Refactor app.py to load model and define class names before classifying image

app.py

@@ -1,26 +1,74 @@
 import gradio as gr
 import tensorflow as tf
-import numpy
-
+import numpy as np
 
+# Load the model
 model = tf.keras.models.load_model('plant_model_v5-beta.h5')
-class_names = {0: 'Apple___Apple_scab', 1: 'Apple___Black_rot', 2: 'Apple___Cedar_apple_rust', 3: 'Apple___healthy', 4: 'Not a plant', 5: 'Blueberry___healthy', 6: 'Cherry___Powdery_mildew', 7: 'Cherry___healthy', 8: 'Corn___Cercospora_leaf_spot Gray_leaf_spot', 9: 'Corn___Common_rust', 10: 'Corn___Northern_Leaf_Blight', 11: 'Corn___healthy', 12: 'Grape___Black_rot',
-               13: 'Grape___Esca_(Black_Measles)', 14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 15: 'Grape___healthy', 16: 'Orange___Haunglongbing_(Citrus_greening)', 17: 'Peach___Bacterial_spot', 18: 'Peach___healthy', 19: 'Pepper,_bell___Bacterial_spot', 20: 'Pepper,_bell___healthy', 21: 'Potato___Early_blight', 22: 'Potato___Late_blight', 23: 'Potato___healthy', 24: 'Raspberry___healthy', 25: 'Soybean___healthy', 26: 'Squash___Powdery_mildew', 27: 'Strawberry___Leaf_scorch', 28: 'Strawberry___healthy', 29: 'Tomato___Bacterial_spot', 30: 'Tomato___Early_blight', 31: 'Tomato___Late_blight', 32: 'Tomato___Leaf_Mold', 33: 'Tomato___Septoria_leaf_spot', 34: 'Tomato___Spider_mites Two-spotted_spider_mite', 35: 'Tomato___Target_Spot', 36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 37: 'Tomato___Tomato_mosaic_virus', 38: 'Tomato___healthy'}
+
+# Define the class names
+class_names = {
+    0: 'Apple___Apple_scab',
+    1: 'Apple___Black_rot',
+    2: 'Apple___Cedar_apple_rust',
+    3: 'Apple___healthy',
+    4: 'Not a plant',
+    5: 'Blueberry___healthy',
+    6: 'Cherry___Powdery_mildew',
+    7: 'Cherry___healthy',
+    8: 'Corn___Cercospora_leaf_spot Gray_leaf_spot',
+    9: 'Corn___Common_rust',
+    10: 'Corn___Northern_Leaf_Blight',
+    11: 'Corn___healthy',
+    12: 'Grape___Black_rot',
+    13: 'Grape___Esca_(Black_Measles)',
+    14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+    15: 'Grape___healthy',
+    16: 'Orange___Haunglongbing_(Citrus_greening)',
+    17: 'Peach___Bacterial_spot',
+    18: 'Peach___healthy',
+    19: 'Pepper,_bell___Bacterial_spot',
+    20: 'Pepper,_bell___healthy',
+    21: 'Potato___Early_blight',
+    22: 'Potato___Late_blight',
+    23: 'Potato___healthy',
+    24: 'Raspberry___healthy',
+    25: 'Soybean___healthy',
+    26: 'Squash___Powdery_mildew',
+    27: 'Strawberry___Leaf_scorch',
+    28: 'Strawberry___healthy',
+    29: 'Tomato___Bacterial_spot',
+    30: 'Tomato___Early_blight',
+    31: 'Tomato___Late_blight',
+    32: 'Tomato___Leaf_Mold',
+    33: 'Tomato___Septoria_leaf_spot',
+    34: 'Tomato___Spider_mites Two-spotted_spider_mite',
+    35: 'Tomato___Target_Spot',
+    36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+    37: 'Tomato___Tomato_mosaic_virus',
+    38: 'Tomato___healthy'
+}
 
 
 def classify_image(image):
     # Preprocess the image
-    img = tf.keras.preprocessing.image.load_img(
-        image.name, target_size=(256, 256))
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.image.resize(image, [256, 256])
     img_array = tf.expand_dims(img_array, 0) / 255.0
 
     # Make a prediction
     prediction = model.predict(img_array)
     predicted_class = tf.argmax(prediction[0], axis=-1)
+    confidence = np.max(prediction[0])
 
-    return class_names[predicted_class.numpy()]
+    return class_names[predicted_class.numpy()], confidence
 
 
-iface = gr.Interface(fn=classify_image, inputs="image", outputs="text")
+iface = gr.Interface(
+    fn=classify_image,
+    inputs="image",
+    outputs=["text", "number"],
+    interpretation="default",
+    examples=[
+        ['https://i.imgur.com/Ls6rCuQ.jpg'],
+        ['https://i.imgur.com/3Y68VBX.jpg'],
+    ])
 iface.launch()