Update app.py

app.py

@@ -36,86 +36,4 @@ 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 = plt.figure(figsize=(8, 8))
-        category_expenses.plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)
-        plt.title('Expense Distribution by Category')
-        plt.ylabel('')
-        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 ($)"}
-        )
-        st.plotly_chart(fig2)
-
-        # Budget and Alerts Example (Tracking if any category exceeds its budget)
-        budgets = {
-            "Groceries": 300,
-            "Rent": 1000,
-            "Utilities": 150,
-            "Entertainment": 100,
-            "Dining": 150,
-            "Transportation": 120,
-        }
-
-        # Track if any category exceeds its budget
-        df['Budget_Exceeded'] = df.apply(lambda row: row['Amount'] > budgets.get(row['Category'], 0), axis=1)
-
-        # Show which categories exceeded their budgets
-        exceeded_budget = df[df['Budget_Exceeded'] == True]
-        st.write("Categories that exceeded the budget:", exceeded_budget[['Date', 'Category', 'Amount']])
-
-        # Visualization 3: Monthly Spending vs Budget (Bar Chart)
-        # Create a new DataFrame to show monthly budget vs actual spending
-        monthly_expenses_df = pd.DataFrame({
-            'Actual': monthly_expenses,
-            'Budget': [sum(budgets.values())] * len(monthly_expenses)  # Same budget for simplicity
-        })
-
-        # Plot a bar chart to compare actual spending vs budget
-        fig3 = monthly_expenses_df.plot(kind='bar', figsize=(10, 6))
-        plt.title('Monthly Spending vs Budget')
-        plt.ylabel('Amount ($)')
-        st.pyplot(fig3)
-
-        # -----------------------------------------
-        # Question Answering Functionality
-        # -----------------------------------------
-
-        # Initialize Hugging Face's question answering model (DistilBERT)
-        qa_model = pipeline('question-answering', model="distilbert-base-uncased-distilled-squad")
-
-        # Convert the DataFrame to a text block suitable for QA
-        # Concatenate relevant information (Description, Amount, Category) to form a knowledge base
-        knowledge_base = "\n".join(df.apply(lambda row: f"Description: {row['Description']}, Amount: {row['Amount']}, Category: {row['Category']}", axis=1))
-
-        # Function to answer questions based on the knowledge base
-        def answer_question(question):
-            result = qa_model(question=question, context=knowledge_base)
-            return result['answer']
-
-        # Test the functionality
-        st.write("Ask a question about your expenses:")
-        user_question = st.text_input("Enter your question:")
-        
-        if user_question:
-            answer = answer_question(user_question)
-            st.write(f"Answer: {answer}")
+        #