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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import gdown
+from PIL import Image
+
+
+
+input_shape = (32, 32, 3)
+resized_shape = (224, 224, 3)
+num_classes = 10
+labels = {
+    0: "plane",
+    1: "car",
+    2: "bird",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "frog",
+    7: "horse",
+    8: "ship",
+    9: "truck",
+}
+
+# a file
+url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL"
+output = "modelV2Lmixed.keras"
+gdown.download(url, output, quiet=False)
+
+
+def load_model():
+    model = tf.keras.models.load_model("./modelV2Lmixed.keras")
+    return model
+
+
+def classify_image(image, model):
+    image = tf.cast(image, tf.float32)
+    image = tf.image.resize(image, [32, 32])
+    image = np.expand_dims(image, axis=0)
+    prediction = model.predict(image)
+    confidences = {labels[i]: float(prediction[i]) for i in range(10)}
+    return confidences
+
+
+model = load_model()
+
+
+gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(32, 32)),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["banana.jpg", "car.jpg"]).launch()

requirements.txt

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+tensorflow
+gdown
+gradio