a

app.py

@@ -1,11 +1,38 @@
 import gradio as gr
 import cv2
+import requests
+import os
+
 from ultralytics import YOLO
 
-# Load YOLO model
+file_urls = [
+    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
+    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
+    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
+]
+
+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  = [['image_0.jpg'], ['image_1.jpg']]
+video_path = [['video.mp4']]
 
-# Function to perform object detection on an image
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
     outputs = model.predict(source=image_path)
@@ -21,9 +48,23 @@ def show_preds_image(image_path):
         )
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-# Function to perform object detection on a video stream
-def show_preds_video(video_stream):
-    cap = cv2.VideoCapture(video_stream.name)
+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="Pothole 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:
@@ -41,28 +82,23 @@ def show_preds_video(video_stream):
                 )
             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
 
-# Define interfaces for image and video inference
-interface_image = gr.Interface(
-    fn=show_preds_image,
-    inputs=gr.inputs.Image(type="file", label="Upload Image"),
-    outputs=gr.outputs.Image(type="numpy", label="Output Image"),
-    title="Pothole Detector (Image Input)",
-)
+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=gr.inputs.Video(type="webcam", label="Webcam Input"),
-    outputs=gr.outputs.Image(type="numpy", label="Output Image"),
-    title="Pothole Detector (Webcam Input)",
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Pothole detector",
+    examples=video_path,
+    cache_examples=False,
 )
 
-# Launch tabbed interface for both image and video inference
-gr.Interface(
+gr.TabbedInterface(
     [interface_image, interface_video],
-    title="Pothole Detector",
-    description="Detect potholes using YOLOv8 on images and webcam streams.",
-    examples=[
-        ["path/to/image.jpg"],
-        ["path/to/video.mp4"]
-    ]
-).launch()
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()