Sweep setup

[DeclanMcrory]

@sweep can you set up this directory structure and add these files.

Directory Structure

- mainFolder
  |-- main.py
  |-- train.py
  |-- config.json
  |-- web_interface
  |     |-- index.html
  |     |-- styles.css
  |-- utils
  |     |-- random_generator.py
  |-- lyrics_generator
  |     |-- lyrics_generator.py
  |-- models
  |     |-- __init__.py
  |     |-- music_vid_gen.py
  |-- data
        |-- __init__.py
        |-- music_vid_synth_dataset.py

1. web_interface/styles.css

/* Styles for the web interface */

body {
    font-family: Arial, sans-serif;
}

h1 {
    text-align: center;
}

form {
    text-align: center;
    margin-top: 50px;
}

button {
    padding: 10px 20px;
    background-color: #337ab7;
    color: #fff;
    border: none;
    cursor: pointer;
}

#output {
    text-align: center;
    margin-top: 50px;
}

/* Responsive layout */
@media only screen and (max-width: 600px) {
    form {
        margin-top: 20px;
    }

    button {
        padding: 8px 16px;
        font-size: 14px;
    }
}

2. train.py

# Training code

import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from data.music_vid_synth_dataset import MusicVidSynthDataset
from models.music_vid_gen import MusicVidGen
import json

def load_config(path):
    with open(path, 'r') as f:
        return json.load(f)

def train(model, dataset, config):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

    for epoch in range(10):
        for batch in dataloader:
            audio_features, character_profile, video_element, gt_video = batch
            audio_features = audio_features.to(device)
            character_profile = character_profile.to(device)
            video_element = video_element.to(device)
            gt_video = gt_video.to(device)

            # Forward pass
            output = model(audio_features, character_profile, video_element)
            loss = F.mse_loss(output, gt_video)

            # Backward pass
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            print(f"Epoch {epoch+1}, Loss: {loss.item()}")

if __name__ == "__main__":
    config = load_config("config.json")
    dataset = MusicVidSynthDataset(config)
    model = MusicVidGen(config)
    train(model, dataset, config)

3. models/music_vid_gen.py

import torch.nn as nn

class MusicVidGen(nn.Module):
    def __init__(self, config):
        super(MusicVidGen, self).__init__()
        self.audio_feature_extractor = nn.Sequential(
            nn.Linear(config['audio_input_dim'], 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU()
        )
        self.character_profile_processor = nn.Sequential(
            nn.Linear(config['character_input_dim'], 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU()
        )
        self.video_element_processor = nn.Sequential(
            nn.Linear(config['video_element_input_dim'], 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU()
        )
        self.video_generator = nn.Sequential(
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, config['output_dim']),
            nn.Sigmoid()
        )

    def forward(self, audio_features, character_profile, video_element):
        audio_processed = self.audio_feature_extractor(audio_features)
        character_processed = self.character_profile_processor(character_profile)
        video_element_processed = self.video_element_processor(video_element)
        combined = torch.cat((audio_processed, character_processed, video_element_processed), dim=1)
        video_output = self.video_generator(combined)
        return video_output

4. data/music_vid_synth_dataset.py

import torch
from torch.utils.data import Dataset

class MusicVidSynthDataset(Dataset):
    def __init__(self, config):
        self.audio_features = torch.randn((config['num_samples'], config['audio_input_dim']))
        self.character_profiles = torch.randn((config['num_samples'], config['character_input_dim']))
        self.video_elements = torch.randn((config['num_samples'], config['video_element_input_dim']))
        self.gt_videos = torch.randn((config['num_samples'], config['output_dim']))

    def __len__(self):
        return len(self.audio_features)

    def __getitem__(self, idx):
        audio_features = self.audio_features[idx]
        character_profile = self.character_profiles[idx]
        video_element = self.video_elements[idx]
        gt_video = self.gt_videos[idx]
        return audio_features, character_profile, video_element, gt_video

5. config.json

{
    "num_samples": 1000,
    "audio_input_dim": 128,
    "character_input_dim": 64,
    "video_element_input_dim": 64,
    "output_dim": 256
}

6. web_interface/index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <link rel="stylesheet" href="styles.css">
    <title>Music Video Generator</title>
</head>
<body>
    <h1>Generate Your Music Video</h1>
    <form id="music-video-form">
        <button type="submit">Generate</button>
    </form>
    <div id="output"></div>

    <script>
        document.getElementById('music-video-form').addEventListener('submit', function(event) {
            event.preventDefault();
            const output = document.getElementById('output');
            output.innerHTML = 'Generating...';
            // Simulate the generation process
            setTimeout(() => {
                output.innerHTML = 'Music Video Generated!';
            }, 3000);
        });
    </script>
</body>
</html>

7. utils/random_generator.py

import random

def generate_random_lyrics():
    lyrics = [
        "We're no strangers to love",
        "You know the rules and so do I",
        "A full commitment's what I'm thinking of",
        "You wouldn't get this from any other guy"
    ]
    return random.choice(lyrics)

8. lyrics_generator/lyrics_generator.py

from utils.random_generator.py import generate_random_lyrics

def get_lyrics():
    return generate_random_lyrics()

9. main.py

from lyrics_generator.lyrics_generator import get_lyrics

def main():
    lyrics = get_lyrics()
    print(f"Generated Lyrics: {lyrics}")

if __name__ == "__main__":
    main()

10. __init__.py

In both models and data directories, create an empty __init__.py file:

# This file is intentionally left blank to make this directory a Python package.

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