| 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() | |