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import gradio as gr
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
MODEL_PATH = "best_model_normal.keras"
CLASS_NAMES = [
"Leaf_Algal",
"Leaf_Blight",
"Leaf_Colletotrichum",
"Leaf_Healthy",
"Leaf_Phomopsis",
"Leaf_Rhizoctonia",
]
IMG_SIZE = (224, 224)
model = tf.keras.models.load_model(MODEL_PATH)
def predict(img):
# img: numpy array HxWx3 (uint8)
x = tf.image.resize(img, IMG_SIZE)
x = tf.cast(x, tf.float32)
x = preprocess_input(x) # pipeline train
x = tf.expand_dims(x, 0) # (1,224,224,3)
prob = model.predict(x, verbose=0)[0]
return {CLASS_NAMES[i]: float(prob[i]) for i in range(len(CLASS_NAMES))}
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy", label="Upload A Durian Leaf"),
outputs=gr.Label(num_top_classes=3, label="Result"),
title="Durian Leaf Disease Classification",
)
demo.launch()
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