Create app.py

app.py

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+import gradio as gr
+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)
+
+roses = list(data_dir.glob('roses/*'))
+print(roses[0])
+PIL.Image.open(str(roses[0]))
+
+img_height,img_width=180,180
+batch_size=32
+train_ds = tf.keras.preprocessing.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.preprocessing.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=(10, 10))
+for images, labels in train_ds.take(1):
+  for i in range(9):
+    ax = plt.subplot(3, 3, i + 1)
+    plt.imshow(images[i].numpy().astype("uint8"))
+    plt.title(class_names[labels[i]])
+    plt.axis("off")
+
+    num_classes = 5
+
+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'])
+
+epochs=1
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs,
+  verbose=1
+)
+
+def predict_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)}
+
+    image = gr.Image(height=180,width=180)
+#label = gr.outputs.Label(num_top_classes=5)
+label = gr.Label(num_top_classes=5)
+#gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')
+
+gr.Interface(predict_image, inputs=image, outputs=label )
+
+iface.launch(debug=True)