Add application file

app.py

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+
+import requests
+import tensorflow as tf
+
+import gradio as gr
+
+inception_net = tf.keras.applications.MobileNetV2()  # load the model
+
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+
+def classify_image(inp):
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+    prediction = inception_net.predict(inp).flatten()
+    return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+
+title = "Image Classifiction + Interpretation"
+description = """
+Task: Image Classification\n
+Dataset: COCO 2017, 1,000 classes\n
+Model: https://huggingface.co/google/mobilenet_v2_1.0_224\n
+Developer: Google \n
+"""
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
+examples = [
+    ["buger.jpg"],
+    ["goldfish.jpg"],
+    ["lake-house.jpg"],
+    ["truck.jpg"],
+]
+
+demo = gr.Interface(
+    fn=classify_image, 
+    inputs=image, 
+    outputs=label, 
+    interpretation="default", 
+    title=title,
+    description=description,
+    examples=examples,
+    theme="freddyaboulton/dracula_revamped",
+    )
+
+demo.launch()

requirements.txt

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+gradio==3.36.1
+tensorflow==2.12.0