upload 6 file

app.py

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+import tkinter as tk
+from tkinter import filedialog, messagebox
+from PIL import Image, ImageTk
+import numpy as np
+import tensorflow as tf
+import json
+import os
+import sys
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller."""
+    try:
+        base_path = sys._MEIPASS  
+    except Exception:
+        base_path = os.path.abspath(".")
+    return os.path.join(base_path, relative_path)
+
+try:
+    model = tf.keras.models.load_model(resource_path("dog_breed_classifier.h5"), compile=False)
+except Exception as e:
+    messagebox.showerror("Model Load Error", f"Could not load model:\n{e}")
+    sys.exit(1)
+
+try:
+    with open(resource_path("class_indices.json"), "r") as f:
+        class_indices = json.load(f)
+        class_names = {int(v): k for k, v in class_indices.items()}
+except Exception as e:
+    messagebox.showerror("Class Index Load Error", f"Could not load labels:\n{e}")
+    sys.exit(1)
+
+def predict_image(image_path):
+    try:
+        img = Image.open(image_path).resize((224, 224)).convert("RGB")
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        predictions = model.predict(img_array)[0]
+        top_idx = np.argmax(predictions)
+        breed = class_names[top_idx]
+        confidence = predictions[top_idx] * 100
+        return breed, confidence
+    except Exception as e:
+        messagebox.showerror("Prediction Error", str(e))
+        return "Error", 0
+
+def upload_image():
+    file_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.jpg *.png *.jpeg")])
+    if not file_path:
+        return
+    image = Image.open(file_path)
+    image = image.resize((250, 250))
+    img_tk = ImageTk.PhotoImage(image)
+    img_label.configure(image=img_tk)
+    img_label.image = img_tk
+
+    breed, confidence = predict_image(file_path)
+    result_label.config(text=f"Breed: {breed}\nConfidence: {confidence:.2f}%")
+
+root = tk.Tk()
+root.title("Dog Breed Detector")
+root.geometry("400x500")
+root.configure(bg="white")
+
+title = tk.Label(root, text="Dog Breed Classification", font=("Arial", 18), bg="white")
+title.pack(pady=10)
+
+btn = tk.Button(root, text="Upload Image", command=upload_image, font=("Arial", 12), bg="#4CAF50", fg="white")
+btn.pack(pady=10)
+
+img_label = tk.Label(root, bg="white")
+img_label.pack()
+
+result_label = tk.Label(root, text="", font=("Arial", 14), bg="white", fg="#333")
+result_label.pack(pady=20)
+
+root.mainloop()

class_indices.json

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+{
+    "Akita Inu": 0, 
+    "Alaskan Malamute": 1, 
+    "Australian Shepherd": 2, 
+    "Basset Hound": 3, 
+    "Beagle": 4, 
+    "Bernese Mountain Dog": 5, 
+    "Border Collie": 6, 
+    "Boston Terrier": 7, 
+    "Boxer": 8, 
+    "Bulldog": 9, 
+    "Chihuahua": 10, 
+    "Cocker Spaniel": 11, 
+    "Dachshund": 12, 
+    "Doberman Pinscher": 13, 
+    "French Bulldog": 14, 
+    "German Shepherd": 15, 
+    "Golden Retriever": 16, 
+    "Great Dane": 17, 
+    "Indian pariah": 18, 
+    "Labrador Retriever": 19, 
+    "Maltese": 20, 
+    "Pomeranian": 21, 
+    "Poodle": 22, 
+    "Pug": 23, 
+    "Rottweiler": 24, 
+    "Saint Bernard": 25, 
+    "Shar pei": 26, 
+    "Shih Tzu": 27, 
+    "Siberian Husky": 28, 
+    "Whippet": 29, 
+    "Yorkshire Terrier": 30
+}

dog_breed_classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de81d8b588600e786a8f23bb76aacc30ffbabc3b92e7b1cd84309c3fa8f8cacf
+size 11420032

readme.md

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+# 🐶 Dog Breed Classification Desktop App
+
+This is a standalone desktop application built using **Tkinter** and **TensorFlow**, capable of identifying dog breeds from images. The app uses a trained `.h5` deep learning model and supports creating `.exe` and `.msi` installers via **cx_Freeze** or **PyInstaller**.
+
+---
+
+## 🚀 Features
+
+- ✅ Offline desktop app (no Python installation required)
+- 🖼️ Upload any dog image (JPG, PNG, JPEG)
+- 🔍 Predicts dog breed with confidence score
+- 📂 Simple folder structure and user-friendly GUI
+- 🛠️ Create `.exe` or `.msi` installers with `cx_Freeze`
+- ⚠️ Error handling for missing models or files
+
+---
+
+## 🧠 Model Info
+
+- Trained on **31 dog breeds** including:
+```
+
+Akita Inu, German Shepherd, Labrador Retriever, Poodle, Pug, Golden Retriever,
+Siberian Husky, Bulldog, Saint Bernard, Rottweiler, and more.
+
+```
+- Input Image Size: **224 x 224**
+- Format: `dog_breed_classifier.h5` (Keras model)
+
+---
+
+## 📁 Folder Structure
+
+```
+
+DogBreedApp/
+│
+├── app.py                  # Main GUI application file
+├── dog\_breed\_classifier.h5 # Trained TensorFlow model
+├── class\_indices.json      # Label-to-classname mapping
+├── setup.py                # cx\_Freeze build script
+├── dog.ico                 # (optional) Icon file
+
+````
+
+---
+
+## 🛠 Requirements
+
+Install these Python packages:
+
+```bash
+pip install -r requirements.txt
+````
+
+**requirements.txt**
+
+```txt
+tensorflow
+numpy
+pillow
+cx-Freeze
+```
+
+---
+
+## 🖥️ Running the App
+
+```bash
+python app.py
+```
+
+---
+
+## 📦 Creating Executable (.exe or .msi)
+
+### ✅ Using cx\_Freeze
+
+1. Create `setup.py`
+2. Build:
+
+```bash
+python setup.py build
+```
+
+3. For MSI Installer:
+
+```bash
+python setup.py bdist_msi
+```
+
+This generates:
+
+* `.exe` in `build/`
+* `.msi` installer in `dist/`
+
+---
+
+## 🧊 Packaging Notes
+
+* Use `resource_path()` for model/JSON so PyInstaller or cx\_Freeze can bundle files.
+* Include `.h5` and `.json` in `include_files` in `setup.py`.
+* Works without requiring Python installation.
+
+---
+
+## 📸 Screenshots
+
+![Upload and Detect](screenshots/ui1.png)
+![Prediction Result](screenshots/ui2.png)
+
+---
+
+## 📜 License
+
+This project is open-source and available for educational and personal use.
+
+---
+
+## 👨‍💻 Author
+
+Developed by **Jayasimma D**
+
+```
+
+---
+
+### ✅ Bonus Tips:
+- Save it as `README.md` in your root folder.
+- Add screenshots to a `/screenshots` folder if you plan to upload this to GitHub or Share.
+- Replace `[Your Name]` with your actual name or GitHub username.