Create app.py

app.py

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+import gradio as gr
+from keras.models import load_model
+from keras.preprocessing.image import ImageDataGenerator
+import numpy as np
+from tensorflow.keras.utils import img_to_array
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from PIL import Image
+
+# model path
+cnn_model = load_model('./model/cnn_model.h5')
+resnet_model = load_model('./model/resnet_model.h5')
+
+import json
+
+with open('data/class_dict.json', 'r') as json_file:
+    class_dict = json.load(json_file)
+
+# get class names
+class_names = [class_dict[i] for i in sorted(class_dict.keys())]
+
+
+def classify_insect(model_name, img):
+    img = img.resize((150, 150))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_preprocessed = preprocess_input(img_array)
+
+    # Select the model based on the dropdown choice
+    if model_name == "CNN Model":
+        model = cnn_model
+    elif model_name == "Transfer Learning ResNet":
+        model = resnet_model
+
+    # Make a prediction
+    prediction = model.predict(img_preprocessed)
+    return {class_name: float(score) for class_name, score in zip(class_names, prediction[0])}
+
+iface = gr.Interface(
+    fn=classify_insect,
+    inputs=[
+        gr.Dropdown(choices=["CNN Model", "Transfer Learning ResNet"], label="Select Model"),
+        gr.Image(shape=(150,150))
+    ],
+    outputs=gr.Label(num_top_classes=3)
+)
+
+iface.launch(share=True)