Update app.py

app.py

@@ -15,41 +15,38 @@ if uploaded_file is not None:
     # Display the uploaded image
     st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
     st.write("")
-
-    # Data preprocessing
-    train_datagen = ImageDataGenerator(
-        rescale=1./255,
-        shear_range=0.2,
-        zoom_range=0.2,
-        horizontal_flip=True
-    )
-
-    # No need to load the model architecture, just load the weights
-    cnn = tf.keras.models.Sequential([
-        tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]),
-        tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
-        tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'),
-        tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
-        tf.keras.layers.Flatten(),
-        tf.keras.layers.Dense(units=128, activation='relu'),
-        tf.keras.layers.Dense(units=1, activation='sigmoid')
-    ])
-
-    # Compile the model
-    cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
-
-    # Load the trained weights
-    cnn.load_weights('cat_dog_classifier_weights.h5')
-
-    # Make prediction
-    test_image = image.load_img(uploaded_file, target_size=(64, 64))
-    test_image = image.img_to_array(test_image)
-    test_image = np.expand_dims(test_image, axis=0)
-    result = cnn.predict(test_image)
-
-    # Class indices
-    class_indices = {0: 'cat', 1: 'dog'}
-
-    # Predict
-    prediction = 'dog' if result[0][0] == 1 else 'cat'
-    st.write("Prediction:", prediction)
+train_datagen = ImageDataGenerator(rescale = 1./255,
+                                   shear_range = 0.2,
+                                   zoom_range = 0.2,
+                                   horizontal_flip = True)
+training_set = train_datagen.flow_from_directory('dataset/training_set',
+                                                 target_size = (64, 64),
+                                                 batch_size = 32,
+                                                 class_mode = 'binary')
+test_datagen = ImageDataGenerator(rescale = 1./255)
+test_set = test_datagen.flow_from_directory('dataset/test_set',
+                                            target_size = (64, 64),
+                                            batch_size = 32,
+                                            class_mode = 'binary')
+cnn=tf.keras.models.Sequential()
+cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu',input_shape=[64,64,3]))
+cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
+cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))
+cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
+cnn.add(tf.keras.layers.Flatten())
+cnn.add(tf.keras.layers.Dense(units=128,activation='relu'))
+cnn.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))
+cnn.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
+cnn.fit(x=training_set,validation_data=test_set,epochs=25)
+import numpy as np
+from keras.preprocessing import image
+test_image=image.load_img('dataset/single_prediction/cat_or_dog_1.jpg',target_size=(64,64))
+test_image=image.img_to_array(test_image)
+test_image=np.expand_dims(test_image,axis=0)
+result=cnn.predict(test_image)
+training_set.class_indices
+if result[0][0]==1:
+  prediction='dog'
+else:
+  prediction='cat'
+print(prediction)