Create app.py

app.py

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+import numpy as np
+import gradio as gr
+import os
+import PIL
+import PIL.Image
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+import pathlib
+dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
+data_dir = tf.keras.utils.get_file(origin=dataset_url,
+                                   fname='flower_photos',
+                                   untar=True)
+data_dir = pathlib.Path(data_dir)
+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)
+
+normalization_layer = tf.keras.layers.Rescaling(1./255)
+normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+first_image = image_batch[0]
+# Notice the pixel values are now in `[0,1]`.
+#print(np.min(first_image), np.max(first_image))
+
+AUTOTUNE = tf.data.AUTOTUNE
+
+train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
+num_classes = 5
+
+model = tf.keras.Sequential([
+  tf.keras.layers.Rescaling(1./255),
+  tf.keras.layers.Conv2D(32, 3, activation='relu'),
+  tf.keras.layers.MaxPooling2D(),
+  tf.keras.layers.Dropout(0.4),
+  tf.keras.layers.Conv2D(32, 3, activation='relu'),
+  tf.keras.layers.MaxPooling2D(),
+  tf.keras.layers.Dropout(0.4),
+  tf.keras.layers.Conv2D(32, 3, activation='relu'),
+  tf.keras.layers.MaxPooling2D(),
+  tf.keras.layers.Flatten(),
+  tf.keras.layers.Dense(256, activation='relu'),
+  tf.keras.layers.Dense(num_classes, activation="softmax")
+])
+model.compile(
+  optimizer='adam',
+  loss='SparseCategoricalCrossentropy',
+  metrics=['accuracy'])
+
+model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=5
+)
+
+def predict_input_image(img):
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model.predict(img_4d)[0]
+  
+  return {class_names[i]: float(prediction[i]*0.100) for i in range(5)}
+
+
+
+
+
+
+image = gr.inputs.Image(shape=(180,180))
+label =gr.outputs.Label(num_top_classes=5)
+
+gr.Interface(fn=predict_input_image, inputs=image, outputs=label,title="Flowers Image Classification").launch()
+#pt