Create HeartPredictor.py

Author: rogers-cyber

Heart-disease-prediction-app/HeartPredictor.py

@@ -0,0 +1,274 @@
+"""
+HeartPredictor v2.0 - Health Intelligence Tool
+Predicts risk of heart disease from patient data using a logistic regression model
+Supports batch CSV input with smooth UI and real-time results
+"""
+
+import os, sys, threading, csv, numpy as np, pandas as pd
+import tkinter as tk
+from tkinter import filedialog, messagebox, ttk
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+import ttkbootstrap as tb
+from ttkbootstrap.constants import *
+
+# ---------------------- 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 MODEL ----------------------
+class HeartDiseaseModel:
+    def __init__(self):
+        self.model = LogisticRegression(max_iter=1000)
+        self.scaler = StandardScaler()
+        self.features = ["age","sex","cp","trestbps","chol","fbs","restecg",
+                         "thalach","exang","oldpeak","slope","ca","thal"]
+        self._train_dummy_model()
+
+    def _train_dummy_model(self):
+        """
+        Creates a dummy dataset to train a simple logistic regression model.
+        In practice, replace this with a real dataset.
+        """
+        np.random.seed(42)
+        X = np.random.randint(0, 100, size=(500, len(self.features)))
+        y = np.random.randint(0, 2, size=(500,))
+        X_scaled = self.scaler.fit_transform(X)
+        self.model.fit(X_scaled, y)
+
+    def predict_risk(self, patient_data):
+        """
+        patient_data: dict of feature_name -> value
+        Returns: "Low", "Medium", "High"
+        """
+        try:
+            x = np.array([float(patient_data.get(f,0)) for f in self.features]).reshape(1,-1)
+            x_scaled = self.scaler.transform(x)
+            prob = self.model.predict_proba(x_scaled)[0][1]  # probability of heart disease
+            if prob < 0.33:
+                return "Low"
+            elif prob < 0.66:
+                return "Medium"
+            else:
+                return "High"
+        except Exception:
+            return "Low"
+
+# ---------------------- WORKER ----------------------
+class PredictionWorker:
+    def __init__(self, files, model, callbacks):
+        self.files = files
+        self.model = model
+        self._running = True
+        self.callbacks = callbacks
+
+    def stop(self):
+        self._running = False
+
+    def run(self):
+        total_files = len(self.files)
+        results = []
+
+        for i, path in enumerate(self.files):
+            if not self._running:
+                break
+            try:
+                df = pd.read_csv(path)
+                for _, row in df.iterrows():
+                    if not self._running:
+                        break
+                    patient_data = row.to_dict()
+                    name = patient_data.get("name","Unknown")
+                    risk = self.model.predict_risk(patient_data)
+                    results.append((path, name, risk))
+                    if "found" in self.callbacks:
+                        self.callbacks["found"](path, name, risk)
+            except Exception as e:
+                print(f"Failed to process {path}: {e}")
+
+            if total_files > 0 and "progress" in self.callbacks:
+                self.callbacks["progress"](int((i + 1) / total_files * 100))
+
+        if "finished" in self.callbacks:
+            self.callbacks["finished"]()
+
+# ---------------------- MAIN APP ----------------------
+class HeartPredictorApp:
+    APP_NAME = "HeartPredictor"
+    APP_VERSION = "2.0"
+    SUPPORTED_EXT = (".csv",)
+
+    def __init__(self):
+        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.model = HeartDiseaseModel()
+        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} - Health Intelligence",
+                 font=("Segoe UI", 22, "bold")).pack(pady=(0, 4))
+        tb.Label(main, text="Predict heart disease risk from patient CSV files",
+                 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, "Select patient 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="🚀 Start Prediction", 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)
+
+        # Progress
+        self.progress = tb.Progressbar(main, bootstyle="success-striped", maximum=100)
+        self.progress.pack(fill=X, pady=(0,6))
+
+        # Treeview
+        columns = ("selected", "filename", "patient", "risk")
+        self.tree = ttk.Treeview(main, columns=columns, show="headings", selectmode="extended", height=20)
+        self.tree.heading("selected", text="✅")
+        self.tree.heading("filename", text="Filename")
+        self.tree.heading("patient", text="Patient Name")
+        self.tree.heading("risk", text="Predicted Risk")
+        self.tree.column("selected", width=50, anchor=CENTER)
+        self.tree.column("filename", width=300)
+        self.tree.column("patient", width=200)
+        self.tree.column("risk", width=100)
+        self.tree.pack(fill=BOTH, expand=True, pady=(0,6))
+
+        self.stats_label = tb.Label(main, text="TOTAL: 0 | LOW: 0 | MEDIUM: 0 | HIGH: 0")
+        self.stats_label.pack(anchor=E)
+
+        self.root.after(15, self.animate_progress)
+
+    # ---------------------- Browse ----------------------
+    def browse(self):
+        paths = filedialog.askopenfilenames(filetypes=[("CSV Files", "*.csv")])
+        if paths:
+            for path in paths:
+                if path not in self.file_set:
+                    self.file_set.add(path)
+                    self.tree.insert("", END, values=("☑️", path, "-", "-"))
+
+    # ---------------------- 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 prediction.")
+            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,
+            model=self.model,
+            callbacks={
+                "found": self.add_result,
+                "progress": self.set_target,
+                "finished": self.finish
+            }
+        )
+        self.worker_obj.run()
+
+    def add_result(self, file, patient, risk):
+        for i in self.tree.get_children():
+            if self.tree.item(i)['values'][1] == file:
+                self.tree.item(i, values=("☑️", file, patient, risk))
+                colors = {"High":"#dc2626","Medium":"#facc15","Low":"#4ade80"}
+                self.tree.tag_configure(risk, foreground=colors.get(risk))
+                self.tree.item(i, tags=(risk,))
+                self.update_stats()
+                break
+
+    def update_stats(self):
+        counts = {"Low":0,"Medium":0,"High":0,"TOTAL":0}
+        for i in self.tree.get_children():
+            risk = self.tree.item(i)['values'][3]
+            if risk in counts:
+                counts[risk] += 1
+                counts["TOTAL"] += 1
+        self.stats_label.config(text=f"TOTAL: {counts['TOTAL']} | LOW: {counts['Low']} | MEDIUM: {counts['Medium']} | HIGH: {counts['High']}")
+
+    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 selected results to export")
+            return
+        path = filedialog.asksaveasfilename(defaultextension=".csv", filetypes=[("CSV Files","*.csv")])
+        if path:
+            with open(path,"w",encoding="utf-8", newline='') as f:
+                writer = csv.writer(f)
+                writer.writerow(["Filename","Patient Name","Predicted Risk"])
+                for s in selected_files:
+                    writer.writerow([s[1], s[2], s[3]])
+            messagebox.showinfo("Export", "Export completed")
+
+    # ---------------------- Styles ----------------------
+    def _apply_styles(self):
+        style = tb.Style(theme="darkly")  # don't assign to self.root.style
+        style.configure("TProgressbar", troughcolor="#1b1f3a", background="#7c3aed", thickness=14)
+
+    # ---------------------- Run ----------------------
+    def run(self):
+        self.root.mainloop()
+
+
+# ---------------------- RUN ----------------------
+if __name__ == "__main__":
+    app = HeartPredictorApp()
+    app.run()