Create app.py

app.py

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+!pip install keras
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import PIL
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
+import pathlib
+dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
+data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
+data_dir = pathlib.Path(data_dir)
+
+image_count = len(list(data_dir.glob('*/*.jpg')))
+print(image_count)
+
+print(os.listdir(data_dir))
+
+roses = list(data_dir.glob('roses/*'))
+PIL.Image.open(str(roses[1]))
+
+daisy = list(data_dir.glob('daisy/*'))
+PIL.Image.open(str(daisy[2]))
+
+batch_size = 32
+img_height = 180
+img_width = 180
+
+train_ds = tf.keras.utils.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="training",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+
+val_ds = tf.keras.utils.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="validation",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+
+class_names = train_ds.class_names
+print(class_names)
+
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(12, 12))
+for images, labels in train_ds.take(1):
+  for i in range(12):
+    ax = plt.subplot(3, 4, i + 1)
+    plt.imshow(images[i].numpy().astype("uint8"))
+    plt.title(class_names[labels[i]])
+    plt.axis("off")
+
+ 
+num_classes = len(class_names)
+
+model = Sequential([
+  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+  layers.Conv2D(16, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(32, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(64, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes,activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+model.summary()
+
+epochs=15
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
+)
+
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs_range = range(epochs)
+
+plt.figure(figsize=(12, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+
+def resize_image(input_image):
+    img = PIL.Image.fromarray(input_image)
+    resized_img = img.resize((180, 180))
+    resized_array = np.array(resized_img)
+    return resized_array
+
+def predict_input_image(img):
+  img=resize_image(img)
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(5)}
+
+!pip install gradio
+
+import gradio as gr
+
+gr.Interface(fn=predict_input_image,
+             inputs=gr.Image(),
+             outputs=gr.Label(num_top_classes=5),
+             live=False).launch(debug='True')