Update app.py

app.py

@@ -32,58 +32,54 @@ if uploaded_file is not None:
         expense_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
         # Function to categorize transactions
-        def categorize_transaction(description):
+        def categorize_transaction_batch(descriptions):
             candidate_labels = ["Groceries", "Entertainment", "Rent", "Utilities", "Dining", "Transportation", "Shopping", "Others"]
-            result = expense_classifier(description, candidate_labels)
-            return result["labels"][0]
+            return [expense_classifier(description, candidate_labels)["labels"][0] for description in descriptions]
 
-        # Apply categorization to the descriptions
-        df['Category'] = df['Description'].apply(categorize_transaction)
+        # Batch process all descriptions at once for efficiency
+        df['Category'] = categorize_transaction_batch(df['Description'].tolist())
 
         # Show categorized data
         st.write("### Categorized Expense Data", df.head())
 
         # Visualizations
 
-        # Pie chart for Category-wise spending
+        # Optimized Pie chart for Category-wise spending
         category_spending = df.groupby("Category")['Amount'].sum()
         st.write("### Category-wise Spending")
-        fig, ax = plt.subplots()
-        category_spending.plot(kind='pie', autopct='%1.1f%%', ax=ax, figsize=(8, 8))
+        fig, ax = plt.subplots(figsize=(8, 8))
+        category_spending.plot(kind='pie', autopct='%1.1f%%', ax=ax)
         ax.set_ylabel('')
-        st.pyplot(fig)  # Explicitly call st.pyplot for matplotlib images
+        st.pyplot(fig)
 
-        # Monthly spending trends (Line plot)
+        # Optimized Monthly spending trends (Line plot)
         df['Date'] = pd.to_datetime(df['Date'])
         df['Month'] = df['Date'].dt.to_period('M')
         monthly_spending = df.groupby('Month')['Amount'].sum()
 
         st.write("### Monthly Spending Trends")
-        fig, ax = plt.subplots()
-        monthly_spending.plot(kind='line', ax=ax, figsize=(10, 6))
+        fig, ax = plt.subplots(figsize=(10, 6))
+        monthly_spending.plot(kind='line', ax=ax)
         ax.set_ylabel('Amount ($)')
         ax.set_xlabel('Month')
         ax.set_title('Monthly Spending Trends')
-        st.pyplot(fig)  # Explicitly call st.pyplot for matplotlib images
+        st.pyplot(fig)
 
-        # Budget Tracker
+        # Optimized Budget Tracker
         st.sidebar.header("Budget Tracker")
         category_list = df['Category'].unique()
-        budget_dict = {}
+        budget_dict = {category: st.sidebar.number_input(f"Set budget for {category}", min_value=0, value=500) for category in category_list}
 
-        for category in category_list:
-            budget_dict[category] = st.sidebar.number_input(f"Set budget for {category}", min_value=0, value=500)
-
-        # Budget vs Actual Spending (Bar chart)
+        # Optimized Budget vs Actual Spending (Bar chart)
         st.write("### Budget vs Actual Spending")
-        budget_spending = {category: [budget_dict[category], category_spending.get(category, 0)] for category in category_list}
-
+        budget_spending = {category: [budget_dict.get(category, 500), category_spending.get(category, 0)] for category in category_list}
         budget_df = pd.DataFrame(budget_spending, index=["Budget", "Actual"]).T
-        fig, ax = plt.subplots()
-        budget_df.plot(kind='bar', ax=ax, figsize=(10, 6))
+
+        fig, ax = plt.subplots(figsize=(10, 6))
+        budget_df.plot(kind='bar', ax=ax)
         ax.set_ylabel('Amount ($)')
         ax.set_title('Budget vs Actual Spending')
-        st.pyplot(fig)  # Explicitly call st.pyplot for matplotlib images
+        st.pyplot(fig)
 
         # Suggestions for saving
         st.write("### Suggested Savings Tips")