CoralHealth / app.py
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import gradio as gr
import tensorflow as tf
import numpy
from PIL import Image
model_path = "Coralhealth.pb"
model = tf.saved_model.load(model_path)
classes = [ "bleached" , "healthy" , ]
def run(image_path):
img = Image.open(image_path).convert('RGB')
img = img.resize((300, 300 * img.size[1] // img.size[0]), Image.ANTIALIAS)
inp_numpy = numpy.array(img)[None]
inp = tensorflow.constant(inp_numpy, dtype='float32')
class_scores = model(inp)[0].numpy()
return class_scores
title = "Trash Detector"
description = (
""
)
examples = glob.glob("images/*.png")
interface = gr.Interface(
run,
inputs=[gr.components.Image(type="filepath")],
outputs="text",
title=title,
description=description,
examples=examples,
)
interface.queue().launch()