Started develop API. Upload images for ex. and upload model.

.gitignore

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+*.keras

app.py

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+import gradio as gr
+
+# For loading files
+from joblib import dump, load
+
+# Model hub
+import tensorflow_hub as hub
+
+# Neural networks
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from keras.applications.vgg16 import preprocess_input
+
+# Image processing
+import PIL
+
+#------------------------------------------
+
+# Loading files
+path = './trained_models/'
+filename_model_VGG16 = 'vgg16_best.keras'
+example_images_path = './example_images'
+
+# Loading model
+model = keras.models.load_model(path + filename_model_VGG16)
+
+# Defining parameters
+breed_names_norm = [Chihuahua, papillon, beagle,
+                    Yorkshire_terrier, Australian_terrier,
+                    Scotch_terrier, golden_retriever,
+                    malinois, kelpie, Doberman, miniature_pinscher,
+                    Great_Dane, Pomeranian, standard_poodle, Mexican_hairless]
+
+
+# Importing stopwords
+with open('./stopwords/stopwords.txt') as file:
+    my_stopwords = {line.rstrip() for line in file}
+
+# Function definitions
+
+def load_img_path(img_path, target_size, show=False):
+    """
+    Loads an image in jpg format into PIL format from a path.
+    """
+    img = keras.preprocessing.image.load_img(
+        img_path, target_size=target_size
+        )
+    if show == True:
+        img.show()
+
+    return img
+
+def predict(model, img, show=False):
+    """
+    Returns a dictionnary: predicted_breeds, where the
+    keys are [1,2,3] for the first, second and third more probable
+    breed for the dog image. Each value is a dictionnary with keys
+    ['idx', 'name', 'confidence'] and their corresponding values.
+    
+    Parameters:
+        img: returned by the function load_img_path
+    """
+    img_array = keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # Creates a batch axis
+
+    predictions = model.predict(img_array, verbose=0)
+    scores = predictions[0]
+
+    # Keep the 3 more probable breeds
+    predicted_breeds = {}
+    for i in [1,2,3]:
+        idx = scores.argsort()[-i]  # First breed is the last proba when sorted
+        name = breed_names_norm[idx]
+        confidence = round(scores[idx]*100,2)
+        predicted_breeds[i] = {'idx':idx,'name':name,'confidence':confidence}
+       
+    return predicted_breeds
+
+# --------------------------------------------------
+
+examples = [
+    ["Jquery/Javascript Opacity animation with scroll <p>I'm looking to change the opacity on an object (and have the transition be animated) based on a users scroll.\nexample(http://davegamache.com/)</p>\n\n<p>I've searched everywhere\nlike here, but it ends up pointing me to the waypoints plugin (http://stackoverflow.com/questions/6316757/opacity-based-on-scroll-position)</p>\n\n<p>I've implemented the [waypoints][1] plugin and have the object fading once it's higher than 100px. [Using the offet attribute] but would like to basically control the opacity of an object and have the animation be visible like the above example.</p>\n\n<p>I've searched all over- this is my last resort.\nAny help is greatly appreciated.</p>\n"],
+    ['Setting cross-domain cookies in Safari <p>I have to call domain A.com (which sets the cookies with http) from domain B.com.\nAll I do on domain B.com is (javascript): </p>\n\n<pre><code>var head = document.getElementsByTagName("head")[0];\nvar script = document.createElement("script");\nscript.src = "A.com/setCookie?cache=1231213123";\nhead.appendChild(script);\n</code></pre>\n\n<p>This sets the cookie on A.com on every browser I\'ve tested, except Safari.\nAmazingly this works in IE6, even without the P3P headers.</p>\n\n<p>Is there any way to make this work in Safari?</p>\n'],
+    ['Database migrations for SQL Server <p>I need a database migration framework for SQL Server, capable of managing both schema changes and data migrations.</p>\n\n<p>I guess I am looking for something similar to django\'s <a href="http://south.aeracode.org/" rel="noreferrer">South</a> framework here.</p>\n\n<p>Given the fact that South is tightly coupled with django\'s ORM, and the fact that there\'s so many ORMs for SQL Server I guess having just a generic migration framework, enabling you to write and execute in controlled and sequential manner SQL data/schema change scripts should be sufficient.</p>\n'],
+]
+
+demo = gr.Interface(fn=tag_suggestion, 
+                    inputs="text", 
+                    outputs=["text"],
+                    examples=examples)
+
+
+if __name__ == "__main__":
+    demo.launch()
+

requirements.txt

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+gradio
+joblib
+tensorflow
+tensorflow_hub
+scikit-learn