File size: 4,106 Bytes
a6ee9ca f10ec56 2c359f1 f10ec56 a6ee9ca f10ec56 a6ee9ca f10ec56 a6ee9ca 2c359f1 a6ee9ca 2c359f1 a6ee9ca 6ca4f9e a6ee9ca 6ca4f9e a6ee9ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline
import plotly.express as px
# Initialize Hugging Face model pipelines for categorization and question-answering
expense_classifier = pipeline('zero-shot-classification', model='distilbert-base-uncased')
qa_model = pipeline('question-answering', model='distilbert-base-uncased-distilled-squad')
# List of possible expense categories
categories = ["Groceries", "Rent", "Utilities", "Entertainment", "Dining", "Transportation", "Salary"]
# Function to categorize transactions
def categorize_expense(description):
result = expense_classifier(description, candidate_labels=categories)
return result['labels'][0] # Choose the most probable category
# Streamlit UI
st.title("Smart Expense Tracker")
st.write("""
Upload a CSV file containing your transaction data, and this app will categorize your expenses, visualize trends,
and provide insights on your spending habits.
""")
# Upload CSV file
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
# Display the first few rows of the data
st.subheader("Transaction Data")
st.dataframe(df.head())
# Check if 'Description', 'Amount', and 'Date' columns exist in the file
if 'Description' in df.columns and 'Amount' in df.columns and 'Date' in df.columns:
# Categorize expenses
df['Category'] = df['Description'].apply(categorize_expense)
# Visualizations
st.subheader("Expense Distribution by Category (Pie Chart)")
category_expenses = df.groupby('Category')['Amount'].sum()
fig1 = px.pie(category_expenses, names=category_expenses.index, values=category_expenses.values, title="Category-wise Spending")
st.plotly_chart(fig1)
# Monthly spending trends (Line Chart)
df['Date'] = pd.to_datetime(df['Date'])
df['Month'] = df['Date'].dt.to_period('M')
monthly_expenses = df.groupby('Month')['Amount'].sum()
st.subheader("Monthly Spending Trends")
fig2 = px.line(monthly_expenses, x=monthly_expenses.index, y=monthly_expenses.values, title="Monthly Spending")
st.plotly_chart(fig2)
# Budget vs Actual Spending (Bar Chart)
budgets = {
"Groceries": 300,
"Rent": 1000,
"Utilities": 150,
"Entertainment": 100,
"Dining": 150,
"Transportation": 120,
}
budget_df = pd.DataFrame({
'Actual': monthly_expenses,
'Budget': [sum(budgets.values())] * len(monthly_expenses)
})
st.subheader("Monthly Spending vs Budget")
fig3 = px.bar(budget_df, x=budget_df.index, y=["Actual", "Budget"], title="Budget vs Actual Spending")
st.plotly_chart(fig3)
# Savings Tips (Alert if exceeding budget)
st.subheader("Savings Tips")
savings_tips = []
category_expenses = df.groupby("Category")['Amount'].sum()
for category, actual in category_expenses.items():
if actual > budgets.get(category, 0):
savings_tips.append(f"- **{category}**: Over budget by ${actual - budgets.get(category, 0)}. Consider reducing this expense.")
if savings_tips:
for tip in savings_tips:
st.write(tip)
else:
st.write("No categories exceeded their budget.")
# Question Answering Feature
st.subheader("Ask Questions About Your Expenses")
question = st.text_input("Ask a question about your expenses (e.g., 'How much did I spend on groceries?')")
if question:
knowledge_base = "\n".join(df.apply(lambda row: f"Description: {row['Description']}, Amount: {row['Amount']}, Category: {row['Category']}", axis=1))
answer = qa_model(question=question, context=knowledge_base)
st.write(f"Answer: {answer['answer']}")
else:
st.error("CSV file should contain 'Description', 'Amount', and 'Date' columns.")
|