Update app.py

app.py

@@ -36,4 +36,63 @@ if uploaded_file is not None:
         # Show the categorized data
         st.write("Categorized Data:", df.head())
 
-        #
+        # Visualization 1: Pie Chart of Spending by Category
+        category_expenses = df.groupby('Category')['Amount'].sum()
+
+        # Plot pie chart for expense distribution by category
+        fig1, ax1 = plt.subplots(figsize=(8, 8))
+        category_expenses.plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors, ax=ax1)
+        ax1.set_title('Expense Distribution by Category')
+        ax1.set_ylabel('')  # Hide the y-axis label
+        st.pyplot(fig1)
+
+        # Visualization 2: Monthly Spending Trends (Line Chart)
+        # Convert 'Date' to datetime
+        df['Date'] = pd.to_datetime(df['Date'])
+
+        # Extract month-year for grouping and convert the Period to string to avoid JSON serialization issues
+        df['Month'] = df['Date'].dt.to_period('M').astype(str)  # Convert Period to string
+
+        # Group by month and calculate the total amount spent per month
+        monthly_expenses = df.groupby('Month')['Amount'].sum()
+
+        # Plot monthly spending trends as a line chart
+        fig2 = px.line(
+            monthly_expenses, 
+            x=monthly_expenses.index, 
+            y=monthly_expenses.values, 
+            title="Monthly Expenses", 
+            labels={"x": "Month", "y": "Amount ($)"}
+        )
+        st.plotly_chart(fig2)
+
+        # Budget and Alerts Example (Tracking if any category exceeds its budget)
+        budgets = {
+            "Groceries": 300,
+            "Rent": 1000,
+            "Utilities": 150,
+            "Entertainment": 100,
+            "Dining": 150,
+            "Transportation": 120,
+        }
+
+        # Track if any category exceeds its budget
+        df['Budget_Exceeded'] = df.apply(lambda row: row['Amount'] > budgets.get(row['Category'], 0), axis=1)
+
+        # Show which categories exceeded their budgets
+        exceeded_budget = df[df['Budget_Exceeded'] == True]
+        st.write("Categories that exceeded the budget:", exceeded_budget[['Date', 'Category', 'Amount']])
+
+        # Visualization 3: Monthly Spending vs Budget (Bar Chart)
+        # Create a figure explicitly for the bar chart
+        fig3, ax3 = plt.subplots(figsize=(10, 6))  # Create figure and axes
+        monthly_expenses_df = pd.DataFrame({
+            'Actual': monthly_expenses,
+            'Budget': [sum(budgets.values())] * len(monthly_expenses)  # Same budget for simplicity
+        })
+        monthly_expenses_df.plot(kind='bar', ax=ax3)  # Pass the axes to the plot
+        ax3.set_title('Monthly Spending vs Budget')
+        ax3.set_ylabel('Amount ($)')
+
+        # Display the plot with Streamlit
+        st.pyplot(fig3)