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brand_sentiment_analysis / visualization /app.py

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"""
Musora Sentiment Analysis Dashboard
Main Streamlit Application
Run with: streamlit run app.py
"""
import streamlit as st
import sys
from pathlib import Path
import json

# Add parent directory to path
parent_dir = Path(__file__).resolve().parent
sys.path.append(str(parent_dir))

from data.data_loader import SentimentDataLoader
from components.dashboard import render_dashboard
from components.sentiment_analysis import render_sentiment_analysis
from components.reply_required import render_reply_required


# Load configuration
config_path = parent_dir / "config" / "viz_config.json"
with open(config_path, 'r') as f:
    config = json.load(f)

# Page configuration
st.set_page_config(
    page_title=config['page_config']['page_title'],
    page_icon=config['page_config']['page_icon'],
    layout=config['page_config']['layout'],
    initial_sidebar_state=config['page_config']['initial_sidebar_state']
)


def main():
    """
    Main application function
    """
    # Sidebar
    with st.sidebar:
        st.image("visualization/img/musora.png", use_container_width=True)
        st.title("Navigation")

        # Page selection
        page = st.radio(
            "Select Page",
            ["📊 Dashboard", "🔍 Sentiment Analysis", "💬 Reply Required"],
            index=0
        )

        st.markdown("---")

        # Filters section
        st.markdown("### 🔍 Global Filters")

        # Initialize session state for filters
        if 'filters_applied' not in st.session_state:
            st.session_state.filters_applied = False

        # Load data first to get filter options
        with st.spinner("Loading data..."):
            data_loader = SentimentDataLoader()
            df = data_loader.load_data()

        if df.empty:
            st.error("No data available. Please check your Snowflake connection.")
            return

        # Get filter options
        filter_options = data_loader.get_filter_options(df)

        # Platform filter
        selected_platforms = st.multiselect(
            "Platforms",
            options=filter_options['platforms'],
            default=[]
        )

        # Brand filter
        selected_brands = st.multiselect(
            "Brands",
            options=filter_options['brands'],
            default=[]
        )

        # Sentiment filter
        selected_sentiments = st.multiselect(
            "Sentiments",
            options=filter_options['sentiments'],
            default=[]
        )

        # Date range filter (if available)
        if 'comment_timestamp' in df.columns and not df.empty:
            min_date = df['comment_timestamp'].min().date()
            max_date = df['comment_timestamp'].max().date()

            date_range = st.date_input(
                "Date Range",
                value=(min_date, max_date),
                min_value=min_date,
                max_value=max_date
            )
        else:
            date_range = None

        # Apply filters button
        if st.button("🔍 Apply Filters", use_container_width=True):
            st.session_state.filters_applied = True

        # Reset filters button
        if st.button("🔄 Reset Filters", use_container_width=True):
            st.session_state.filters_applied = False
            st.rerun()

        st.markdown("---")

        # Data refresh
        st.markdown("### 🔄 Data Management")

        if st.button("♻️ Reload Data", use_container_width=True):
            st.cache_data.clear()
            st.rerun()

        # Display data info
        st.markdown("---")
        st.markdown("### ℹ️ Data Info")
        st.info(f"**Total Records:** {len(df):,}")

        if 'processed_at' in df.columns and not df.empty:
            last_update = df['processed_at'].max()
            st.info(f"**Last Updated:** {last_update.strftime('%Y-%m-%d %H:%M')}")

    # Apply filters if needed
    if st.session_state.filters_applied:
        df = data_loader.apply_filters(
            df,
            platforms=selected_platforms if selected_platforms else None,
            brands=selected_brands if selected_brands else None,
            sentiments=selected_sentiments if selected_sentiments else None,
            date_range=date_range if date_range and len(date_range) == 2 else None
        )

        # Show filter summary
        if df.empty:
            st.warning("No data matches the selected filters. Please adjust your filters.")
            return
        else:
            st.info(f"Showing {len(df):,} records after applying filters")

    # Main content area - render selected page
    if page == "📊 Dashboard":
        render_dashboard(df)

    elif page == "🔍 Sentiment Analysis":
        render_sentiment_analysis(df)

    elif page == "💬 Reply Required":
        render_reply_required(df)

    # Footer
    st.markdown("---")
    st.markdown(
        """
        <div style='text-align: center; color: gray; padding: 20px;'>
            <p>Musora Sentiment Analysis Dashboard v1.0</p>
            <p>Powered by Streamlit | Data from Snowflake</p>
        </div>
        """,
        unsafe_allow_html=True
    )


if __name__ == "__main__":
    try:
        main()
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")
        st.exception(e)

