Update app.py

app.py

@@ -4,6 +4,7 @@ import requests
 import os
 from ultralytics import YOLO
 
+# Downloading the necessary files
 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',
@@ -21,6 +22,7 @@ for i, url in enumerate(file_urls):
     else:
         download_file(url, f"image_{i}.jpg")
 
+# Define the colors for different classes
 colors = {
     0: (255, 0, 0),    # Red for class 0
     1: (0, 128, 0),    # Green (dark) for class 1
@@ -32,9 +34,8 @@ colors = {
     7: (0, 225, 0),    # Green for class 7
 }
 
+# Load the YOLO model
 model = YOLO('modelbest.pt')
-image_paths = [['image_0.jpg'], ['image_1.jpg']]
-video_paths = [['video.mp4']]
 
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
@@ -69,9 +70,7 @@ interface_image = gr.Interface(
     fn=show_preds_image,
     inputs=inputs_image,
     outputs=outputs_image,
-    title="Smoke Detection on Indian Roads",
-    examples=image_paths,
-    cache_examples=False,
+    title="Smoke Detection on Indian Roads"
 )
 
 def show_preds_video(video_path):
@@ -122,287 +121,10 @@ interface_video = gr.Interface(
     fn=show_preds_video,
     inputs=inputs_video,
     outputs=outputs_video,
-    title="Smoke Detection on Indian Roads",
-    examples=video_paths,
-    cache_examples=False,
+    title="Smoke Detection on Indian Roads"
 )
 
 gr.TabbedInterface(
     [interface_image, interface_video],
     tab_names=['Image inference', 'Video inference']
 ).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):
-#     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")
-
-# colors = {
-#     0: (255, 0, 0),    # Red for class 0
-#     1: (0, 128, 0),    # Green (dark) for class 1
-#     2: (0, 0, 255),    # Blue for class 2
-#     3: (255, 255, 0),  # Yellow for class 3
-#     4: (255, 0, 255),  # Magenta for class 4
-#     5: (0, 255, 255),  # Cyan for class 5
-#     6: (128, 0, 0),    # Maroon for class 6
-#     7: (0, 225, 0),    # Green for class 7
-# }
-
-# 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):
-#         class_id = int(results.boxes.cls[i])
-#         label = model.names[class_id]
-
-#         # Get the bounding box coordinates
-#         x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
-        
-#         # Draw the bounding box with the specified color
-#         color = colors.get(class_id, (0, 0, 255))
-#         cv2.rectangle(image, (x1, y1), (x2, y2), color, 2, cv2.LINE_AA)
-        
-#         # Calculate text size and position
-#         label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.75, 2)
-#         text_x = x1 + (x2 - x1) // 2 - label_size[0] // 2
-#         text_y = y1 + (y2 - y1) // 2 + label_size[1] // 2
-
-#         # Draw the label text
-#         cv2.putText(image, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2, cv2.LINE_AA)
-
-#     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    
-# # 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.Image(type="filepath", label="Input Image"),
-# ]
-# outputs_image = [
-#     gr.Image(type="numpy", label="Output Image"),
-# ]
-
-# interface_image = gr.Interface(
-#     fn=show_preds_image,
-#     inputs=inputs_image,
-#     outputs=outputs_image,
-#     title="Smoke Detection on Indian Roads",
-#     examples=path,
-#     cache_examples=False,
-# )
-
-# def show_preds_video(video_path):
-#     # Open the input video
-#     cap = cv2.VideoCapture(video_path)
-    
-#     # Get video properties
-#     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-#     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-#     fps = int(cap.get(cv2.CAP_PROP_FPS))
-    
-#     # Define the codec and create a VideoWriter object
-#     fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 'mp4v' for .mp4 format
-#     out = cv2.VideoWriter('output_video.mp4', fourcc, fps, (width, height))
-    
-#     while cap.isOpened():
-#         ret, frame = cap.read()
-#         if not ret:
-#             break
-        
-#         frame_copy = frame.copy()
-#         outputs = model.predict(source=frame)
-#         results = outputs[0].cpu().numpy()
-
-#         for i, det in enumerate(results.boxes.xyxy):
-#             class_id = int(results.boxes.cls[i])
-#             label = model.names[class_id]
-
-#             # Get the bounding box coordinates
-#             x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
-            
-#             # Draw the bounding box with the specified color
-#             color = colors.get(class_id, (0, 0, 255))
-#             cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2, cv2.LINE_AA)
-            
-#             # Calculate text size and position
-#             label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.75, 2)
-#             text_x = x1 + (x2 - x1) // 2 - label_size[0] // 2
-#             text_y = y1 + (y2 - y1) // 2 + label_size[1] // 2
-
-#             # Draw the label text
-#             cv2.putText(frame_copy, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2, cv2.LINE_AA)
-
-#         # Write the frame to the output video
-#         out.write(frame_copy)
-    
-#     # Release everything
-#     cap.release()
-#     out.release()
-
-#     return 'output_video.mp4'
-
-# # Updated Gradio interface
-# inputs_video = [
-#     gr.Video(format="mp4", label="Input Video"),
-# ]
-# outputs_video = [
-#     gr.Video(label="Output Video"),
-# ]
-# interface_video = gr.Interface(
-#     fn=show_preds_video,
-#     inputs=inputs_video,
-#     outputs=outputs_video,
-#     title="Smoke Detection on Indian Roads",
-#     examples=video_path,
-#     cache_examples=False,
-# )
-# gr.TabbedInterface(
-#     [interface_image, interface_video],
-#     tab_names=['Image inference', 'Video inference']
-# ).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()