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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from transformers import pipeline | |
| from io import StringIO | |
| # Streamlit page configuration | |
| st.set_page_config(page_title="Smart Expense Tracker", page_icon=":money_with_wings:") | |
| # Title | |
| st.title("Smart Expense Tracker :money_with_wings:") | |
| # File uploader to upload CSV | |
| st.sidebar.header("Upload your expense data") | |
| uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=["csv"]) | |
| # Check if file is uploaded | |
| if uploaded_file is not None: | |
| # Read CSV | |
| df = pd.read_csv(uploaded_file) | |
| # Display first few rows of the uploaded data | |
| st.write("### Uploaded Data", df.head()) | |
| # Ensure correct column names | |
| if 'Date' not in df.columns or 'Description' not in df.columns or 'Amount' not in df.columns: | |
| st.error("CSV file should contain 'Date', 'Description', and 'Amount' columns.") | |
| else: | |
| # Initialize Hugging Face pipeline for text classification (expense categorization) | |
| expense_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # Function to categorize transactions | |
| def categorize_transaction(description): | |
| candidate_labels = ["Groceries", "Entertainment", "Rent", "Utilities", "Dining", "Transportation", "Shopping", "Others"] | |
| result = expense_classifier(description, candidate_labels) | |
| return result["labels"][0] | |
| # Apply categorization to the descriptions | |
| df['Category'] = df['Description'].apply(categorize_transaction) | |
| # Show categorized data | |
| st.write("### Categorized Expense Data", df.head()) | |
| # Visualizations | |
| # 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)) | |
| ax.set_ylabel('') | |
| st.pyplot(fig) | |
| # 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)) | |
| ax.set_ylabel('Amount ($)') | |
| ax.set_xlabel('Month') | |
| ax.set_title('Monthly Spending Trends') | |
| st.pyplot(fig) | |
| # Budget Tracker | |
| st.sidebar.header("Budget Tracker") | |
| category_list = df['Category'].unique() | |
| budget_dict = {} | |
| for category in category_list: | |
| budget_dict[category] = st.sidebar.number_input(f"Set budget for {category}", min_value=0, value=500) | |
| # Budget vs Actual Spending (Bar chart) | |
| st.write("### Budget vs Actual Spending") | |
| budget_spending = {category: [budget_dict[category], category_spending.get(category, 0)] for category in category_list} | |
| budget_df = pd.DataFrame(budget_spending, index=["Budget", "Actual"]).T | |
| fig, ax = plt.subplots() | |
| budget_df.plot(kind='bar', ax=ax, figsize=(10, 6)) | |
| ax.set_ylabel('Amount ($)') | |
| ax.set_title('Budget vs Actual Spending') | |
| st.pyplot(fig) | |
| # Suggestions for saving | |
| st.write("### Suggested Savings Tips") | |
| for category, actual in category_spending.items(): | |
| if actual > budget_dict.get(category, 500): | |
| st.write(f"- **{category}**: Over budget by ${actual - budget_dict.get(category, 500)}. Consider reducing this expense.") | |
| else: | |
| st.write("Upload a CSV file to start tracking your expenses!") | |