Create app.py

app.py

@@ -0,0 +1,242 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from datetime import datetime
+import os
+import tempfile
+import traceback
+
+# ------------------------
+# Config
+# ------------------------
+st.set_page_config(page_title="Expense Tracker", page_icon="💰", layout="centered")
+DATA_FILE = os.path.join(os.path.dirname(__file__), "expenses.csv")
+
+# ------------------------
+# Helpers
+# ------------------------
+def get_empty_df():
+    return pd.DataFrame(columns=["Date", "Description", "Amount", "Category"])
+
+def load_data():
+    """Load CSV safely and normalize types. Returns DataFrame."""
+    if not os.path.exists(DATA_FILE):
+        return get_empty_df()
+
+    try:
+        df = pd.read_csv(DATA_FILE)
+        # Ensure required columns exist
+        for col in ["Date", "Description", "Amount", "Category"]:
+            if col not in df.columns:
+                df[col] = pd.NA
+
+        # Parse Date to datetime (coerce errors -> NaT)
+        df["Date"] = pd.to_datetime(df["Date"], errors="coerce")
+
+        # Coerce Amount to numeric and fill NaNs with 0.0 (won't crash plots)
+        df["Amount"] = pd.to_numeric(df["Amount"], errors="coerce").fillna(0.0)
+
+        # Ensure Description and Category are strings
+        df["Description"] = df["Description"].astype(str).fillna("")
+        df["Category"] = df["Category"].astype(str).fillna("Other")
+
+        # Re-order columns
+        df = df[["Date", "Description", "Amount", "Category"]]
+        return df
+    except Exception as e:
+        st.error("Error loading data file. Starting with empty dataset.")
+        st.text(traceback.format_exc())
+        return get_empty_df()
+
+def save_data(df: pd.DataFrame):
+    """Save CSV atomically to avoid partial writes."""
+    try:
+        df_to_save = df.copy()
+        # Save Date as ISO date (YYYY-MM-DD) for readability
+        df_to_save["Date"] = pd.to_datetime(df_to_save["Date"], errors="coerce").dt.date
+        dirpath = os.path.dirname(DATA_FILE) or "."
+        with tempfile.NamedTemporaryFile("w", delete=False, dir=dirpath, newline='') as tf:
+            df_to_save.to_csv(tf.name, index=False)
+            tf.flush()
+            try:
+                os.fsync(tf.fileno())
+            except Exception:
+                pass
+        os.replace(tf.name, DATA_FILE)
+    except Exception as e:
+        st.error("Failed to save data.")
+        st.text(traceback.format_exc())
+
+# ------------------------
+# Session state for persistent DataFrame between interactions
+# ------------------------
+if "df" not in st.session_state:
+    st.session_state.df = load_data()
+
+# Keep a local reference for convenience
+df = st.session_state.df
+
+# ------------------------
+# UI - Title
+# ------------------------
+st.title("💰 Personal Expense Tracker")
+st.markdown("Track your expenses and visualize your spending patterns.")
+
+# ------------------------
+# Input form
+# ------------------------
+with st.form("expense_form", clear_on_submit=False):
+    st.subheader("Add New Expense")
+    c1, c2 = st.columns(2)
+    with c1:
+        date_input = st.date_input("Date", value=datetime.today().date(), key="date_input")
+        category = st.selectbox(
+            "Category",
+            options=["Food", "Transport", "Entertainment", "Shopping", "Bills", "Healthcare", "Other"],
+            index=0,
+            key="category_input"
+        )
+    with c2:
+        description = st.text_input("Description", key="description_input")
+        amount = st.number_input("Amount ($)", min_value=0.0, format="%.2f", step=0.5, key="amount_input")
+
+    submitted = st.form_submit_button("Add Expense")
+    if submitted:
+        # validation
+        if amount <= 0:
+            st.error("Amount must be greater than 0.")
+        elif not description or not description.strip():
+            st.error("Please enter a description.")
+        else:
+            try:
+                new_row = {
+                    "Date": pd.to_datetime(date_input),
+                    "Description": description.strip(),
+                    "Amount": float(amount),
+                    "Category": category or "Other",
+                }
+                # Append to session-state DataFrame
+                st.session_state.df = pd.concat(
+                    [st.session_state.df, pd.DataFrame([new_row])],
+                    ignore_index=True
+                )
+                # Persist to disk
+                save_data(st.session_state.df)
+                st.success("Expense added successfully!")
