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import gradio as gr
from fastai.vision.all import *
from fastcore.all import *
learn = load_learner("animal_species_detection.pkl")
categories = ("cat","horse","rabbit")
def classify_img(img):
pred,idx,probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
with gr.Blocks(title ="which animal is this ?") as demo:
with gr.Row():
gr.Markdown("""
## Animal species detection
#### click on the photos to and then click "PREDİCT" button
""")
with gr.Row():
image = gr.inputs.Image(shape=(192,192))
with gr.Row():
output = gr.outputs.Label()
with gr.Row():
image_button = gr.Button("PREDİCT")
image_button.click(classify_img, inputs=image, outputs=output)
with gr.Row():
with gr.Column():
gr.Examples(inputs=image,examples=["r1.jpg"],label="rabbit")
with gr.Column():
gr.Examples(inputs=image,examples=["r2.jpg"],label="rabbit")
with gr.Column():
gr.Examples(inputs=image,examples=["r2.jpg"],label="rabbit")
with gr.Column():
gr.Examples(inputs=image,examples=["h1.jpg"],label="horse")
with gr.Column():
gr.Examples(inputs=image,examples=["h2.jpg"],label="horse")
with gr.Column():
gr.Examples(inputs=image,examples=["h3.jpg"],label="horse")
with gr.Column():
gr.Examples(inputs=image,examples=["c1.jpg"],label="cat")
with gr.Column():
gr.Examples(inputs=image,examples=["c2.jpg"],label="cat")
with gr.Column():
gr.Examples(inputs=image,examples=["c3.jpg"],label="cat")
demo.launch()
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