Moved the data, app.py, requirements.txt file out

REQUIREMENTS.txt

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+streamlit
+pandas
+plotly

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime
+import os
+
+# Set page configuration
+st.set_page_config(page_title="Music Popularity Trends", layout="wide")
+
+# Title
+st.title("Music Popularity Trends Over Time")
+
+# Load the data from the 'data' folder
+@st.cache_data
+def load_data():
+    # Define the path to the data folder
+    data_path = os.path.join(os.getcwd(), 'data', 'music_data.csv')
+    # Load the CSV file
+    data = pd.read_csv(data_path)
+    # Convert Album Release Date to datetime
+    data['Album Release Date'] = pd.to_datetime(data['Album Release Date'], errors='coerce')
+    # Extract year and decade
+    data['Year'] = data['Album Release Date'].dt.year
+    data['Decade'] = (data['Year'] // 10) * 10
+    return data
+
+# Load data
+try:
+    df = load_data()
+except FileNotFoundError:
+    st.error("Error: 'music_data.csv' not found in the 'data' folder. Please ensure the file exists.")
+    st.stop()
+
+# Sidebar for filtering
+st.sidebar.header("Filter Options")
+min_year = int(df['Year'].min())
+max_year = int(df['Year'].max())
+year_range = st.sidebar.slider(
+    "Select Year Range",
+    min_year,
+    max_year,
+    (min_year, max_year)
+)
+
+# Filter data based on year range
+filtered_df = df[
+    (df['Year'] >= year_range[0]) & 
+    (df['Year'] <= year_range[1])
+]
+
+# 1. Line Chart - Average Popularity by Decade
+st.header("Average Popularity by Decade")
+decade_avg = filtered_df.groupby('Decade')['Popularity'].mean().reset_index()
+
+fig_line = px.line(
+    decade_avg,
+    x='Decade',
+    y='Popularity',
+    title='Average Song Popularity by Decade',
+    labels={'Popularity': 'Average Popularity', 'Decade': 'Decade'},
+    template='plotly_white'
+)
+
+fig_line.update_layout(
+    xaxis=dict(tickmode='linear', dtick=10),
+    yaxis=dict(range=[0, 100])
+)
+
+st.plotly_chart(fig_line, use_container_width=True)
+
+# 2. Scatter Plot - Individual Song Popularity Over Time
+st.header("Individual Song Popularity Over Time")
+fig_scatter = px.scatter(
+    filtered_df,
+    x='Album Release Date',
+    y='Popularity',
+    hover_data=['Track Name', 'Artist Name(s)'],
+    title='Song Popularity by Release Date',
+    labels={'Album Release Date': 'Release Date', 'Popularity': 'Popularity'},
+    template='plotly_white'
+)
+
+fig_scatter.update_traces(
+    marker=dict(size=8, opacity=0.6),
+    selector=dict(mode='markers')
+)
+
+fig_scatter.update_layout(
+    yaxis=dict(range=[0, 100]),
+    showlegend=False
+)
+
+st.plotly_chart(fig_scatter, use_container_width=True)
+
+# Additional Insights
+st.header("Key Insights")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Most Popular Decade")
+    most_popular_decade = decade_avg.loc[decade_avg['Popularity'].idxmax()]
+    st.write(f"Decade: {int(most_popular_decade['Decade'])}s")
+    st.write(f"Average Popularity: {most_popular_decade['Popularity']:.1f}")
+
+with col2:
+    st.subheader("Most Popular Song")
+    most_popular_song = filtered_df.loc[filtered_df['Popularity'].idxmax()]
+    st.write(f"Track: {most_popular_song['Track Name']}")
+    st.write(f"Artist: {most_popular_song['Artist Name(s)']}")
+    st.write(f"Popularity: {most_popular_song['Popularity']}")
+    st.write(f"Release Year: {int(most_popular_song['Year'])}")
+
+# Notes
+st.markdown("""
+**Notes:**
+- Popularity scores range from 0 to 100
+""")