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DeclanMcrory / MusicVidGen

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Sweep #6

Open DeclanMcrory opened 2 hours ago

DeclanMcrory commented 2 hours ago

@sweepai task involves creating a pull request to push changes for a project that includes a Flask web application for uploading music files, a machine learning model for generating music videos, and the necessary training scripts. The solution involves setting up the required files and ensuring they work together seamlessly.

How to implement

File: requirements.txt

We need to list all the Python packages required to run the project. This includes libraries for machine learning, data processing, and web development.

numpy==1.21.2
pandas==1.3.3
scikit-learn==0.24.2
tensorflow==2.6.0  # or torch==1.9.0 if using PyTorch
opencv-python==4.5.3.56
flask==2.0.1
flask-cors==3.0.10
matplotlib==3.4.3
requests==2.26.0
gunicorn==20.1.0

File: app.py

We will create a Flask web application to handle file uploads and integrate with the model to generate music videos.

from flask import Flask, request, render_template, redirect, url_for
import os

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads/'

os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        return redirect(request.url)
    file = request.files['file']
    if file.filename == '':
        return redirect(request.url)
    if file:
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
        file.save(filepath)
        # Here you would call your model to process the file
        # For example: result = generate_music_video(filepath)
        return redirect(url_for('result', filename=file.filename))

@app.route('/result/<filename>')
def result(filename):
    # Here you would retrieve and display the result
    # For example: result = get_result_for_file(filename)
    return render_template('result.html', filename=filename)

if __name__ == '__main__':
    app.run(debug=True)

Templates

Create a folder named templates in the same directory as app.py and add the following HTML files:

index.html

<!doctype html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Music Video Generator</title>
</head>
<body>
    <h1>Upload a Music File</h1>
    <form action="{{ url_for('upload_file') }}" method="post" enctype="multipart/form-data">
        <input type="file" name="file">
        <input type="submit" value="Upload">
    </form>
</body>
</html>

result.html

<!doctype html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Music Video Result</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 0;
            padding: 0;
            display: flex;
            flex-direction: column;
            align-items: center;
            justify-content: center;
            height: 100vh;
            background-color: #f0f0f0;
        }
        h1 {
            color: #333;
        }
        .container {
            text-align: center;
            background: white;
            padding: 20px;
            border-radius: 8px;
            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
        }
        .video-container {
            margin-top: 20px;
        }
        .download-link {
            margin-top: 20px;
            display: inline-block;
            padding: 10px 20px;
            background-color: #007bff;
            color: white;
            text-decoration: none;
            border-radius: 5px;
        }
        .download-link:hover {
            background-color: #0056b3;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Generated Music Video for {{ filename }}</h1>
        <div class="video-container">
            <!-- Assuming the video is saved in a static folder and can be accessed via a URL -->
            <video width="600" controls>
                <source src="{{ url_for('static', filename='videos/' + filename) }}" type="video/mp4">
                Your browser does not support the video tag.
            </video>
        </div>
        <a href="{{ url_for('static', filename='videos/' + filename) }}" class="download-link" download>Download Video</a>
    </div>
</body>
</html>

File: train.py

We will implement the training logic for the Music Video Generator model.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
import os
from model import MusicVideoGeneratorModel  # Assuming the model is defined in model.py

class MusicVideoDataset(Dataset):
    def __init__(self, data_dir):
        self.data_dir = data_dir
        self.data = self.load_data()

    def load_data(self):
        data = []
        for file in os.listdir(self.data_dir):
            if file.endswith('.npy'):
                data.append(np.load(os.path.join(self.data_dir, file)))
        return data

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

    def __getitem__(self, idx):
        return self.data[idx]

def get_data_loaders(data_dir, batch_size=32, shuffle=True):
    dataset = MusicVideoDataset(data_dir)
    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
    return data_loader

def train_model(model, data_loader, criterion, optimizer, num_epochs=25):
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        for inputs in data_loader:
            inputs = inputs.to(device)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, inputs)
            loss.backward()
            optimizer.step()
            running_loss += loss.item() * inputs.size(0)

        epoch_loss = running_loss / len(data_loader.dataset)
        print(f'Epoch {epoch}/{num_epochs - 1}, Loss: {epoch_loss:.4f}')
    return model

def evaluate_model(model, data_loader, criterion):
    model.eval()
    running_loss = 0.0
    with torch.no_grad():
        for inputs in data_loader:
            inputs = inputs.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, inputs)
            running_loss += loss.item() * inputs.size(0)

    total_loss = running_loss / len(data_loader.dataset)
    print(f'Evaluation Loss: {total_loss:.4f}')
    return total_loss

def save_model(model, path='music_video_generator.pth'):
    torch.save(model.state_dict(), path)

if __name__ == "__main__":
    data_dir = 'path/to/data'
    batch_size = 32
    num_epochs = 25
    learning_rate = 0.001

