Create app.py

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from statsmodels.tsa.arima.model import ARIMA
+import matplotlib.pyplot as plt
+
+# Load and cache dataset
+@st.cache
+def load_data():
+    return pd.read_excel('gcp_usage_data_2024.xlsx')
+
+df = load_data()
+
+# Aggregate costs by service description
+service_costs = df.groupby('Service Description')['Cost ($)'].sum()
+
+# Calculate average cost
+average_cost = service_costs.mean()
+
+# Filter services with costs greater than the average cost
+services_above_average = service_costs[service_costs > average_cost].sort_values(ascending=False)
+
+# Forecast future costs for a specific service using ARIMA
+def forecast_costs(service_name, steps=3):
+    service_data = df[df['Service Description'] == service_name].copy()
+    service_data['Date'] = pd.to_datetime(service_data['Date'])
+    service_data.set_index('Date', inplace=True)
+    monthly_costs = service_data['Cost ($)'].resample('M').sum()
+    model = ARIMA(monthly_costs, order=(1, 1, 1))
+    model_fit = model.fit()
+    forecast = model_fit.forecast(steps=steps)
+    return monthly_costs, forecast
+
+# Streamlit UI
+st.title('GCP Cost Analysis and Optimization')
+
+# Display the dataset
+if st.checkbox('Show Raw Data'):
+    st.write(df)
+
+# Display aggregate costs by service
+st.write("### Aggregated Costs by Service")
+st.dataframe(service_costs.sort_values(ascending=False))
+
+# Show services with costs greater than the average
+st.write(f"### Average Cost: ${average_cost:.2f}")
+st.write("### Services with Costs Greater Than Average:")
+st.dataframe(services_above_average)
+
+# Forecast costs
+st.write("### Cost Forecasting")
+service_name = st.selectbox('Select a Service for Forecasting', df['Service Description'].unique())
+if st.button('Forecast Costs'):
+    monthly_costs, forecast = forecast_costs(service_name)
+    
+    st.write("### Forecasted Costs")
+    st.write(forecast)
+
+    # Plot the results
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(monthly_costs, label='Observed Costs')
+    ax.plot(pd.date_range(start=monthly_costs.index[-1], periods=len(forecast) + 1, freq='M')[1:], forecast, label='Forecast', color='red')
+    ax.set_title('Monthly Cost Forecast')
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Cost ($)')
+    ax.legend()
+    st.pyplot(fig)
+
+# Cost Optimization
+st.write("### Cost Optimization Analysis")
+optimization_factor = st.slider('Optimization Factor (%)', min_value=0, max_value=100, value=25)
+df['Optimized Cost ($)'] = df['Cost ($)'] * (1 - optimization_factor / 100)
+
+total_cost_before = df['Cost ($)'].sum()
+total_cost_after = df['Optimized Cost ($)'].sum()
+cost_change_percentage = ((total_cost_before - total_cost_after) / total_cost_before) * 100
+dollar_saving = total_cost_before - total_cost_after
+
+st.write(f"Total Cost Before Optimization: ${total_cost_before:.2f}")
+st.write(f"Total Cost After Optimization: ${total_cost_after:.2f}")
+st.write(f"Percentage Change in Cost: {cost_change_percentage:.2f}%")
+st.write(f"Dollar Saving: ${dollar_saving:.2f}")
+
+# Optionally, show a chart of cost before and after optimization
+fig, ax = plt.subplots(figsize=(10, 6))
+services = df['Service Description'].unique()
+costs_before = df.groupby('Service Description')['Cost ($)'].sum()
+costs_after = df.groupby('Service Description')['Optimized Cost ($)'].sum()
+
+ax.barh(services, costs_before, label='Before Optimization', alpha=0.7)
+ax.barh(services, costs_after, label='After Optimization', alpha=0.7)
+ax.set_title('Cost Before and After Optimization')
+ax.set_xlabel('Cost ($)')
+ax.legend()
+st.pyplot(fig)
+
+