Initial Commit

Added the model inference pipeline.

app.py

@@ -0,0 +1,40 @@
+import numpy as np
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+import gradio as gr
+
+IMAGE_SIZE = 72
+# labels taken from https://huggingface.co/datasets/cifar10
+labels = {0: "airplane",
+   1: "automobile"
+   2: "bird"
+   3: "cat"
+   4: "deer"
+   5: "dog"
+   6: "frog"
+   7: "horse"
+   8: "ship"
+   9: "truck"}
+
+
+model = from_pretrained_keras("keras-io/randaugment")
+
+
+
+def predict_img_label(img):
+  inp = tf.image.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
+  pred = model.predict(tf.expand_dims(inp, 0)).flatten()
+  return {labels[i]: float(pred[i]) for i in range(len(labels))}
+
+image = gr.inputs.Image()
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(
+    fn=predict_img_label, inputs=image, outputs=label, interpretation="default").launch()
+
+title = "Image Classification Model Using RandAugment"
+description = "Upload an image to classify images"
+
+article = "<div style='text-align: center;'><a href='https://github.com/BishmoyPaul' target='_blank'>Space by Bishmoy Paul</a><br><a href='https://keras.io/examples/vision/randaugment/' target='_blank'>Keras example by Sayak Paul</a></div>"
+gr.Interface(predict_img_label, inputs=image, outputs=label, allow_flagging=False, analytics_enabled=False,
+  title=title, description=description, article=article).launch(enable_queue=True)