Update app.py

app.py

@@ -2,7 +2,27 @@ import pandas as pd
 import plotly.express as px
 import streamlit as st
 from transformers import pipeline
-import datetime
+import matplotlib.pyplot as plt
+
+# Function to add custom background image from a URL
+def add_bg_from_url(image_url):
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background-image: url("{image_url}");
+            background-size: cover;
+            background-position: center center;
+            background-repeat: no-repeat;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Add the background image using the provided URL
+background_image_url = 'https://huggingface.co/spaces/engralimalik/Smart-Expense-Tracker/resolve/main/colorful-abstract-textured-background-design.jpg'
+add_bg_from_url(background_image_url)
 
 # File upload
 uploaded_file = st.file_uploader("Upload your expense CSV file", type=["csv"])
@@ -12,11 +32,11 @@ if uploaded_file:
     # Display Dataframe
     st.write(df.head())
 
-    # Initialize Hugging Face model for zero-shot classification
-    classifier = pipeline('zero-shot-classification', model='distilbert-base-uncased')
+    # Initialize Hugging Face model for zero-shot classification (using a better model like roberta-large-mnli)
+    classifier = pipeline('zero-shot-classification', model='roberta-large-mnli')
     categories = ["Groceries", "Rent", "Utilities", "Entertainment", "Dining", "Transportation"]
 
-    # Function to categorize
+    # Function to categorize expenses based on the description
     def categorize_expense(description):
         result = classifier(description, candidate_labels=categories)
         return result['labels'][0]  # Most probable category
@@ -24,6 +44,9 @@ if uploaded_file:
     # Apply categorization
     df['Category'] = df['Description'].apply(categorize_expense)
 
+    # Display categorized data
+    st.write("Categorized Data", df)
+
     # Sidebar for setting the monthly budget using sliders
     st.sidebar.header("Set Your Monthly Budget")
     groceries_budget = st.sidebar.slider("Groceries Budget", 0, 1000, 300)
@@ -43,49 +66,37 @@ if uploaded_file:
         "Transportation": transportation_budget
     }
 
-    # Add a date slider for start and end date (default is the last month)
-    today = datetime.date.today()
-    last_month = today - pd.DateOffset(months=1)
-    start_date = st.sidebar.date_input("Start Date", last_month)
-    end_date = st.sidebar.date_input("End Date", today)
-
-    # Filter data by date range
-    df['Date'] = pd.to_datetime(df['Date'])
-    df_filtered = df[(df['Date'] >= pd.to_datetime(start_date)) & (df['Date'] <= pd.to_datetime(end_date))]
-
     # Track if any category exceeds its budget
-    df_filtered['Budget_Exceeded'] = df_filtered.apply(lambda row: row['Amount'] > budgets.get(row['Category'], 0), axis=1)
+    df['Budget_Exceeded'] = df.apply(lambda row: row['Amount'] > budgets.get(row['Category'], 0), axis=1)
 
     # Show categories that exceeded their budget
-    exceeded_budget = df_filtered[df_filtered['Budget_Exceeded'] == True]
+    exceeded_budget = df[df['Budget_Exceeded'] == True]
     st.write("Categories that exceeded the budget:", exceeded_budget[['Date', 'Category', 'Amount']])
 
     # Visualizations
 
     # 1. Pie Chart for expense distribution by category
-    category_expenses = df_filtered.groupby('Category')['Amount'].sum()
+    category_expenses = df.groupby('Category')['Amount'].sum()
     fig1 = px.pie(category_expenses, values=category_expenses.values, names=category_expenses.index, title="Expense Distribution by Category")
     st.plotly_chart(fig1)
 
     # 2. Monthly Spending Trends (Line Chart)
-    df_filtered['Month'] = df_filtered['Date'].dt.to_period('M').astype(str)  # Convert Period to string for Plotly compatibility
-    monthly_expenses = df_filtered.groupby('Month')['Amount'].sum()
+    df['Date'] = pd.to_datetime(df['Date'])
+    df['Month'] = df['Date'].dt.to_period('M').astype(str)  # Convert Period to string for Plotly compatibility
+    monthly_expenses = df.groupby('Month')['Amount'].sum()
 
-    # Convert monthly_expenses into DataFrame for correct plotting
-    monthly_expenses_df = monthly_expenses.reset_index()
-    if not monthly_expenses_df.empty:
-        fig2 = px.line(monthly_expenses_df, x='Month', y='Amount', title="Monthly Expenses", labels={"Month": "Month", "Amount": "Amount ($)"})
-        st.plotly_chart(fig2)
-    else:
-        st.write("No data to display for the selected date range.")
+    fig2 = px.line(monthly_expenses, x=monthly_expenses.index, y=monthly_expenses.values, title="Monthly Expenses", labels={"x": "Month", "y": "Amount ($)"})
+    st.plotly_chart(fig2)
 
     # 3. Monthly Spending vs Budget (Bar Chart)
-    if not monthly_expenses_df.empty:
-        monthly_expenses_df = pd.DataFrame({
-            'Actual': monthly_expenses,
-            'Budget': [sum(budgets.values())] * len(monthly_expenses)  # Same budget for simplicity
-        })
-        fig3 = monthly_expenses_df.plot(kind='bar', figsize=(10, 6))
-        st.pyplot(fig3)
-    else:
-        st.write("No data to display for the selected date range.")
+    monthly_expenses_df = pd.DataFrame({
+        'Actual': monthly_expenses,
+        'Budget': [sum(budgets.values())] * len(monthly_expenses)  # Same budget for simplicity
+    })
+
+    # Create a matplotlib figure for the bar chart
+    fig3, ax = plt.subplots(figsize=(10, 6))
+    monthly_expenses_df.plot(kind='bar', ax=ax)
+    ax.set_title('Monthly Spending vs Budget')
+    ax.set_ylabel('Amount ($)')
+    st.pyplot(fig3)