Update app.py

app.py

@@ -1,15 +1,12 @@
 import numpy as np
 import pandas as pd
-from datetime import datetime
 import gradio as gr
 
-# --------------------------
-# Helpers
-# --------------------------
+# -------- helpers --------
 def month_start(ts):
     """Return the first day of the month as Timestamp."""
     ts = pd.to_datetime(ts)
-    return ts.to_period("M").to_timestamp()  # safe, gives YYYY-MM-01
+    return ts.to_period("M").to_timestamp()  # YYYY-MM-01
 
 def prep_monthly_features(df: pd.DataFrame) -> pd.DataFrame:
     """
@@ -17,15 +14,13 @@ def prep_monthly_features(df: pd.DataFrame) -> pd.DataFrame:
     amount: +inflows, -spend
     """
     df = df.copy()
-
-    # Parse dates safely
     df["date"] = pd.to_datetime(df["date"], errors="coerce")
     df = df.dropna(subset=["date"])
 
-    # Month bucket (always the 1st of month)
+    # month bucket
     df["month"] = df["date"].apply(month_start)
 
-    # Monthly aggregates
+    # monthly aggregates
     month_agg = (
         df.groupby("month", as_index=False)
           .agg(
@@ -36,7 +31,7 @@ def prep_monthly_features(df: pd.DataFrame) -> pd.DataFrame:
           )
     )
 
-    # Category counts per month (diversity proxy)
+    # category counts per month (diversity proxy)
     cats = (
         df.assign(cnt=1)
           .pivot_table(index="month", columns="category", values="cnt",
@@ -46,7 +41,7 @@ def prep_monthly_features(df: pd.DataFrame) -> pd.DataFrame:
 
     out = month_agg.merge(cats, left_on="month", right_index=True, how="left").fillna(0)
 
-    # Sort and create target/lag features
+    # target & lags
     out = out.sort_values("month").reset_index(drop=True)
     out["target_next_spend"] = out["spend"].shift(-1)
 
@@ -81,24 +76,21 @@ def train_model(monthly_df: pd.DataFrame):
     return model, feature_cols, mae
 
 def predict_next(monthly_df: pd.DataFrame, model, feature_cols):
-    # Predict next month using last row features
     last = monthly_df.iloc[[-1]][feature_cols]
     pred = float(model.predict(last)[0])
 
-    # Overspend risk (more negative spend = higher spend)
+    # overspend risk (more negative spend = higher spend)
     p10 = float(np.percentile(monthly_df["spend"], 10))
     risk = "High" if pred <= p10 else "Low"
 
-    # Format next month label
+    # next month label
     last_month = monthly_df["month"].iloc[-1]
     next_month = month_start(last_month + pd.offsets.MonthBegin(1)).strftime("%Y-%m")
     return next_month, pred, risk
 
-# --------------------------
-# App logic
-# --------------------------
+# -------- app logic --------
 def load_or_demo(file, budget):
-    # Demo data if no file
+    # demo data if no file
     if file is None:
         rng = pd.date_range("2024-01-01", periods=365, freq="D")
         cats = ["groceries", "rent", "utilities", "fun", "transport"]
@@ -108,7 +100,6 @@ def load_or_demo(file, budget):
         for d in rng:
             if d.day == 1:
                 rows.append({"date": d, "amount": income, "category": "income", "income": income})
-            # Poisson number of daily spend txns
             for _ in range(rng_seed.poisson(2)):
                 amt = -float(rng_seed.choice([15, 25, 40, 60, 120, 300], p=[.25, .25, .2, .15, .1, .05]))
                 rows.append({"date": d, "amount": amt, "category": rng_seed.choice(cats), "income": income})
@@ -127,7 +118,7 @@ def load_or_demo(file, budget):
     model, feats, mae = train_model(m)
     next_m, spend_pred, risk = predict_next(m, model, feats)
 
-    # Budget evaluation
+    # budget evaluation
     try:
         budget_val = float(budget) if budget not in (None, "") else 0.0
     except Exception:
@@ -156,26 +147,20 @@ def safe_run(file, budget):
         print("TRACEBACK:\n", traceback.format_exc())
         raise gr.Error(str(e))
 
-# --------------------------
-# Gradio UI
-# --------------------------
+# -------- UI --------
 with gr.Blocks(title="Retail Finance: Spend Forecast") as demo:
     gr.Markdown(
         "## Retail Finance Spend Forecaster\n"
         "Upload your transactions CSV (columns: `date, amount, category, income`) or use demo data. "
         "The model forecasts **next-month spend** and flags **overspend risk**."
     )
-    with gr.Row():
-    file = gr.File(label="Upload CSV (optional)")
-    budget = gr.Number(value=2500, label="Monthly budget (positive number)")
-    income_override = gr.Number(value=None, label="Expected income next month (optional)")
     with gr.Row():
         file = gr.File(label="Upload CSV (optional)")
         budget = gr.Number(value=2500, label="Monthly budget (positive number)")
     btn = gr.Button("Run Forecast")
     summary = gr.Dataframe(label="Summary")
     monthly_table = gr.Dataframe(label="Monthly aggregates used by the model")
-   btn.click(safe_run, inputs=[file, budget, income_override], outputs=[summary, monthly_table])
+    btn.click(safe_run, inputs=[file, budget], outputs=[summary, monthly_table])
 
 if __name__ == "__main__":
     demo.launch()