Create InsurePredict.py

Author: rogers-cyber

Insurance-cost-prediction-GUI/InsurePredict.py

@@ -0,0 +1,255 @@
+"""
+InsurePredict v1.0 - Enterprise Edition
+Insurance Cost Prediction & Analysis Tool
+AI-driven cost predictions with smooth GUI
+"""
+
+import os, sys, threading
+import tkinter as tk
+from tkinter import filedialog, messagebox, ttk
+
+import ttkbootstrap as tb
+from ttkbootstrap.constants import *
+
+try:
+    from tkinterdnd2 import TkinterDnD, DND_FILES
+    DND_ENABLED = True
+except ImportError:
+    DND_ENABLED = False
+    print("Drag & Drop requires tkinterdnd2: pip install tkinterdnd2")
+
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+
+# ---------------------- 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)
+
+# ---------------------- PREDICTION WORKER ----------------------
+class PredictionWorker:
+    def __init__(self, files, callbacks):
+        self.files = files
+        self.callbacks = callbacks
+        self._running = True
+        # Dummy regression model (for demonstration)
+        self.model = LinearRegression()
+        # Example training on synthetic data
+        self.model.fit([[18,0],[25,1],[40,1],[60,0]], [2000,3000,4000,3500])
+
+    def stop(self):
+        self._running = False
+
+    def run(self):
+        results = []
+        total_files = len(self.files)
+        for i, file in enumerate(self.files):
+            if not self._running:
+                break
+            try:
+                df = pd.read_csv(file)
+                # Expected columns: ["age", "smoker"] for simplicity
+                if "age" in df.columns and "smoker" in df.columns:
+                    pred = self.model.predict(df[["age","smoker"]])
+                    results.append((file, pred.tolist()))
+                else:
+                    results.append((file, "Missing required columns"))
+            except Exception as e:
+                results.append((file, f"Error: {e}"))
+
+            # Update progress
+            if "progress" in self.callbacks:
+                self.callbacks["progress"](int((i+1)/total_files*100))
+            if "found" in self.callbacks:
+                self.callbacks["found"](file, results[-1][1])
+
+        if "finished" in self.callbacks:
+            self.callbacks["finished"]()
+
+# ---------------------- MAIN APP ----------------------
+class InsurePredictApp:
+    APP_NAME = "InsurePredict"
+    APP_VERSION = "1.0"
+    SUPPORTED_EXT = (".csv",)
+
+    def __init__(self):
+        if DND_ENABLED:
+            self.root = TkinterDnD.Tk()
+        else:
+            self.root = tb.Window(themename="darkly")
+
+        self.root.title(f"{self.APP_NAME} v{self.APP_VERSION}")
+        self.root.minsize(1000, 600)
+
+        self.worker_obj = None
+        self.smooth_value = 0
+        self.target_progress = 0
+        self.file_set = set()
+
+        self._build_ui()
+        self._apply_styles()
+
+    # ---------------------- UI ----------------------
+    def _build_ui(self):
+        main = tb.Frame(self.root, padding=10)
+        main.pack(fill=BOTH, expand=True)
+
+        tb.Label(main, text=f"💰 {self.APP_NAME} - Insurance Cost Predictor",
+                 font=("Segoe UI", 22, "bold")).pack(pady=(0,4))
+
+        tb.Label(main, text="Predict insurance cost from CSV data",
+                 font=("Segoe UI", 10, "italic"), foreground="#9ca3af").pack(pady=(0,20))
+
+        # Row 1: File Selection
+        row1 = tb.Frame(main)
+        row1.pack(fill=X, pady=(0,6))
+
+        self.path_input = tb.Entry(row1, width=80)
+        self.path_input.pack(side=LEFT, fill=X, expand=True, padx=(0,6))
+        self.path_input.insert(0, "Drag & drop CSV files here…")
+
+        browse_btn = tb.Button(row1, text="📂 Browse", bootstyle=INFO, command=self.browse)
+        browse_btn.pack(side=LEFT, padx=3)
+
+        self.start_btn = tb.Button(row1, text="🚀 Predict", bootstyle=SUCCESS, command=self.start)
+        self.start_btn.pack(side=LEFT, padx=3)
+
+        self.cancel_btn = tb.Button(row1, text="⏹ Cancel", bootstyle=DANGER, command=self.cancel)
+        self.cancel_btn.pack(side=LEFT, padx=3)
+        self.cancel_btn.config(state=DISABLED)
+
+        export_btn = tb.Button(row1, text="💾 Export Results", bootstyle=PRIMARY, command=self.export_results)
+        export_btn.pack(side=LEFT, padx=3)
+
+        about_btn = tb.Button(row1, text="ℹ️ About", bootstyle=INFO, command=self.show_about)
+        about_btn.pack(side=LEFT, padx=3)
+
+        # Progress
+        self.progress = tb.Progressbar(main, bootstyle="success-striped", maximum=100)
+        self.progress.pack(fill=X, pady=(0,6))
+
+        # Treeview
+        columns = ("selected","filename","prediction")
+        self.tree = ttk.Treeview(main, columns=columns, show="headings", selectmode="extended", height=20)
+        self.tree.heading("selected", text="✅")
