Create app.py

app.py

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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()