Update app.py

app.py

@@ -11,22 +11,111 @@ if uploaded_file is not None:
     # Load the file into a DataFrame
     df = pd.read_csv(uploaded_file)
 
-    # Display the first few rows of the dataset
-    st.write("First few rows of the dataset:", df.head())
+    # Debug: Display the column names to check if 'Description' exists
+    st.write("Columns in the uploaded file:", df.columns)
 
-    # Initialize Hugging Face's zero-shot text classification model
-    model_name = 'distilbert-base-uncased'
-    classifier = pipeline('zero-shot-classification', model=model_name)
+    # Check if the 'Description' column exists
+    if 'Description' not in df.columns:
+        st.error("Error: The CSV file does not contain a 'Description' column.")
+    else:
+        # Initialize Hugging Face's zero-shot text classification model
+        model_name = 'distilbert-base-uncased'
+        classifier = pipeline('zero-shot-classification', model=model_name)
 
-    # List of possible expense categories
-    categories = ["Groceries", "Rent", "Utilities", "Entertainment", "Dining", "Transportation", "Salary"]
+        # List of possible expense categories
+        categories = ["Groceries", "Rent", "Utilities", "Entertainment", "Dining", "Transportation", "Salary"]
 
-    # Function to classify transaction descriptions into categories
-    def categorize_expense(description):
-        result = classifier(description, candidate_labels=categories)
-        return result['labels'][0]  # Choose the most probable category
+        # Function to classify transaction descriptions into categories
+        def categorize_expense(description):
+            result = classifier(description, candidate_labels=categories)
+            return result['labels'][0]  # Choose the most probable category
 
-    # Apply the categorization function to the 'Description' column in the dataset
-    df['Category'] = df['Description'].apply(categorize_expense)
+        # Apply the categorization function to the 'Description' column in the dataset
+        df['Category'] = df['Description'].apply(categorize_expense)
 
-    # Show the ca
+        # 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}")