app.py

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)