	"""
	Musora Sentiment Analysis Dashboard
	Main Streamlit Application

	Run with: streamlit run app.py
	"""
	import streamlit as st
	import sys
	from pathlib import Path
	import json

	# Add parent directory to path
	parent_dir = Path(__file__).resolve().parent
	sys.path.append(str(parent_dir))

	from data.data_loader import SentimentDataLoader
	from components.dashboard import render_dashboard
	from components.sentiment_analysis import render_sentiment_analysis
	from components.reply_required import render_reply_required


	# Load configuration
	config_path = parent_dir / "config" / "viz_config.json"
	with open(config_path, 'r') as f:
	config = json.load(f)

	# Page configuration
	st.set_page_config(
	page_title=config['page_config']['page_title'],
	page_icon=config['page_config']['page_icon'],
	layout=config['page_config']['layout'],
	initial_sidebar_state=config['page_config']['initial_sidebar_state']
	)


	def main():
	"""
	Main application function
	"""
	# Sidebar
	with st.sidebar:
	st.image("visualization/img/musora.png", use_container_width=True)
	st.title("Navigation")

	# Page selection
	page = st.radio(
	"Select Page",
	["📊 Dashboard", "🔍 Sentiment Analysis", "💬 Reply Required"],
	index=0
	)

	st.markdown("---")

	# Filters section
	st.markdown("### 🔍 Global Filters")

	# Initialize session state for filters
	if 'filters_applied' not in st.session_state:
	st.session_state.filters_applied = False

	# Load data first to get filter options
	with st.spinner("Loading data..."):
	data_loader = SentimentDataLoader()
	df = data_loader.load_data()

	if df.empty:
	st.error("No data available. Please check your Snowflake connection.")
	return

	# Get filter options
	filter_options = data_loader.get_filter_options(df)

	# Platform filter
	selected_platforms = st.multiselect(
	"Platforms",
	options=filter_options['platforms'],
	default=[]
	)

	# Brand filter
	selected_brands = st.multiselect(
	"Brands",
	options=filter_options['brands'],
	default=[]
	)

	# Sentiment filter
	selected_sentiments = st.multiselect(
	"Sentiments",
	options=filter_options['sentiments'],
	default=[]
	)

	# Date range filter (if available)
	if 'comment_timestamp' in df.columns and not df.empty:
	min_date = df['comment_timestamp'].min().date()
	max_date = df['comment_timestamp'].max().date()

	date_range = st.date_input(
	"Date Range",
	value=(min_date, max_date),
	min_value=min_date,
	max_value=max_date
	)
	else:
	date_range = None

	# Apply filters button
	if st.button("🔍 Apply Filters", use_container_width=True):
	st.session_state.filters_applied = True

	# Reset filters button
	if st.button("🔄 Reset Filters", use_container_width=True):
	st.session_state.filters_applied = False
	st.rerun()

	st.markdown("---")

	# Data refresh
	st.markdown("### 🔄 Data Management")

	if st.button("♻️ Reload Data", use_container_width=True):
	st.cache_data.clear()
	st.rerun()

	# Display data info
	st.markdown("---")
	st.markdown("### ℹ️ Data Info")
	st.info(f"Total Records: {len(df):,}")

	if 'processed_at' in df.columns and not df.empty:
	last_update = df['processed_at'].max()
	st.info(f"Last Updated: {last_update.strftime('%Y-%m-%d %H:%M')}")

	# Apply filters if needed
	if st.session_state.filters_applied:
	df = data_loader.apply_filters(
	df,
	platforms=selected_platforms if selected_platforms else None,
	brands=selected_brands if selected_brands else None,
	sentiments=selected_sentiments if selected_sentiments else None,
	date_range=date_range if date_range and len(date_range) == 2 else None
	)

	# Show filter summary
	if df.empty:
	st.warning("No data matches the selected filters. Please adjust your filters.")
	return
	else:
	st.info(f"Showing {len(df):,} records after applying filters")

	# Main content area - render selected page
	if page == "📊 Dashboard":
	render_dashboard(df)

	elif page == "🔍 Sentiment Analysis":
	render_sentiment_analysis(df)

	elif page == "💬 Reply Required":
	render_reply_required(df)

	# Footer
	st.markdown("---")
	st.markdown(
	"""
	<div style='text-align: center; color: gray; padding: 20px;'>
	<p>Musora Sentiment Analysis Dashboard v1.0</p>
	<p>Powered by Streamlit \| Data from Snowflake</p>
	</div>
	""",
	unsafe_allow_html=True
	)


	if __name__ == "__main__":
	try:
	main()
	except Exception as e:
	st.error(f"An error occurred: {str(e)}")
	st.exception(e)