removed magic !pip installs

app.py

@@ -7,10 +7,14 @@ Original file is located at
     https://colab.research.google.com/drive/1ckZU76dq3XWcpa5PpQF8a6qJwkTttg8v
 
 # ⚙️ Setup
-
-## Fix random seeds
 """
 
+#!pip install gradio -q
+#!pip install wget -q
+#!pip install tensorflow_addons -q
+
+"""## Fix random seeds"""
+
 SEED = 11
 import os
 os.environ['PYTHONHASHSEED']=str(SEED)
@@ -24,46 +28,22 @@ tf.random.set_seed(SEED)
 
 """## Imports"""
 
-!pip install gradio -q
-
 import gradio as gr
-
-from scipy import linalg
-import matplotlib.pyplot as plt
+import wget
 import pandas as pd
-
-from tensorflow import keras
-from tensorflow.keras import layers
-from keras.applications.inception_v3 import InceptionV3, preprocess_input
-
-from tensorflow.keras.layers import Layer, Input, Dense, Reshape, Flatten
-from tensorflow.keras.layers import Conv2D, Conv2DTranspose, ReLU, LeakyReLU
-from tensorflow.keras.layers import Dropout, Embedding, Concatenate, Add, Activation
-from tensorflow.keras.layers import GlobalAveragePooling2D, UpSampling2D, BatchNormalization
-import tensorflow.keras.backend as K
-
-from tensorflow.python.keras.utils import conv_utils
-from tensorflow.keras.initializers import RandomNormal
-from tensorflow.keras.optimizers import Adam
-
-!pip install tensorflow_addons
-import tensorflow_addons as tfa
-from tensorflow_addons.layers import SpectralNormalization
-
 import gdown
 from zipfile import ZipFile
 
-from tqdm.notebook import tqdm
-
 """## Download CelebA attributes
 
 We'll use face images from the CelebA dataset, resized to 64x64.
 """
 
 #Download labels from public github, they have been processed in a 0,1 csv file
-!mkdir "/content/celeba_gan"
-!wget -q -O "/content/celeba_gan/list_attr_celeba01.csv.zip" "https://github.com/buoi/conditional-face-GAN/blob/main/list_attr_celeba01.csv.zip?raw=true" 
-!unzip -o "/content/celeba_gan/list_attr_celeba01.csv.zip" -d "/content/celeba_gan"
+os.mkdir("/content/celeba_gan")
+wget.download(url="https://github.com/buoi/conditional-face-GAN/blob/main/list_attr_celeba01.csv.zip?raw=true", out="/content/celeba_gan/list_attr_celeba01.csv.zip")
+import shutil
+shutil.unpack_archive(filename="/content/celeba_gan/list_attr_celeba01.csv.zip", extract_dir="/content/celeba_gan")
 
 """## Dataset preprocessing functions"""
 
@@ -127,12 +107,13 @@ acgan40_hd = ModelEntry('buio','booicugb','acgan10_nonseparBNstdev_split_299_218
 #2o3z6bqb SAGAN_5 v17 buianifolli
 #zscel8bz SAGAN_6 v29 buianifolli
 
+#wandb artifacts
 #sagan40 v18
 
-keras_metadata_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3MzA4NTY=/5f09f68e9bb5b09efbc37ad76cdcdbb0"
-saved_model_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/2676cd88ef1866d6e572916e413a933e"
-variables_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/5cab1cb7351f0732ea137fb2d2e0d4ec"
-index_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/480b55762c3358f868b8cce53984736b"
+#keras_metadata_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3MzA4NTY=/5f09f68e9bb5b09efbc37ad76cdcdbb0"
+#saved_model_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/2676cd88ef1866d6e572916e413a933e"
+#variables_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/5cab1cb7351f0732ea137fb2d2e0d4ec"
+#index_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/480b55762c3358f868b8cce53984736b"
 
 #sagan10 v16
 
@@ -141,9 +122,6 @@ saved_model_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY
 variables_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMzQ0Mjg=/a62bf0c4bf7047c0a31df7d2cfdb54f0"
 index_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMzQ0Mjg=/de6539a7f0909d1dafa89571c7df43d1"
 
-
-
-
 #download model
 gan_path = "/content/gan_model/"
 try:
@@ -157,7 +135,7 @@ os.makedirs(gan_path,exist_ok =True)
 os.makedirs(gan_path+"/variables",exist_ok =True)
 
 
-!pip install wget -q
+
 import wget
 wget.download(keras_metadata_url, gan_path+"keras_metadata.pb",)
 wget.download(saved_model_url, gan_path+"saved_model.pb")
@@ -243,28 +221,4 @@ iface = gr.Interface(
     "image",
     layout='unaligned'
 )
-iface.launch(debug=True)
-
-def sentence_builder(quantity, animal, place, activity_list, morning):
-    return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
-
-def generate_image(attributes)
-
-iface = gr.Interface(
-    sentence_builder,
-    [
-        gr.inputs.Slider(2, 20),
-        gr.inputs.Dropdown(["cat", "dog", "bird"]),
-        gr.inputs.Radio(["park", "zoo", "road"]),
-        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
-        gr.inputs.Checkbox(label="Is it the morning?"),
-    ],
-    "text",
-    examples=[
-        [2, "cat", "park", ["ran", "swam"], True],
-        [4, "dog", "zoo", ["ate", "swam"], False],
-        [10, "bird", "road", ["ran"], False],
-        [8, "cat", "zoo", ["ate"], True],
-    ],
-)
 iface.launch()