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app.py
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import pandas as pd
import plotly.express as px
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
# Load dataset
#df = pd.read_csv("data/raw/df_5.csv")
df = pd.read_csv("data/raw/df_5.csv", encoding="utf-8", on_bad_lines="skip")
print(df.columns)
df.columns = df.columns.str.replace(r'\xad', '', regex=True) # Removes soft hyphen
df.rename(columns={'codename': 'codename'}, inplace=True) # Rename properly with capitalized format
# Rename column 'Release' to 'Launch Year' if it exists
if 'Release' in df.columns:
df.rename(columns={'Release': 'Launch Year'}, inplace=True)
# Extract year from 'Launch Year' (format: 'Q1 2021')
df['Launch Year (Year Only)'] = df['Launch Year'].str.extract(r'(\d{4})')
df['Launch Year (Year Only)'] = df['Launch Year (Year Only)'].astype(float)
# Initialize Dash app
app = dash.Dash(__name__)
# Layout with sidebar for filters
app.layout = html.Div(style={'backgroundColor': '#0071C5', 'height': '100vh', 'padding': '20px'}, children=[
# Header with Intel logo and title
html.Div([
html.Img(src='/assets/intc.png', style={'width': '120px', 'height': 'auto', 'margin-right': '20px'}),
html.H1("Intel Processor Dashboard", style={'color': 'white', 'margin': '0', 'flex': '1'}),
html.Img(src='https://upload.wikimedia.org/wikipedia/commons/c/c9/Intel-logo.png',
style={'width': '120px', 'height': 'auto', 'margin-left': '20px'})
], style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center', 'margin-bottom': '20px'}),
# Main content layout (Filters on left, Graphs on right)
html.Div([
# Left Panel - Filters
html.Div(style={'width': '25%', 'padding': '20px'}, children=[
html.Div([
html.Label("Select Intel Series:", style={'color': 'white'}),
dcc.Dropdown(
id='series-dropdown',
options=[{'label': series, 'value': series} for series in df['Series'].unique()],
multi=True,
placeholder="Select Intel Series"
)
], style={'margin-bottom': '20px'}),
html.Div([
html.Label("Select Launch Year Range:", style={'color': 'white'}),
dcc.RangeSlider(
id='year-slider',
min=int(df['Launch Year (Year Only)'].min()),
max=int(df['Launch Year (Year Only)'].max()),
value=[int(df['Launch Year (Year Only)'].min()), int(df['Launch Year (Year Only)'].max())],
marks={str(year): {'label': str(year), 'style': {'color': 'black', 'font-size': '16px'}}
for year in range(int(df['Launch Year (Year Only)'].min()), int(df['Launch Year (Year Only)'].max()) + 1, 2)},
step=1
)
], style={'margin-bottom': '20px'}),
html.Div([
html.Label("Select Cache (MB):", style={'color': 'white'}),
dcc.RangeSlider(
id='cache-slider',
min=df['Cache'].min(),
max=df['Cache'].max(),
value=[df['Cache'].min(), df['Cache'].max()],
marks={str(cache): str(cache) for cache in range(int(df['Cache'].min()), int(df['Cache'].max()) + 2, 2)},
step=1
)
], style={'margin-bottom': '20px'}),
html.Div([
html.Label("Select Codename:", style={'color': 'white'}),
dcc.Dropdown(
id='codename-dropdown',
options=[{'label': codename, 'value': codename} for codename in df['codename'].dropna().unique()],
multi=True,
placeholder="Select Codename"
)
], style={'margin-bottom': '20px'}),
]),
# Right Panel - Graphs
html.Div(style={'width': '70%', 'padding': '20px'}, children=[
dcc.Graph(id='price-trend-graph'),
dcc.Graph(id='price-comparison-graph'),
dcc.Graph(id='price-bar-graph'),
dcc.Graph(id='performance-trend-graph')
])
], style={'display': 'flex', 'justify-content': 'space-between'})
])
# Callback to update graphs based on filters
@app.callback(
[
Output('price-trend-graph', 'figure'),
Output('price-comparison-graph', 'figure'),
Output('price-bar-graph', 'figure'),
Output('performance-trend-graph', 'figure')
],
[
Input('series-dropdown', 'value'),
Input('year-slider', 'value'),
Input('cache-slider', 'value'),
Input('codename-dropdown', 'value')
]
)
def update_graphs(selected_series, year_range, selected_cache, selected_codename):
# Ensure selected_series is not None
if not selected_series:
selected_series = df['Series'].unique()
# Ensure selected_codename is not None
if not selected_codename:
selected_codename = df['codename'].dropna().unique()
# Filter dataframe based on selections
filtered_df = df[
(df["Series"].isin(selected_series)) &
(df["Launch Year (Year Only)"] >= year_range[0]) &
(df["Launch Year (Year Only)"] <= year_range[1]) &
(df["Cache"] >= selected_cache[0]) & (df["Cache"] <= selected_cache[1]) &
(df["codename"].isin(selected_codename))
]
# Sort by Launch Year for proper line plotting
filtered_df = filtered_df.sort_values(by="Launch Year (Year Only)")
# Price trends over time (Line Graph)
fig1 = px.line(filtered_df, x="Launch Year (Year Only)", y="Price ($)", color="Series",
title="Price Trends Over Time", markers=True)
fig1.update_traces(marker=dict(size=8))
fig1.update_layout(yaxis=dict(autorange="reversed"))
# Price comparison by series (Box Plot)
fig2 = px.box(filtered_df, x="Series", y="Price ($)", title="Intel Series Price Comparison")
fig2.update_layout(yaxis=dict(autorange="reversed"))
# Price bar graph by year
fig3 = px.bar(filtered_df, x="Launch Year (Year Only)", y="Price ($)", color="Series",
title="Price Over Years")
fig3.update_layout(yaxis=dict(autorange="reversed"))
# Processor vs Performance line graph
fig4 = px.line(filtered_df, x="Launch Year (Year Only)", y="Clock rate (GHz)", color="Series",
title="Processor Performance Over Time", markers=True)
fig4.update_traces(marker=dict(size=8))
return fig1, fig2, fig3, fig4
# Expose Flask server for Gunicorn
server = app.server
if __name__ == '__main__':
app.run_server(debug=True)