Create app.py

app.py

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+import gradio as gr
+import tensorflow
+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()