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| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| import json | |
| # let's load the image label file | |
| with open("/content/drive/MyDrive/imagenet_labels.json") as labels_file: | |
| labels = json.load(labels_file) | |
| mobile_net = tf.keras.applications.MobileNetV2() | |
| # let's create a function to classify an image | |
| def image_classifier(img): | |
| arr = np.expand_dims(img, axis=0) | |
| arr = tf.keras.applications.mobilenet.preprocess_input(arr) | |
| predictions = mobile_net.predict(arr).flatten() | |
| return {labels[i]:float(predictions[i]) for i in range(1000)} | |
| iface = gr.Interface(image_classifier, | |
| gr.inputs.Image(shape=(224,224)), | |
| gr.outputs.Label(num_top_classes = 5), | |
| capture_session = True, | |
| interpretation = 'default', | |
| title="JBimageCap Program For Image Caption", | |
| description = "This Project is called 'JBImageCap' . This project service classifies elements in images into intuitive categories, such as people, objects, environments, activities, or artwork, to define image themes and application scenarios. It supports on-cloud recognition modes. And this project has been created by Bitingo Josaphat JB", | |
| examples = [ | |
| ["/content/drive/MyDrive/images/cheetah1.jpg"], | |
| ["/content/drive/MyDrive/images/IMG-20210416-WA0047.jpg"], | |
| ["/content/drive/MyDrive/images/IMG-20210416-WA0042.jpg"], | |
| ["/content/drive/MyDrive/images/IMG-20210507-WA0022.jpg"], | |
| ["/content/drive/MyDrive/images/download.jpg"], | |
| ["/content/drive/MyDrive/images/lion.jpg"] | |
| ]) | |
| # Now Lemme launch My App From the Colab Environment | |
| iface.launch() |