Update app.py

app.py

@@ -2,32 +2,24 @@ import gradio as gr
 import cv2
 import requests
 import os
- 
 from ultralytics import YOLO
+
 file_urls = [
-    'https://www.dropbox.com/scl/fi/kqd1z6wby1212c6ndodb3/Pol_20_jpg.rf.133c835b66958a7d48c12deeda31a719.jpg?rlkey=uqgvs2cwvahnmju15fv1zgorg&st=snv2yvtk&dl=0',
-    'https://www.dropbox.com/scl/fi/39aakapeh2y5ztk94rsyu/11e-a347-3f2d_jpg.rf.c66e5aeb57ee2ed660fdf0162156127d.jpg?rlkey=xoi3iw45vksgiejycau2ha7fh&st=etiawigv&dl=0',
-    'https://www.dropbox.com/scl/fi/8f08ehy53vsemw164g8n7/Recording2024-06-26184319.mp4?rlkey=pnmov906ttodl0cm92rpvc5ta&st=2twc9pjn&dl=0'
+    # '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"
-        )
+        download_file(file_urls[i], f"video.mp4")
     else:
-        download_file(
-            file_urls[i],
-            f"image_{i}.jpg"
-        )
+        download_file(file_urls[i], f"image_{i}.jpg")
 
 model = YOLO('modelbest.pt')
 path  = [['image_0.jpg'], ['image_1.jpg']]
@@ -47,12 +39,12 @@ def show_preds_image(image_path):
             lineType=cv2.LINE_AA
         )
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- 
+
 inputs_image = [
-    gr.components.Image(type="filepath", label="Input Image"),
+    gr.Image(type="filepath", label="Input Image"),
 ]
 outputs_image = [
-    gr.components.Image(type="numpy", label="Output Image"),
+    gr.Image(type="numpy", label="Output Image"),
 ]
 
 interface_image = gr.Interface(
@@ -64,10 +56,9 @@ interface_image = gr.Interface(
     cache_examples=False,
 )
 
-
 def show_preds_video(video_path):
     cap = cv2.VideoCapture(video_path)
-    while(cap.isOpened()):
+    while cap.isOpened():
         ret, frame = cap.read()
         if ret:
             frame_copy = frame.copy()
@@ -83,13 +74,12 @@ def show_preds_video(video_path):
                     lineType=cv2.LINE_AA
                 )
             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
- 
+
 inputs_video = [
-    gr.components.Video(type="filepath", label="Input Video"),
- 
+    gr.Video(format="mp4", label="Input Video"),
 ]
 outputs_video = [
-    gr.components.Image(type="numpy", label="Output Image"),
+    gr.Image(type="numpy", label="Output Image"),
 ]
 interface_video = gr.Interface(
     fn=show_preds_video,
@@ -103,4 +93,111 @@ interface_video = gr.Interface(
 gr.TabbedInterface(
     [interface_image, interface_video],
     tab_names=['Image inference', 'Video inference']
-).queue().launch()
+).queue().launch()
+
+# import gradio as gr
+# import cv2
+# import requests
+# import os
+ 
+# from ultralytics import YOLO
+# file_urls = [
+#     'https://www.dropbox.com/scl/fi/kqd1z6wby1212c6ndodb3/Pol_20_jpg.rf.133c835b66958a7d48c12deeda31a719.jpg?rlkey=uqgvs2cwvahnmju15fv1zgorg&st=snv2yvtk&dl=0',
+#     'https://www.dropbox.com/scl/fi/39aakapeh2y5ztk94rsyu/11e-a347-3f2d_jpg.rf.c66e5aeb57ee2ed660fdf0162156127d.jpg?rlkey=xoi3iw45vksgiejycau2ha7fh&st=etiawigv&dl=0',
+#     'https://www.dropbox.com/scl/fi/8f08ehy53vsemw164g8n7/Recording2024-06-26184319.mp4?rlkey=pnmov906ttodl0cm92rpvc5ta&st=2twc9pjn&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('modelbest.pt')
+# path  = [['image_0.jpg'], ['image_1.jpg']]
+# 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="Pothole detector",
+#     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(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="Pothole detector",
+#     examples=video_path,
+#     cache_examples=False,
+# )
+
+# gr.TabbedInterface(
+#     [interface_image, interface_video],
+#     tab_names=['Image inference', 'Video inference']
+# ).queue().launch()