simple.</p>
</div>
"""
# Cached data loading
@st.cache_data(ttl=3600) # Cache for 1 hour
def load_default_data():
"""Load and cache the default dataset"""
try:
return pd.read_csv("laptop_data.csv")
except Exception as e:
st.error(f"❌ Error loading default dataset: {str(e)}")
return None
# Performance monitoring decorator
def measure_time(func):
"""Decorator to measure execution time of functions"""
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
execution_time = end_time - start_time
if execution_time > 1.0: # Only log slow operations
print(f"⏱️ {func.__name__} took {execution_time:.2f} seconds to execute")
return result
return wrapper
@measure_time
def main():
"""Optimized main function for Streamlit AutoML app"""
# First show loading screen before anything else
if "initialized" not in st.session_state:
# Show loading animation in full screen mode
with st.container():
show_loading_state()
# Force render loading screen first
st.empty().markdown("<style>#root > div:nth-child(1) > div > div > div > div > section > div {padding: 0rem;}</style>", unsafe_allow_html=True)
# Now load components in background
components = load_components()
(show_footer, visualize_data,
show_welcome_page, show_overview_page, clean_csv,
model_training_tab, display_ai_insights, display_model_evaluation) = components
try:
# Load and clean data with caching
default_df = load_default_data()
if default_df is not None:
cleaned_df, insights = clean_csv(default_df)
# Store everything in session state
st.session_state.update({
"df": cleaned_df,
"insights": insights,
"components": components,
"initialized": True,
"current_tab_index": 0 # Use consistent naming for tab tracking
})
# Rerun to hide loading screen
st.rerun()
else:
st.error("❌ Failed to load default dataset")
return
except Exception as e:
st.error(f"❌ Error during initialization: {str(e)}")
return
# After initialization, show main interface
if "initialized" in st.session_state:
components = st.session_state.components
(show_footer, visualize_data,
show_welcome_page, show_overview_page, clean_csv,
model_training_tab, display_ai_insights, display_model_evaluation) = components
# Render main interface
st.markdown(render_header(), unsafe_allow_html=True)
# Create tabs with tab names as constants to avoid recreation
TAB_NAMES = ["👋 Welcome", "📊 Overview", "📈 Visualization",
"🤖 Model Training", "💡 Insights", "📊 Test Results"]
# Initialize current tab index if not present
if "current_tab_index" not in st.session_state:
st.session_state.current_tab_index = 0
# Create tabs and get the current tab index
tab_index = st.tabs(TAB_NAMES)
# Display content in all tabs
with tab_index[0]:
show_welcome_page()
with tab_index[1]:
show_overview_page()
with tab_index[2]:
visualize_data(st.session_state.df)
with tab_index[3]:
model_training_tab(st.session_state.df)
with tab_index[4]:
display_ai_insights()
with tab_index[5]:
display_model_evaluation()
show_footer()
if __name__ == "__main__":
main()
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