Update app.py

app.py

@@ -1,91 +1,70 @@
-import streamlit as st
+import gradio as gr
 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
+import plotly.express as px
 
-# Streamlit page configuration
-st.set_page_config(page_title="Smart Expense Tracker", page_icon=":money_with_wings:")
+# Initialize the Hugging Face model for expense categorization
+expense_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-# Title
-st.title("Smart Expense Tracker :money_with_wings:")
+# Function to categorize expenses
+def categorize_transaction_batch(descriptions):
+    candidate_labels = ["Groceries", "Entertainment", "Rent", "Utilities", "Dining", "Transportation", "Shopping", "Others"]
+    return [expense_classifier(description, candidate_labels)["labels"][0] for description in descriptions]
 
-# File uploader to upload CSV
-st.sidebar.header("Upload your expense data")
-uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=["csv"])
+# Function to process the uploaded CSV and generate visualizations
+def process_expenses(file):
+    df = pd.read_csv(file.name)
 
-# 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
+    # Check required columns
     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_batch(descriptions):
-            candidate_labels = ["Groceries", "Entertainment", "Rent", "Utilities", "Dining", "Transportation", "Shopping", "Others"]
-            return [expense_classifier(description, candidate_labels)["labels"][0] for description in descriptions]
-
-        # Batch process all descriptions at once for efficiency
-        df['Category'] = categorize_transaction_batch(df['Description'].tolist())
-
-        # Show categorized data
-        st.write("### Categorized Expense Data", df.head())
-
-        # Visualizations
-
-        # Optimized Pie chart for Category-wise spending
-        category_spending = df.groupby("Category")['Amount'].sum()
-        st.write("### Category-wise Spending")
-        fig, ax = plt.subplots(figsize=(8, 8))
-        category_spending.plot(kind='pie', autopct='%1.1f%%', ax=ax)
-        ax.set_ylabel('')
-        st.pyplot(fig)
-
-        # Optimized 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(figsize=(10, 6))
-        monthly_spending.plot(kind='line', ax=ax)
-        ax.set_ylabel('Amount ($)')
-        ax.set_xlabel('Month')
-        ax.set_title('Monthly Spending Trends')
-        st.pyplot(fig)
-
-        # Optimized Budget Tracker
-        st.sidebar.header("Budget Tracker")
-        category_list = df['Category'].unique()
-        budget_dict = {category: st.sidebar.number_input(f"Set budget for {category}", min_value=0, value=500) for category in category_list}
-
-        # Optimized Budget vs Actual Spending (Bar chart)
-        st.write("### Budget vs Actual Spending")
-        budget_spending = {category: [budget_dict.get(category, 500), category_spending.get(category, 0)] for category in category_list}
-        budget_df = pd.DataFrame(budget_spending, index=["Budget", "Actual"]).T
-
-        fig, ax = plt.subplots(figsize=(10, 6))
-        budget_df.plot(kind='bar', ax=ax)
-        ax.set_ylabel('Amount ($)')
-        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!")
+        return "CSV file should contain 'Date', 'Description', and 'Amount' columns."
+
+    # Categorize the expenses
+    df['Category'] = categorize_transaction_batch(df['Description'].tolist())
+
+    # Pie chart for Category-wise spending
+    category_spending = df.groupby("Category")['Amount'].sum()
+    fig1 = px.pie(category_spending, names=category_spending.index, values=category_spending.values, title="Category-wise Spending")
+    
+    # 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()
+    fig2 = px.line(monthly_spending, x=monthly_spending.index, y=monthly_spending.values, title="Monthly Spending Trends")
+    
+    # Budget vs Actual Spending (Bar chart)
+    category_list = df['Category'].unique()
+    budget_dict = {category: 500 for category in category_list}  # Set default budget of 500 for each category
+    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
+    fig3 = px.bar(budget_df, x=budget_df.index, y=["Budget", "Actual"], title="Budget vs Actual Spending")
+
+    # Suggested savings
+    savings_tips = []
+    for category, actual in category_spending.items():
+        if actual > budget_dict.get(category, 500):
+            savings_tips.append(f"- **{category}**: Over budget by ${actual - budget_dict.get(category, 500)}. Consider reducing this expense.")
+    
+    return df.head(), fig1, fig2, fig3, savings_tips
+
+# Gradio interface definition
+inputs = gr.inputs.File(label="Upload Expense CSV", type="file")
+outputs = [
+    gr.outputs.Dataframe(label="Categorized Expense Data"),
+    gr.outputs.Plotly(label="Category-wise Spending (Pie Chart)"),
+    gr.outputs.Plotly(label="Monthly Spending Trends (Line Chart)"),
+    gr.outputs.Plotly(label="Budget vs Actual Spending (Bar Chart)"),
+    gr.outputs.Textbox(label="Savings Tips")
+]
+
+# Launch Gradio interface
+gr.Interface(
+    fn=process_expenses,
+    inputs=inputs,
+    outputs=outputs,
+    live=True,
+    title="Smart Expense Tracker",
+    description="Upload your CSV of transactions, categorize them, and view insights like spending trends and budget analysis."
+).launch()