Create app.py

app.py

@@ -0,0 +1,74 @@
+import tkinter as tk
+from tkinter import filedialog
+import cv2
+from PIL import Image, ImageTk
+import numpy as np
+from tensorflow.keras.models import load_model
+
+class ShelfClassifierApp:
+    def __init__(self, master):
+        self.master = master
+        self.master.title("Shelf Classifier")
+
+        self.model = load_model('your_model.h5')  # Load your model
+
+        self.canvas = tk.Canvas(master, width=300, height=300)
+        self.canvas.pack()
+
+        self.load_button = tk.Button(master, text="Load Image", command=self.load_image)
+        self.load_button.pack()
+
+        self.classify_button = tk.Button(master, text="Classify", command=self.classify_image)
+        self.classify_button.pack()
+
+        self.result_label = tk.Label(master, text="")
+        self.result_label.pack()
+
+        self.image = None
+
+    def load_image(self):
+        file_path = filedialog.askopenfilename()
+        if file_path:
+            self.image = cv2.imread(file_path)
+            self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+            self.display_image(self.image)
+
+    def display_image(self, image):
+        image = Image.fromarray(image)
+        image = ImageTk.PhotoImage(image)
+        self.canvas.create_image(0, 0, anchor=tk.NW, image=image)
+        self.canvas.image = image
+
+    def classify_image(self):
+        if self.image is not None:
+            # Preprocess the image
+            resized_image = cv2.resize(self.image, (224, 224))
+            resized_image = resized_image.astype('float32') / 255
+            resized_image = np.expand_dims(resized_image, axis=0)
+
+            # Make prediction
+            prediction = self.model.predict(resized_image)
+
+            # Postprocess the prediction
+            class_index = np.argmax(prediction)
+            class_label = "Disorganized or Empty" if class_index == 1 else "Organized"
+
+            # Draw bounding box if shelf is disorganized or empty
+            if class_index == 1:
+                # Draw red rectangle
+                image_with_box = cv2.rectangle(self.image, (0, 0), (self.image.shape[1], self.image.shape[0]), (255, 0, 0), 2)
+                self.display_image(image_with_box)
+            else:
+                self.display_image(self.image)
+
+            self.result_label.config(text=class_label)
+        else:
+            self.result_label.config(text="Please load an image first")
+
+def main():
+    root = tk.Tk()
+    app = ShelfClassifierApp(root)
+    root.mainloop()
+
+if __name__ == "__main__":
+    main()