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import streamlit as st
import pandas as pd
import plotly.express as px
from datetime import datetime, date
from transformers import pipeline
# Initialize sentiment analysis pipeline (Hugging Face)
classifier = pipeline("zero-shot-classification")
# Streamlit App
st.title("Personal Finance Analysis")
# User Input
st.subheader("Enter Transaction Details:")
date_input = st.date_input("Date")
amount = st.number_input("Amount", min_value=0.0)
category = st.selectbox("Category", ["Groceries", "Rent", "Entertainment", "Utilities", "Other"])
description = st.text_input("Description (Optional)") # Add description field
# Spending Limit Input
if "monthly_limit" not in st.session_state:
st.session_state.monthly_limit = 0.0 # Initialize monthly limit
monthly_limit = st.number_input("Set Monthly Spending Limit", min_value=0.0, value=st.session_state.monthly_limit)
st.session_state.monthly_limit = monthly_limit # Update the session state
# Add Transaction Button
if st.button("Add Transaction"):
transaction = {"category": category, "date": str(date_input), "amount": amount, "description": description}
if "transactions" not in st.session_state:
st.session_state.transactions = []
st.session_state.transactions.append(transaction)
st.success("Transaction added successfully!")
# Display Transactions
if "transactions" in st.session_state and st.session_state.transactions:
st.subheader("Transaction History:")
df = pd.DataFrame(st.session_state.transactions)
st.dataframe(df)
# Data Analysis and Charts
st.subheader("Data Analysis:")
# Convert date to datetime for proper sorting and analysis
df['date'] = pd.to_datetime(df['date'])
df['month'] = df['date'].dt.to_period('M') # Add month column for analysis
# Chart 1: Spending by Category (Pie Chart)
category_spending = df.groupby("category")["amount"].sum().reset_index()
fig1 = px.pie(category_spending, values="amount", names="category", title="Spending by Category")
st.plotly_chart(fig1)
# Chart 2: Spending Over Time (Line Chart)
daily_spending = df.groupby("date")["amount"].sum().reset_index()
fig2 = px.line(daily_spending, x="date", y="amount", title="Spending Over Time")
st.plotly_chart(fig2)
# Chart 3: Spending by Category (Bar Chart)
fig3 = px.bar(category_spending, x="category", y="amount", title="Spending by Category (Bar)")
st.plotly_chart(fig3)
# Chart 4: Average Spending per Category
average_spending = df.groupby('category')['amount'].mean().reset_index()
fig4 = px.bar(average_spending, x='category', y='amount', title='Average Spending per Category')
st.plotly_chart(fig4)
# Monthly Spending and Limit Check
current_month = pd.Timestamp.now().to_period('M')
monthly_spent = df[df['month'] == current_month]['amount'].sum()
st.subheader("Monthly Spending Summary")
st.write(f"Current Month: {current_month}")
st.write(f"Total Spent This Month: {monthly_spent:.2f}")
st.write(f"Monthly Limit: {monthly_limit:.2f}")
if monthly_limit > 0: # Check to avoid division by zero
percentage_spent = (monthly_spent / monthly_limit) * 100
st.write(f"Percentage of Limit Used: {percentage_spent:.2f}%")
if monthly_spent > monthly_limit:
st.warning(f"You have exceeded your monthly spending limit! ({monthly_spent:.2f} / {monthly_limit:.2f})")
elif percentage_spent > 80: # Example threshold (80%)
st.info("You are approaching your monthly spending limit.")
# Sentiment Analysis (Example)
if description: # Only perform if the user provided one
candidate_labels = ["positive", "negative", "neutral"]
classified = classifier(description, candidate_labels)
st.write("Sentiment Analysis of Description:", classified) |