Update app.py

app.py

@@ -3,89 +3,77 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 def process_data(df):
-    # Clean data and handle date parsing
-    df = df[df['Project Category'].notna()]
+    # Clean data and consolidate categories
+    df = df[['Project Category', 'Logged']].copy()
     
-    # Convert date strings to datetime
-    df['Start Date'] = pd.to_datetime(
-        df['Date'].str.split(' to ').str[0], 
-        format='%d/%b/%y', 
-        errors='coerce'
-    )
-    
-    # Filter valid dates and assign weeks
-    df = df.dropna(subset=['Start Date'])
-    df['Week'] = df['Start Date'].apply(
-        lambda x: 1 if x <= pd.Timestamp('2025-01-05') else 2
-    )
-    
-    # Consolidate billable categories
+    # Map to main categories
     df['Category'] = df['Project Category'].apply(
-        lambda x: 'Billable' if 'Billable' in x else x
+        lambda x: 'Billable' if 'Billable' in x else x.strip()
     )
     
     # Aggregate data
-    utilization = df.groupby(['Week', 'Category'])['Logged'].sum().unstack(fill_value=0)
+    summary = df.groupby('Category')['Logged'].sum().reset_index()
+    total = summary['Logged'].sum()
+    summary['Percentage'] = (summary['Logged'] / total * 100).round(1)
     
-    # Select relevant categories and calculate percentages
-    categories = ['Billable', 'Non-Billable', 'Leaves']
-    utilization = utilization.reindex(categories, axis=1, fill_value=0)
-    total_hours = utilization.sum(axis=1)
-    utilization_percent = utilization.div(total_hours, axis=0) * 100
-    
-    return utilization_percent
+    return summary
 
-def create_utilization_chart(week_data, week_number):
+def create_pie_chart(data):
     fig, ax = plt.subplots(figsize=(6, 6))
-    labels = week_data.index[week_data > 0]
-    sizes = week_data[week_data > 0]
-    
     wedges, texts, autotexts = ax.pie(
-        sizes,
-        labels=labels,
+        data['Logged'],
+        labels=data['Category'],
         autopct='%1.1f%%',
         colors=['#4CAF50', '#FFC107', '#9E9E9E'],
         startangle=90
     )
-    
     plt.setp(autotexts, size=10, weight="bold", color='white')
-    ax.set_title(f'Week {week_number} Utilization', pad=20)
+    ax.set_title('Overall Utilization', pad=20)
+    return fig
+
+def create_bar_chart(data):
+    fig, ax = plt.subplots(figsize=(10, 4))
+    data[data['Category'] == 'Non-Billable'].plot(
+        kind='bar',
+        x='Project Category',
+        y='Logged',
+        ax=ax,
+        legend=False
+    )
+    ax.set_title('Non-Billable Details')
+    ax.set_ylabel('Hours')
+    plt.xticks(rotation=45)
     return fig
 
 def main():
-    st.title('QA Team Utilization Dashboard')
+    st.title('QA Utilization Dashboard')
     
-    uploaded_file = st.file_uploader("Upload Tempo Timesheet", type=['xls', 'xlsx'])
+    uploaded_file = st.file_uploader("Upload Timesheet", type=['xls', 'xlsx'])
     
     if uploaded_file:
-        try:
-            df = pd.read_excel(uploaded_file, sheet_name='Report')
-            utilization_percent = process_data(df)
-            
-            # Page 4 Visualization
-            st.header("Bi-Weekly Utilization Report")
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                if 1 in utilization_percent.index:
-                    week1 = utilization_percent.loc[1]
-                    st.pyplot(create_utilization_chart(week1, 1))
-                else:
-                    st.warning("No data for Week 1")
-            
-            with col2:
-                if 2 in utilization_percent.index:
-                    week2 = utilization_percent.loc[2]
-                    st.pyplot(create_utilization_chart(week2, 2))
-                else:
-                    st.warning("No data for Week 2")
-            
-            # Show raw data for verification
-            st.subheader("Processed Data Preview")
-            st.dataframe(utilization_percent)
+        df = pd.read_excel(uploaded_file, sheet_name='Report')
+        processed_data = process_data(df)
+        
+        # Show main visualization
+        st.header("Overall Utilization")
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            st.pyplot(create_pie_chart(processed_data))
             
-        except Exception as e:
-            st.error(f"Error processing file: {str(e)}")
+        with col2:
+            st.dataframe(
+                processed_data[['Category', 'Logged', 'Percentage']],
+                hide_index=True,
+                column_config={
+                    'Logged': 'Hours',
+                    'Percentage': st.column_config.NumberColumn(format="%.1f%%")
+                }
+            )
+        
+        # Show non-billable details
+        st.header("Non-Billable Breakdown")
+        st.pyplot(create_bar_chart(df))
 
 if __name__ == "__main__":
     main()