Delete App.py

App.py

@@ -1,91 +0,0 @@
-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