Upload folder using huggingface_hub

app.py

@@ -0,0 +1,36 @@
+
+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()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+numpy
+tensorflow==2.18.0