Create app.py

app.py

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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!")