Create app.py

app.py

@@ -0,0 +1,111 @@
+import gradio as gr
+import cv2
+import requests
+import os
+
+from ultralytics import YOLO
+
+file_urls = ["https://www.dropbox.com/scl/fi/i582tgw95r0f8i8cssmx0/images.jpg?rlkey=fa3d74yaj0bh941jo67n0elns&dl=0"
+    # 'https://www.dropbox.com/scl/fi/z4tnnills03s1o4evbqpl/download.jpg?rlkey=gmh63kexnnjcva6ahzo2wfmsd&dl=0'
+]
+
+def download_file(url, save_name):
+    url = url
+    if not os.path.exists(save_name):
+        file = requests.get(url)
+        open(save_name, 'wb').write(file.content)
+
+for i, url in enumerate(file_urls):
+    if 'mp4' in file_urls[i]:
+        download_file(
+            file_urls[i],
+            f"video.mp4"
+        )
+    else:
+        download_file(
+            file_urls[i],
+            f"image_{i}.jpg"
+        )
+
+model = YOLO('best.pt')
+path  = [['images.jpg'],['images_1.png'],['image/i1.png'],['image/i2.png'],['image/i3.png']]
+#          ,['image/i4.png'],['image/i5.png'],['image/i6.png'],['image/i7.png'],['image/i8.png'],['image/i9.png'],['image/i10.png'],['image/i11.png'],
+#          ['image/i12.png'],['image/i13.png'],['image/i14.png'],['image/i15.png'],['image/i16.png'],['image/i17.png'],['image/i18.png'],['image/i19.png'],
+#          ['image/i20.png'],['image/i21.png'],['image/i22.png'],['image/i23.png'],['image/i24.png'],['image/i25.png'],['image/i26.png'],['image/i27.png'],['image/i28.png']]
+# video_path = [['video.mp4']]
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path)
+    results = outputs[0].cpu().numpy()
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(
+            image,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Airport Luggage Weapon Detector app",
+    examples=path,
+    cache_examples=False,
+)
+
+def show_preds_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if ret:
+            frame_copy = frame.copy()
+            outputs = model.predict(source=frame)
+            results = outputs[0].cpu().numpy()
+            for i, det in enumerate(results.boxes.xyxy):
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+
+inputs_video = [
+    gr.components.Video(),
+
+]
+outputs_video = [
+    gr.components.Image(),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Airport Luggage Weapon Detector",
+    cache_examples=False,
+)
+
+MORE = """ ## TRY Other Models
+        ![imagea](image_path 'image/i5.png')
+        ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image
+        """
+
+gr.Markdown(MORE)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()