+                # Refresh local reference
+                df = st.session_state.df
+                # Clear form inputs (workaround)
+                st.experimental_rerun()
+            except Exception as e:
+                st.error("Failed to add expense.")
+                st.text(traceback.format_exc())
+
+# ------------------------
+# Display data & visualizations
+# ------------------------
+df = st.session_state.df  # refresh reference after any changes
+
+if df is None or df.empty:
+    st.info("No expenses recorded yet. Add your first expense above!")
+else:
+    st.subheader("Expense History")
+    # Defensive: ensure Amount is numeric
+    df["Amount"] = pd.to_numeric(df["Amount"], errors="coerce").fillna(0.0)
+
+    # Summary stats (handle possible empty cases)
+    total_expenses = float(df["Amount"].sum())
+    avg_expense = float(df["Amount"].mean()) if len(df) > 0 else 0.0
+
+    # Largest expense (defensive)
+    largest_amount_display = "$0.00"
+    largest_caption = ""
+    try:
+        if df["Amount"].notna().any() and len(df) > 0:
+            idx = df["Amount"].idxmax()
+            row = df.loc[idx]
+            largest_amount_display = f"${float(row['Amount']):,.2f}"
+            largest_caption = str(row.get("Description", ""))
+    except Exception:
+        pass
+
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Total Expenses", f"${total_expenses:,.2f}")
+    col2.metric("Average Expense", f"${avg_expense:,.2f}")
+    col3.metric("Largest Expense", largest_amount_display, largest_caption)
+
+    # Table (most recent first)
+    try:
+        display_df = df.sort_values("Date", ascending=False, na_position="last").reset_index(drop=True)
+        st.dataframe(display_df, hide_index=True, use_container_width=True)
+    except Exception:
+        st.dataframe(df, hide_index=True, use_container_width=True)
+
+    # Visualizations
+    st.subheader("Spending Analysis")
+    tab1, tab2, tab3 = st.tabs(["By Category", "Over Time", "Detailed Analysis"])
+
+    with tab1:
+        try:
+            category_totals = df.groupby("Category", sort=False)["Amount"].sum().reset_index()
+            if category_totals.empty:
+                st.info("No category data to plot yet.")
+            else:
+                fig = px.pie(category_totals, values="Amount", names="Category", title="Expenses by Category")
+                st.plotly_chart(fig, use_container_width=True)
+        except Exception:
+            st.error("Couldn't generate category chart.")
+            st.text(traceback.format_exc())
+
+    with tab2:
+        try:
+            # Group by date (daily). Remove rows without a valid date first.
+            df_time = df.dropna(subset=["Date"]).copy()
+            if df_time.empty:
+                st.info("No dated expenses to show over time.")
+            else:
+                df_time = df_time.groupby(pd.Grouper(key="Date", freq="D"))["Amount"].sum().reset_index()
+                fig = px.line(df_time, x="Date", y="Amount", title="Spending Over Time")
+                st.plotly_chart(fig, use_container_width=True)
+        except Exception:
+            st.error("Couldn't generate time series.")
+            st.text(traceback.format_exc())
+
+    with tab3:
+        try:
+            category_totals = df.groupby("Category", sort=False)["Amount"].sum().reset_index()
+            if category_totals.empty:
+                st.info("No data for detailed analysis.")
+            else:
+                fig = px.bar(category_totals, x="Category", y="Amount", title="Total Spending by Category")
+                st.plotly_chart(fig, use_container_width=True)
+        except Exception:
+            st.error("Couldn't generate detailed analysis chart.")
+            st.text(traceback.format_exc())
+
+    # Download CSV
+    try:
+        csv = df.copy()
+        csv["Date"] = pd.to_datetime(csv["Date"], errors="coerce").dt.date
+        st.download_button(
+            label="Download Expenses as CSV",
+            data=csv.to_csv(index=False),
+            file_name="expenses.csv",
+            mime="text/csv",
+        )
+    except Exception:
+        st.error("Failed to prepare CSV for download.")
+        st.text(traceback.format_exc())
+
+# ------------------------
+# Footer and optional debug
+# ------------------------
+st.markdown("---")
+st.markdown("Built with Streamlit • Deploy on Hugging Face Spaces")
+
+with st.expander("Debug / Data snapshot (expand if you need)"):
+    try:
+        st.write("Data file path:", DATA_FILE)
+        st.write("Rows in memory:", len(st.session_state.df))
+        st.dataframe(st.session_state.df.head(10))
+    except Exception:
+        st.text("No debug info available.")