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# -*- coding: utf-8 -*-
"""
Created on Sun Jan 28 18:48:07 2024
@author: liewchooichin
"""
import os
import pathlib
import gradio as gr
import pandas as pd
# my own py to make predictions
import image_pretrained
# global variables
# predictions from:
pred_eff = pd.DataFrame() # Efficient Net
pred_mob = pd.DataFrame() # Mobile Net
pred_xcept = pd.DataFrame() # Xception
def get_prediction(img_path):
pred_eff, pred_mob, pred_xcept = \
image_pretrained.predict(img_path)
print(pred_eff)
return pred_eff, pred_mob, pred_xcept
def clear_image(img):
# Clear the previous output result
return pred_eff, pred_mob, pred_xcept
with gr.Blocks() as demo:
image_width = 256
image_height = 256
gr.Markdown(
"""
# Image classfication
Predict the class of the image with pretrained model.
Models: Xception, MobileNet V3 Small, \
EfficientNet V2 Small.
These models were trained on ImageNet 1000. \
Go to [IMAGENET 1000 Class List]\
(https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/)\
to see what objects the models can recognize.
Tip: The ImageNet does not contain classes for people or faces.\
Input some image of people and human faces and the model will give \
interesting predictions!
Top three predictions of classes are shown for each \
of the model.
Upload an image for predictions of its class and \
its probabilities.
"""
)
with gr.Row():
with gr.Column():
img = gr.Image(height=image_height,
width=image_width,
sources=["upload", "clipboard"],
interactive=True,
type="filepath")
# label_1 = gr.Label(label="Efficient net")
# label_2 = gr.Label(label="Mobile net")
# label_3 = gr.Label(label="Xception")
with gr.Column():
text_1 = gr.Text(label="Efficient net v2")
text_2 = gr.Text(label="Mobile net v3")
text_3 = gr.Text(label="Xception")
# load the images directory
data_dir = "images"
img_path = pathlib.Path(data_dir)
image_list = [[i] for i in list(img_path.glob("*.jpg"))]
print(f"List of examples: {image_list}")
examples = gr.Examples(
examples=[
os.path.join(os.path.dirname(__file__), "images",
"cat.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"mrt_train.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"duck.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"daisy.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"apples.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"bus.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"butterfly.jpg"),
os.path.join(os.path.dirname(__file__), "images",
"me_small.jpg"),
],
inputs=[img],
outputs=[text_1, text_2, text_3],
run_on_click=True,
fn=get_prediction
)
# prediction when a file is uploaded
img.upload(fn=get_prediction, inputs=[img],
outputs=[text_1, text_2, text_3])
# when an example is clicked
img.change(fn=get_prediction, inputs=[img],
outputs=[text_1, text_2, text_3])
# when an image is cleared
img.clear(fn=clear_image, inputs=[img],
outputs=[text_1, text_2, text_3])
if __name__ == "__main__":
demo.launch()
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