Add application file

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import random
+from datetime import datetime
+
+# Set page configuration
+st.set_page_config(
+    page_title="Quick Streamlit Demo",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Add custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #1E88E5;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #424242;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# App title
+st.markdown('<p class="main-header">Interactive Streamlit Dashboard</p>', unsafe_allow_html=True)
+st.markdown("This is a quick demo of Streamlit's capabilities.")
+
+# Sidebar
+st.sidebar.title("Controls")
+data_size = st.sidebar.slider("Data Points", 10, 500, 100)
+chart_height = st.sidebar.slider("Chart Height", 300, 800, 400)
+
+# Define color themes dictionary mapping to actual Plotly color sequences
+color_themes = {
+    "blues": px.colors.sequential.Blues,
+    "reds": px.colors.sequential.Reds,
+    "greens": px.colors.sequential.Greens,
+    "purples": px.colors.sequential.Purples,
+    "oranges": px.colors.sequential.Oranges
+}
+
+color_theme = st.sidebar.selectbox("Color Theme", list(color_themes.keys()))
+
+# Generate random data
+@st.cache_data
+def generate_data(n_points):
+    np.random.seed(42)
+    dates = pd.date_range(start=datetime.now().date(), periods=n_points)
+    df = pd.DataFrame({
+        'date': dates,
+        'value': np.random.randn(n_points).cumsum(),
+        'category': np.random.choice(['A', 'B', 'C', 'D'], n_points),
+        'size': np.random.randint(10, 100, n_points)
+    })
+    return df
+
+# Create tabs
+tab1, tab2, tab3 = st.tabs(["Chart", "Data Explorer", "About"])
+
+with tab1:
+    st.markdown('<p class="sub-header">Interactive Data Visualization</p>', unsafe_allow_html=True)
+    
+    # Generate and display data
+    df = generate_data(data_size)
+    
+    # Create a line chart with Plotly
+    fig = px.line(
+        df, x='date', y='value', 
+        color='category', 
+        color_discrete_sequence=color_themes[color_theme],
+        height=chart_height
+    )
+    fig.update_layout(
+        title="Time Series Data",
+        xaxis_title="Date",
+        yaxis_title="Value",
+        legend_title="Category",
+        hovermode="x unified"
+    )
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Add some metrics
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("Max Value", f"{df['value'].max():.2f}", f"{random.uniform(-10, 10):.2f}%")
+    with col2:
+        st.metric("Min Value", f"{df['value'].min():.2f}", f"{random.uniform(-10, 10):.2f}%")
+    with col3:
+        st.metric("Average", f"{df['value'].mean():.2f}", f"{random.uniform(-10, 10):.2f}%")
+    with col4:
+        st.metric("Data Points", len(df), f"{data_size - 50}")
+
+with tab2:
+    st.markdown('<p class="sub-header">Data Explorer</p>', unsafe_allow_html=True)
+    
+    # Filter options
+    st.subheader("Filter Data")
+    
+    # Create columns for filters
+    filter_col1, filter_col2 = st.columns(2)
+    
+    with filter_col1:
+        selected_categories = st.multiselect(
+            "Select Categories",
+            options=df['category'].unique(),
+            default=df['category'].unique()
+        )
+    
+    with filter_col2:
+        min_val, max_val = st.slider(
+            "Value Range",
+            float(df['value'].min()),
+            float(df['value'].max()),
+            [float(df['value'].min()), float(df['value'].max())]
+        )
+    
+    # Filter the data
+    filtered_df = df[
+        (df['category'].isin(selected_categories)) &
+        (df['value'] >= min_val) &
+        (df['value'] <= max_val)
+    ]
+    
+    # Show the filtered dataframe
+    st.subheader(f"Filtered Data ({len(filtered_df)} rows)")
+    st.dataframe(filtered_df, use_container_width=True)
+    
+    # Download button
+    csv = filtered_df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        "Download CSV",
+        csv,
+        "filtered_data.csv",
+        "text/csv",
+        key='download-csv'
+    )
+
+with tab3:
+    st.markdown('<p class="sub-header">About This App</p>', unsafe_allow_html=True)
+    
+    st.markdown("""
+    This is a quick demo showcasing some of Streamlit's features:
+    
+    - **Interactive Charts**: Visualize data dynamically
+    - **Data Filtering**: Filter and explore datasets
+    - **Download Capabilities**: Export data as CSV
+    - **Metrics & KPIs**: Display key performance indicators
+    - **Custom Styling**: Enhance UI with custom CSS
+    
+    Streamlit makes it easy to create data apps with just a few lines of Python code.
+    """)
+    
+    st.info("Made with ❤️ using Streamlit")
+
+# Footer
+st.markdown("---")
+st.markdown("Created as a quick Streamlit demo")