import torch
from torchvision import transforms
from torchvision.models import inception_v3
from torch.nn import functional as F
from torch.autograd import Variable
from PIL import Image
import moviepy.editor as mp

# Load the pre-trained Inception v3 model
inception = inception_v3(pretrained=True)
inception.eval()

# Define the image transformation pipeline
preprocess = transforms.Compose([
    transforms.Resize((299, 299)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Function to generate captions for a single frame
def generate_caption(frame):
    # Preprocess the frame
    frame = Image.fromarray(frame)
    frame = preprocess(frame)
    frame = Variable(frame.unsqueeze(0))

    # Forward pass through the Inception v3 model
    if torch.cuda.is_available():
        frame = frame.cuda()
        inception.cuda()
    features = inception(frame)
    
    # Generate the caption
    # Replace this with your own captioning model or logic
    caption = "This is a sample caption for the frame."

    return caption

# Function to process the video and generate captions
def process_video(video_path):
    # Load the video file
    video = mp.VideoFileClip(video_path)

    # Iterate over each frame
    captions = []
    for frame in video.iter_frames():
        # Generate caption for the frame
        caption = generate_caption(frame)
        captions.append(caption)

    # Print the captions
    for caption in captions:
        print(caption)

# Example usage
video_path = "path/to/video.mp4"
process_video(video_path)
