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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from transformers import pipeline | |
| import plotly.express as px | |
| # Initialize the Hugging Face model for expense categorization (use zero-shot classification) | |
| expense_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # Batch categorization function for efficiency | |
| def categorize_transaction_batch(descriptions): | |
| candidate_labels = ["Groceries", "Entertainment", "Rent", "Utilities", "Dining", "Transportation", "Shopping", "Others"] | |
| return [expense_classifier(description, candidate_labels)["labels"][0] for description in descriptions] | |
| # Function to process the uploaded CSV and generate visualizations | |
| def process_expenses(file): | |
| # Read CSV data | |
| df = pd.read_csv(file.name) | |
| # Check if required columns are present | |
| if 'Date' not in df.columns or 'Description' not in df.columns or 'Amount' not in df.columns: | |
| return "CSV file should contain 'Date', 'Description', and 'Amount' columns." | |
| # Categorize the expenses (using batch processing to minimize model calls) | |
| df['Category'] = categorize_transaction_batch(df['Description'].tolist()) | |
| # Create visualizations: | |
| # 1. Pie chart for Category-wise spending | |
| category_spending = df.groupby("Category")['Amount'].sum() | |
| fig1 = px.pie(category_spending, names=category_spending.index, values=category_spending.values, title="Category-wise Spending") | |
| # 2. 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() | |
| fig2 = px.line(monthly_spending, x=monthly_spending.index, y=monthly_spending.values, title="Monthly Spending Trends") | |
| # 3. Budget vs Actual Spending (Bar chart) | |
| category_list = df['Category'].unique() | |
| budget_dict = {category: 500 for category in category_list} # Default budget is 500 for each category | |
| 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 | |
| fig3 = px.bar(budget_df, x=budget_df.index, y=["Budget", "Actual"], title="Budget vs Actual Spending") | |
| # 4. Suggested savings (only calculate if over budget) | |
| savings_tips = [] | |
| for category, actual in category_spending.items(): | |
| if actual > budget_dict.get(category, 500): | |
| savings_tips.append(f"- **{category}**: Over budget by ${actual - budget_dict.get(category, 500)}. Consider reducing this expense.") | |
| return df.head(), fig1, fig2, fig3, savings_tips | |
| # Gradio interface definition | |
| inputs = gr.File(label="Upload Expense CSV") | |
| outputs = [ | |
| gr.Dataframe(label="Categorized Expense Data"), | |
| gr.Plot(label="Category-wise Spending (Pie Chart)"), | |
| gr.Plot(label="Monthly Spending Trends (Line Chart)"), | |
| gr.Plot(label="Budget vs Actual Spending (Bar Chart)"), | |
| gr.Textbox(label="Savings Tips") | |
| ] | |
| # Launch Gradio interface | |
| gr.Interface( | |
| fn=process_expenses, | |
| inputs=inputs, | |
| outputs=outputs, | |
| live=True, | |
| title="Smart Expense Tracker", | |
| description="Upload your CSV of transactions, categorize them, and view insights like spending trends and budget analysis." | |
| ).launch() | |