Update app.py

app.py

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+import gradio as gr
+import cv2
+import os
+ 
+from ultralytics import YOLO
+
+
+model = YOLO('yolov8x-seg.pt')
+path  = [['3891186464_00d76e10a2_z.jpg'], ['images (1).jpeg']]
+video_path = [['sheep.mp4']]
+
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    image_copy=image.copy()
+    threshold = 0.1
+    results = model(image)[0]
+    for result in results.boxes.data.tolist():
+                x1, y1, x2, y2, score, class_id = result
+
+                if score > threshold:
+                    cv2.rectangle(image_copy, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+                    cv2.putText(image_copy, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
+                    cv2.putText(image_copy, str(score), (int(x1), int(y2 + 10)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3, cv2.LINE_AA)
+    return cv2.cvtColor(image_copy, 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="Animal detector using YOLOV8 NANO",
+    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:
+            threshold = 0.1
+            frame_copy = frame.copy()
+            results = model(frame)[0]
+            for result in results.boxes.data.tolist():
+                x1, y1, x2, y2, score, class_id = result
+
+                if score > threshold:
+                    cv2.rectangle(frame_copy, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+                    cv2.putText(frame_copy, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
+                    cv2.putText(frame_copy, str(score), (int(x1), int(y2 + 10)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3, cv2.LINE_AA)
+            
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+ 
+inputs_video = [
+    gr.components.Video(type="filepath", label="Input Video"),
+ 
+]
+outputs_video = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Cattle detector using YOLOV8 NANO",
+    examples=video_path,
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).launch()