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import gradio as gr
import tensorflow as tf
import numpy as np
num_classes = 200
IMG_HEIGHT = 300
IMG_WIDTH = 300
with open("classlabel.txt", 'r') as file:
CLASS_LABEL = [x.strip() for x in file.readlines()]
def normalize_image(img):
img = tf.cast(img, tf.float32) / 255.0
img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH), method='bilinear')
return img
def load_model(model_name):
# Load the model based on the model_name input
if model_name == 1:
return tf.keras.models.load_model("model/Xception.h5")
elif model_name == 2:
return tf.keras.models.load_model("model/InceptionV3.h5")
elif model_name == 3:
return tf.keras.models.load_model("model/InceptionResNetV2.h5")
elif model_name == 4:
return tf.keras.models.load_model("model/DenseNet201.h5")
else:
raise ValueError("Invalid model_name")
def predict_top_classes(img, model_name):
img = img.convert('RGB')
img_data = normalize_image(img)
x = np.array(img_data)
x = np.expand_dims(x, axis=0)
model = load_model(model_name)
temp = model.predict(x)
idx = np.argsort(np.squeeze(temp))[::-1]
top5_value = np.asarray([temp[0][i] for i in idx[0:5]])
top5_idx = idx[0:5]
return {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
models = {
"Xception": 1,
"InceptionV3": 2,
"InceptionResNetV2": 3,
"DenseNet201": 4
}
def dropdown_example(choice, img):
model_name = models[choice]
return predict_top_classes(img, model_name)
dropdown = gr.inputs.Dropdown(
choices=list(models.keys()),
type="str",
label="Select a model"
)
image_input = gr.inputs.Image(type='pil')
interface = gr.Interface(
fn=dropdown_example,
inputs=[dropdown, image_input],
outputs='label'
)
interface.launch()
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