Create HousePrice_Sentinel.py

Author: rogers-cyber

HousePrice-Sentinel/HousePrice_Sentinel.py

@@ -0,0 +1,242 @@
+"""
+HousePrice Sentinel v1.0 - Enterprise Edition
+Smart Real Estate Price Prediction Tool
+ML-powered house valuation with modern UI
+"""
+
+import os, sys, threading
+import tkinter as tk
+from tkinter import filedialog, messagebox
+from tkinter import ttk
+
+import pandas as pd
+import numpy as np
+
+import ttkbootstrap as tb
+from ttkbootstrap.constants import *
+
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+
+# ---------------------- UTIL ----------------------
+def resource_path(file_name):
+    base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__)))
+    return os.path.join(base_path, file_name)
+
+
+# ---------------------- ML WORKER ----------------------
+class PriceModelWorker:
+    def __init__(self, csv_path, callbacks):
+        self.csv_path = csv_path
+        self.callbacks = callbacks
+        self.model = LinearRegression()
+
+    def run(self):
+        try:
+            df = pd.read_csv(self.csv_path)
+
+            X = df[["Area", "Bedrooms", "Bathrooms"]]
+            y = df["Price"]
+
+            X_train, X_test, y_train, y_test = train_test_split(
+                X, y, test_size=0.2, random_state=42
+            )
+
+            self.model.fit(X_train, y_train)
+            score = self.model.score(X_test, y_test)
+
+            if "trained" in self.callbacks:
+                self.callbacks["trained"](score, self.model)
+
+        except Exception as e:
+            if "error" in self.callbacks:
+                self.callbacks["error"](str(e))
+
+
+# ---------------------- MAIN APP ----------------------
+class HousePriceApp:
+    APP_NAME = "HousePrice Sentinel"
+    APP_VERSION = "1.0"
+
+    def __init__(self):
+        self.root = tb.Window(themename="darkly")
+        self.root.title(f"{self.APP_NAME} v{self.APP_VERSION}")
+        self.root.minsize(1100, 650)
+
+        try:
+            self.root.iconbitmap(resource_path("logo.ico"))
+        except:
+            pass
+
+        self.model = None
+        self._build_ui()
+
+    # ---------------------- UI ----------------------
+    def _build_ui(self):
+        main = tb.Frame(self.root, padding=10)
+        main.pack(fill=BOTH, expand=True)
+
+        tb.Label(
+            main,
+            text="🏠 HousePrice Sentinel",
+            font=("Segoe UI", 22, "bold")
+        ).pack(pady=(0, 4))
+
+        tb.Label(
+            main,
+            text="AI-Powered Real Estate Price Prediction",
+            font=("Segoe UI", 10, "italic"),
+            foreground="#9ca3af"
+        ).pack(pady=(0, 20))
+
+        # Dataset row
+        row1 = tb.Frame(main)
+        row1.pack(fill=X, pady=6)
+
+        self.dataset_entry = tb.Entry(row1, width=90)
+        self.dataset_entry.pack(side=LEFT, fill=X, expand=True, padx=(0, 6))
+        self.dataset_entry.insert(0, "Load CSV dataset (Area, Bedrooms, Bathrooms, Price)")
+
+        tb.Button(
+            row1,
+            text="📂 Load Dataset",
+            bootstyle=INFO,
+            command=self.load_dataset
+        ).pack(side=LEFT, padx=3)
+
+        tb.Button(
+            row1,
+            text="🧠 Train Model",
+            bootstyle=SUCCESS,
+            command=self.train_model
+        ).pack(side=LEFT, padx=3)
+
+        # Stats
+        self.stats_label = tb.Label(
+            main,
+            text="Model Status: Not trained",
+            font=("Segoe UI", 10)
+        )
+        self.stats_label.pack(anchor=W, pady=(10, 10))
+
+        # Prediction inputs
+        form = tb.Labelframe(main, text="Price Prediction", padding=15)
+        form.pack(fill=X, pady=10)
+
+        self.area_var = tk.DoubleVar()
+        self.bed_var = tk.IntVar()
+        self.bath_var = tk.IntVar()
+
+        tb.Label(form, text="Area (sqft)").grid(row=0, column=0, padx=5, pady=5)
+        tb.Entry(form, textvariable=self.area_var).grid(row=0, column=1, padx=5)
+
+        tb.Label(form, text="Bedrooms").grid(row=0, column=2, padx=5)
+        tb.Entry(form, textvariable=self.bed_var).grid(row=0, column=3, padx=5)
+
+        tb.Label(form, text="Bathrooms").grid(row=0, column=4, padx=5)
+        tb.Entry(form, textvariable=self.bath_var).grid(row=0, column=5, padx=5)
+
+        tb.Button(
+            form,
+            text="💰 Predict Price",
+            bootstyle=PRIMARY,
+            command=self.predict_price
+        ).grid(row=0, column=6, padx=10)
+
+        tb.Button(
+            form,
+            text="ℹ️ About",
+            bootstyle=INFO,
+            command=self.show_about
+        ).grid(row=0, column=7, padx=10)
+        
+        self.result_label = tb.Label(
+            main,
+            text="Predicted Price: —",
+            font=("Segoe UI", 16, "bold"),
+            foreground="#4ade80"
+        )
+        self.result_label.pack(pady=20)
+
+    # ---------------------- Actions ----------------------
+    def load_dataset(self):
+        path = filedialog.askopenfilename(
+            filetypes=[("CSV Files", "*.csv")]
+        )
+        if path:
+            self.dataset_entry.delete(0, END)
+            self.dataset_entry.insert(0, path)
+
+    def train_model(self):
+        path = self.dataset_entry.get()
+        if not os.path.isfile(path):
+            messagebox.showerror("Error", "Invalid dataset path")
+            return
+
+        self.stats_label.config(text="Training model...")
+
+        threading.Thread(
+            target=self._train_worker,
+            args=(path,),
+            daemon=True
+        ).start()
+
+    def _train_worker(self, path):
+        worker = PriceModelWorker(
+            path,
+            callbacks={
+                "trained": self.on_trained,
+                "error": self.on_error
+            }
+        )
+        worker.run()
+
+    def on_trained(self, score, model):
+        self.model = model
+        self.stats_label.config(
+            text=f"Model trained successfully | Accuracy: {score:.2f}"
+        )
+
+    def on_error(self, msg):
+        messagebox.showerror("Training Error", msg)
+        self.stats_label.config(text="Training failed")
+
+    def predict_price(self):
+        if not self.model:
+            messagebox.showwarning("Model", "Train the model first")
+            return
+
+        X = pd.DataFrame([{
+            "Area": self.area_var.get(),
+            "Bedrooms": self.bed_var.get(),
+            "Bathrooms": self.bath_var.get()
+        }])
+
+        price = self.model.predict(X)[0]
+
+        self.result_label.config(
+            text=f"Predicted Price: ${price:,.2f}"
+        )
+
+    # ---------------------- About ----------------------
+    def show_about(self):
+        messagebox.showinfo(
+            f"About {self.APP_NAME}",
+            f"{self.APP_NAME} v{self.APP_VERSION}\n\n"
+            "• CSV-based ML training\n"
+            "• Real-time house price prediction\n"
+            "• Clean enterprise UI\n"
+            "• Built with Python & Scikit-Learn\n\n"
+            "🏢 Mate Technologies"
+        )
+
+    # ---------------------- Run ----------------------
+    def run(self):
+        self.root.mainloop()
+
+
+# ---------------------- RUN ----------------------
+if __name__ == "__main__":
+    app = HousePriceApp()
+    app.run()