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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cats_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/*
+kia-fs23/*
+.DS_Store

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        
+        {
+            "name": "Python: Flask",
+            "type": "python",
+            "request": "launch",
+            "module": "flask",
+            "env": {
+                "FLASK_APP": "app.py",
+                "FLASK_DEBUG": "1"
+            },
+            "args": [
+                "run",
+                //"--no-debugger",
+                //"--no-reload"
+            ],
+            "jinja": true,
+            "justMyCode": true
+        }
+    ]
+}

README.md

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+# KI-Anwendungen-n-to-n-backend
+
+Flask install: https://flask.palletsprojects.com/en/2.2.x/installation/
+Flask quickstart: https://flask.palletsprojects.com/en/2.2.x/quickstart/
+Check that the correct envierment is active.
+
+Pythonanywhere (to deploy the backend): https://www.pythonanywhere.com/

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from tensorflow import keras
+from keras import layers
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import base64
+import os
+
+app = Flask(__name__)
+CORS(app)
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1000 * 1000
+
+cats_and_dogs_model = None
+
+# Load and train the cats and dogs model
+def load_cats_and_dogs_model():
+    # Define the model architecture
+    model = keras.Sequential([
+        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 3)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.Flatten(),
+        layers.Dense(64, activation='relu'),
+        layers.Dense(1, activation='sigmoid')
+    ])
+
+    # Compile the model
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+
+    # Load or generate your training data (X_train and y_train)
+    dataset_directory = 'C:\\Users\\chant\\OneDrive - ZHAW\\ZHAW\\Semester 6\\KI Anwendungen\\Tutorial\\Project2\\dataset'
+    train_directory = os.path.join(dataset_directory, 'train')
+    test_directory = os.path.join(dataset_directory, 'test')
+    train_cats_directory = os.path.join(train_directory, 'cats')
+    train_dogs_directory = os.path.join(train_directory, 'dogs')
+    test_cats_directory = os.path.join(test_directory, 'cats')
+    test_dogs_directory = os.path.join(test_directory, 'dogs')
+
+    train_images = []
+    train_labels = []
+    test_images = []
+    test_labels = []
+
+    for filename in os.listdir(train_cats_directory):
+        if filename.endswith('.jpg'):
+            img = Image.open(os.path.join(train_cats_directory, filename))
+            img = img.resize((28, 28))
+            img = img.convert('RGB')
+            img = np.array(img)
+            train_images.append(img)
+            train_labels.append(0)  # Assign label 0 for cats
+
+    for filename in os.listdir(train_dogs_directory):
+        if filename.endswith('.jpg'):
+            img = Image.open(os.path.join(train_dogs_directory, filename))
+            img = img.resize((28, 28))
+            img = img.convert('RGB')
+            img = np.array(img)
+            train_images.append(img)
+            train_labels.append(1)  # Assign label 1 for dogs
+
+    for filename in os.listdir(test_cats_directory):
+        if filename.endswith('.jpg'):
+            img = Image.open(os.path.join(test_cats_directory, filename))
+            img = img.resize((28, 28))
+            img = img.convert('RGB')
+            img = np.array(img)
+            test_images.append(img)
+            test_labels.append(0)  # Assign label 0 for cats
+
+    for filename in os.listdir(test_dogs_directory):
+        if filename.endswith('.jpg'):
+            img = Image.open(os.path.join(test_dogs_directory, filename))
+            img = img.resize((28, 28))
+            img = img.convert('RGB')
+            img = np.array(img)
+            test_images.append(img)
+            test_labels.append(1)  # Assign label 1 for dogs
+
+    X_train = np.array(train_images)
+    y_train = np.array(train_labels)
+    X_test = np.array(test_images)
+    y_test = np.array(test_labels)
+
+    # Preprocess the data
+    X_train = X_train.astype('float32') / 255
+    X_test = X_test.astype('float32') / 255
+
+    # Train the model
+    model.fit(X_train, y_train, epochs=10, batch_size=32)
+
+    # Evaluate the model
+    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+    print('Test accuracy:', test_acc)
+
+    # Set the trained model as the global variable
+    global cats_and_dogs_model
+    cats_and_dogs_model = model
+
+# Define the route for image classification
+@app.route('/api/prediction/classify', methods=['POST'])
+def classify_image():
+    data = request.get_json()
+    image_data = data['image']
+    image = Image.open(BytesIO(base64.b64decode(image_data)))
+    image = image.resize((28, 28))
+    image = image.convert('RGB')
+    image = np.array(image)
+    image = image.astype('float32') / 255
+    image = np.expand_dims(image, axis=0)
+    result = cats_and_dogs_model.predict(image)[0][0]
+    class_name = 'cat' if result < 0.5 else 'dog'
+    response = {'class_name': class_name, 'confidence': float(result)}
+    return jsonify(response)
+
+# Add this if statement to start the Flask app
+if __name__ == "__main__" or __name__ == "app" or __name__ == "flask_app":
+    print(("* Loading models and starting the server..."
+           "please wait until the server has fully started"))
+    load_cats_and_dogs_model()
+    app.run()

cats_model/fingerprint.pb

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+size 56

cats_model/keras_metadata.pb

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cats_model/saved_model.pb

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cats_model/variables/variables.data-00000-of-00001

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knn_clf.pkl

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randomforest_regression.pkl

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requirements.txt

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+black==23.1.0
+click==8.1.3
+colorama==0.4.6
+Flask==2.2.3
+Flask-Cors==3.0.10
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.2.0
+MarkupSafe==2.1.2
+mypy-extensions==1.0.0
+numpy==1.24.2
+packaging==23.0
+pandas==1.5.3
+pathspec==0.11.0
+platformdirs==3.1.0
+Pygments==2.14.0
+pypandoc==1.11
+python-dateutil==2.8.2
+pytz==2022.7.1
+scikit-learn==1.2.1
+scipy==1.10.1
+six==1.16.0
+superprompt==1.2.0
+threadpoolctl==3.1.0
+tomli==2.0.1
+Werkzeug==2.2.3