Spaces:
Build error
Build error
File size: 1,574 Bytes
f7dfb0f 9c52842 259984c f7dfb0f 9c52842 cc1b998 f7dfb0f d995000 259984c 9c52842 5dc2040 9c52842 3662000 ae16aa7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
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 == "model1":
return tf.keras.models.load_model("model1.h5")
elif model_name == "model2":
return tf.keras.models.load_model("model2.h5")
elif model_name == "model3":
return tf.keras.models.load_model("model3.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)}
interface = gr.Interface(
predict_top_classes,
[
gr.inputs.Image(type='pil'),
gr.inputs.Button(label="Model 1 (Xception)", value="model1"),
gr.inputs.Button(label="Model 2 (InceptionV3)", value="model2"),
gr.inputs.Button(label="Model 3 (InceptionResNetV2)", value="model3"),
],
outputs='label'
)
interface.launch()
|