Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+MODEL_PATH = hf_hub_download(
+    repo_id="ggg4mless/RateBooru_Efficient",
+    filename="ratebooru_efficientnetb1.keras"
+)
+model = tf.keras.models.load_model(MODEL_PATH)
+
+class_names = ['explicit', 'general', 'questionable']
+IMG_SIZE = (240, 240)
+
+print("Loaded")
+
+def predict_image(input_img):
+    img = Image.fromarray(input_img.astype('uint8'), 'RGB')
+    img = img.resize(IMG_SIZE)
+
+    img_array = tf.keras.utils.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)
+
+    predictions = model.predict(img_array)
+
+    score = tf.nn.softmax(predictions[0])
+
+    confidences = {class_names[i]: float(score[i]) for i in range(len(class_names))}
+
+    return confidences
+
+image_input = gr.Image(label="Upload your Picture")
+label_output = gr.Label(num_top_classes=1, label="Prediction Result")
+
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=image_input,
+    outputs=label_output,
+    title="Ratebooru",
+    description="Upload image to be classified into categories: General, Questionable, or Explicit."
+)
+
+demo.launch(debug=False, share=True)