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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train_cls_best_small.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,47 @@
+import os
+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+image_directory = 'home/user/app/example_images'
+
+img_files = [file for file in os.listdir(
+    image_directory) if file.lower().endswith('.jpg') or file.lower().endswith('.png')]
+
+path = [os.path.join(image_directory, filename) for filename in img_files]
+
+model = YOLO('home/user/app/train_cls_best_small.pt')
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_text = [
+    gr.components.Textbox(type="text", label="Model predict"),
+]
+
+
+def show_preds_image(image_path):
+
+    results = model(image_path)
+
+    names_dict = results[0].names
+
+    probs = results[0].probs.data.tolist()
+
+    return names_dict[np.argmax(probs)], np.max(probs)
+
+
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_text,
+    title="Cat vs Dog",
+    examples=path,
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image Inference'],
+).queue().launch(debug=True)

requirements.txt

@@ -0,0 +1,3 @@
+opencv-python
+gradio
+ultralytics

train_cls_best_small.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f40a9723131ab60b2ab621c023d1b534385055bd39b99bb3b90e7dc1ade0ade
+size 10382048