this file u provided isnt a fully functional next.js app? Why add the required files also set this as const sample images ["/samples/frame_0251.jpg", "/samples/frame_0252.jpg", "/samples/frame_0253.jpg", "/samples/frame_0254.jpg"]. now remove the Paste Image URL Option because we dont need it for now. Also for processing and visualization this is the python code i was running locally in my machine import cv2
import numpy as np
import os
import time
from ultralytics import YOLO
import torch

Path to the model weights

model_path = r"D:\Anjan\files\runs\segment\road_boundary_segmentation2\weights\best.pt"

Initialize the YOLO model with the provided weights

model = YOLO(model_path)

Move model to GPU if available

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
print(f"Using device: {device}")

Function to generate output path based on timestamp

def generate_output_path(input_path, output_folder):
timestamp = int(time.time()) # Unique timestamp
# Check if the input is an image or video and append accordingly
if input_path.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
return os.path.join(output_folder, f"output_video_{timestamp}.mp4")
else:
return os.path.join(output_folder, f"output_image_{timestamp}.jpg")

Set your input path directly here (no need to input during runtime)

input_path = r"D:\Anjan\Testinvideosday\Testing Files\8.mp4" # <-- Specify the file here (image or video)

Output folder (can be changed or left empty for current directory)

output_folder = r"D:\Anjan\files\bestoutputs" # <-- Specify your output folder path here or leave it empty for default

If output folder doesn't exist, create it

os.makedirs(output_folder, exist_ok=True)

Checking if input is a video

if input_path.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
is_video = True
cap = cv2.VideoCapture(input_path)

# Video properties  
fps = int(cap.get(cv2.CAP_PROP_FPS))  
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))  
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  

# Generate output video path with timestamp  
output_path = generate_output_path(input_path, output_folder)  
fourcc = cv2.VideoWriter_fourcc(*"mp4v")  
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))  

# Process video  
while cap.isOpened():  
    ret, frame = cap.read()  
    if not ret:  
        break  

    # Run inference with proper image size (multiple of 32)  
    results = model.predict(frame, conf=0.35, imgsz=1408, verbose=False)  # Use imgsz=640 for efficiency  

    if results[0].masks is not None:  
        mask = results[0].masks.data[0].cpu().numpy()  
        mask = (mask * 255).astype(np.uint8)  
        mask = cv2.resize(mask, (width, height))  

        # Create transparent blue overlay  
        overlay = frame.copy()  
        overlay[mask > 128] = [255, 0, 0]  # Blue fill  

        # Blend overlay with original frame (Transparency: 0.3)  
        frame = cv2.addWeighted(overlay, 0.1, frame, 0.7, 0)  

        # Bold red boundary edges  
        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  
        cv2.drawContours(frame, contours, -1, (0, 0, 255), 2)  # Red boundary  

    out.write(frame)  

cap.release()  
out.release()  
print(f"Processed video saved at: {output_path}")  

If the input is an image

else:
is_video = False
frame = cv2.imread(input_path)
height, width, _ = frame.shape

# Run inference with a smaller image size (640 is typically more efficient)  
results = model.predict(frame, conf=0.3, imgsz=1408, verbose=False)  

if results[0].masks is not None:  
    mask = results[0].masks.data[0].cpu().numpy()  
    mask = (mask * 255).astype(np.uint8)  
    mask = cv2.resize(mask, (width, height))  

    # Create transparent blue overlay  
    overlay = frame.copy()  
    overlay[mask > 128] = [255, 0, 0]  # Blue fill  

    # Blend overlay with original frame (Transparency: 0.1)  
    frame = cv2.addWeighted(overlay, 0.1, frame, 0.7, 0)  

    # Bold red boundary edges  
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  
    cv2.drawContours(frame, contours, -1, (0, 0, 255), 2)  # Red boundary  

# Generate output path with timestamp  
output_path = generate_output_path(input_path, output_folder)  
cv2.imwrite(output_path, frame)  
print(f"Processed image saved at: {output_path}") any chance u can execute this using as a backend application and the confidence UI is equivalent to conf=0.35 this part in python code so can u execute this?