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
+
+CLASS_NAMES = ["Oral Homogenous Leukoplakia", "Oral Non-Homogenous Leukoplakia", "Other Oral White Lesions"]
+model = tf.keras.models.load_model('./model.keras')
+
+def predict(image):
+    img = image.convert("RGB").resize((224, 224))
+    arr = (np.array(img, dtype=np.float32) / 127.5) - 1.0
+    preds = model.predict(np.expand_dims(arr, 0))[0]
+    idx = int(np.argmax(preds))
+    return {"className": CLASS_NAMES[idx], "confidence": round(float(preds[idx]) * 100, 1)}
+
+demo = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="json")
+demo.launch()