Create app.py

app.py

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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.
+
+        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"),
+        ],
+        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()