+        self.tree.heading("filename", text="Filename", anchor=W)
+        self.tree.heading("prediction", text="Prediction", anchor=W)
+        self.tree.column("selected", width=50, anchor=CENTER)
+        self.tree.column("filename", width=600)
+        self.tree.column("prediction", width=300)
+        self.tree.pack(fill=BOTH, expand=True, pady=(0,6))
+
+        # Timer
+        self.root.after(15, self.animate_progress)
+
+        # Bind DnD if enabled
+        if DND_ENABLED:
+            self.tree.drop_target_register(DND_FILES)
+            self.tree.dnd_bind("<<Drop>>", self.on_drop)
+
+    # ---------------------- File Handling ----------------------
+    def browse(self):
+        files = filedialog.askopenfilenames(title="Select CSV Files", filetypes=[("CSV Files","*.csv")])
+        if files:
+            self._insert_files_chunked(files)
+
+    def on_drop(self, event):
+        dropped_paths = self.root.tk.splitlist(event.data)
+        self._insert_files_chunked(dropped_paths)
+
+    def _insert_files_chunked(self, paths, chunk_size=500):
+        if not paths:
+            return
+        chunk = paths[:chunk_size]
+        remaining = paths[chunk_size:]
+        for path in chunk:
+            ext = os.path.splitext(path)[1].lower()
+            if ext == ".csv" and path not in self.file_set:
+                self.file_set.add(path)
+                self.tree.insert("", END, values=("☑️", path, "Queued"))
+        self.root.after(1, lambda: self._insert_files_chunked(remaining, chunk_size))
+
+    # ---------------------- Actions ----------------------
+    def start(self):
+        selected_files = [self.tree.item(i)['values'][1] for i in self.tree.get_children() if self.tree.item(i)['values'][0]=="☑️"]
+        if not selected_files:
+            messagebox.showwarning("No Selection", "Select CSV files before predicting.")
+            return
+        self.progress["value"] = 0
+        self.smooth_value = 0
+        self.target_progress = 0
+        self.start_btn.config(state=DISABLED)
+        self.cancel_btn.config(state=NORMAL)
+        threading.Thread(target=self._run_worker, args=(selected_files,), daemon=True).start()
+
+    def _run_worker(self, files):
+        self.worker_obj = PredictionWorker(files, callbacks={
+            "found": self.add_prediction,
+            "progress": self.set_target,
+            "finished": self.finish
+        })
+        self.worker_obj.run()
+
+    def add_prediction(self, file, prediction):
+        for i in self.tree.get_children():
+            if self.tree.item(i)['values'][1] == file:
+                self.tree.item(i, values=("☑️", file, str(prediction)))
+                break
+
+    def set_target(self, v):
+        self.target_progress = v
+
+    def animate_progress(self):
+        if self.smooth_value < self.target_progress:
+            self.smooth_value += 1
+            self.progress["value"] = self.smooth_value
+        self.root.after(15, self.animate_progress)
+
+    def cancel(self):
+        if self.worker_obj:
+            self.worker_obj.stop()
+        self.finish()
+
+    def finish(self):
+        self.start_btn.config(state=NORMAL)
+        self.cancel_btn.config(state=DISABLED)
+        self.progress["value"] = 100
+
+    # ---------------------- Export ----------------------
+    def export_results(self):
+        selected_files = [self.tree.item(i)['values'] for i in self.tree.get_children() if self.tree.item(i)['values'][0]=="☑️"]
+        if not selected_files:
+            messagebox.showwarning("Export", "No results to export")
+            return
+        path = filedialog.asksaveasfilename(defaultextension=".txt", filetypes=[("Text Files","*.txt")])
+        if path:
+            with open(path,"w",encoding="utf-8") as f:
+                for s in selected_files:
+                    f.write(f"{s[1]} | {s[2]}\n")
+            messagebox.showinfo("Export", "Export completed")
+
+    # ---------------------- About ----------------------
+    def show_about(self):
+        messagebox.showinfo(f"About {self.APP_NAME}",
+            f"{self.APP_NAME} v{self.APP_VERSION}\n\n"
+            "• Predict insurance cost from CSV files\n"
+            "• Drag & drop support\n"
+            "• Multi-file batch predictions\n"
+            "• Export results to text\n\n"
+            "🏢 Built by Mate Technologies\n"
+            "🌐 https://matetools.gumroad.com"
+        )
+
+    # ---------------------- Styles ----------------------
+    def _apply_styles(self):
+        self.root.style = tb.Style(theme="darkly")
+        self.root.style.configure("TProgressbar", troughcolor="#1b1f3a", background="#7c3aed", thickness=14)
+
+    # ---------------------- Run ----------------------
+    def run(self):
+        self.root.mainloop()
+
+# ---------------------- RUN ----------------------
+if __name__ == "__main__":
+    app = InsurePredictApp()
+    app.run()