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    model = MusicVideoGeneratorModel().to(device)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    data_loader = get_data_loaders(data_dir, batch_size)

    trained_model = train_model(model, data_loader, criterion, optimizer, num_epochs)
    evaluate_model(trained_model, data_loader, criterion)
    save_model(trained_model)

File: model.py

We will define the architecture of the Music Video Generator model.

import torch
import torch.nn as nn
import torch.nn.functional as F

class MusicVideoGeneratorModel(nn.Module):
    def __init__(self, audio_length, video_frame_size):
        super(MusicVideoGeneratorModel, self).__init__()

        self.audio_conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.audio_conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.audio_fc = nn.Linear(32 * audio_length, 128)  # Assuming audio_length is the length of the audio sequence

        self.video_fc1 = nn.Linear(128, 256)
        self.video_fc2 = nn.Linear(256, 512)
        self.video_fc3 = nn.Linear(512, video_frame_size)  # Assuming video_frame_size is the size of the video frame

    def forward(self, audio_input):
        x = F.relu(self.audio_conv1(audio_input))
        x = F.relu(self.audio_conv2(x))
        x = x.view(x.size(0), -1)  # Flatten the tensor
        x = F.relu(self.audio_fc(x))

        x = F.relu(self.video_fc1(x))
        x = F.relu(self.video_fc2(x))
        video_output = torch.sigmoid(self.video_fc3(x))  # Assuming output is in the range [0, 1]

        return video_output

# Example usage
if __name__ == "__main__":
    audio_length = 100  # Example length of the audio sequence
    video_frame_size = 1024  # Example size of the video frame
    model = MusicVideoGeneratorModel(audio_length, video_frame_size)
    audio_input = torch.randn(1, 1, audio_length)  # Example input
    video_output = model(audio_input)
    print(video_output)

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on templates/index.html To create the HTML form for file uploads in `templates/index.html`, follow these steps: 1. **Create the `templates` Directory**: Ensure that you have a directory named `templates` in the same directory as `app.py`. 2. **Create `index.html`**: Inside the `templates` directory, create a file named `index.html`. 3. **Add HTML Content**: Add the necessary HTML content to create a form for file uploads. Here is the content for `index.html`: ```html Music Video Generator

Upload a Music File

``` ### Explanation: - **DOCTYPE and HTML Structure**: The `` declaration defines the document type and version of HTML. - **Head Section**: Contains meta tags for character set and viewport settings, and a title for the page. - **Body Section**: Contains a heading and a form. - **Form**: The form uses the `POST` method and `multipart/form-data` encoding type to handle file uploads. - **File Input**: An input field of type `file` for selecting the file to upload. - **Submit Button**: A button to submit the form. This `index.html` file will provide a simple interface for users to upload music files, which will then be processed by the Flask application defined in `app.py`.
Changes on requirements.txt To complete the `requirements.txt` file, you need to list all the Python packages required to run the project. Based on the tasks for the other files, the project involves machine learning model training, web application development, and possibly data processing. Here is a practical proposal for the `requirements.txt` file: 1. **Machine Learning and Data Processing Libraries**: - `numpy`: For numerical operations. - `pandas`: For data manipulation and analysis. - `scikit-learn`: For machine learning utilities. - `tensorflow` or `pytorch`: Depending on which framework you are using for the model. - `opencv-python`: For video processing. 2. **Web Development Libraries**: - `flask`: For creating the web interface. - `flask-cors`: For handling Cross-Origin Resource Sharing (CORS) if your frontend and backend are on different servers. 3. **Miscellaneous**: - `matplotlib`: For plotting and visualization. - `requests`: For making HTTP requests if needed. - `gunicorn`: For deploying the Flask app. Here is a sample `requirements.txt` file: ```plaintext numpy==1.21.2 pandas==1.3.3 scikit-learn==0.24.2 tensorflow==2.6.0 # or torch==1.9.0 if using PyTorch opencv-python==4.5.3.56 flask==2.0.1 flask-cors==3.0.10 matplotlib==3.4.3 requests==2.26.0 gunicorn==20.1.0 ``` Make sure to adjust the versions according to your project's compatibility and requirements. If you are using additional libraries specific to your project, include them as well.
Changes on templates/result.html To create the `result.html` file, we need to design an HTML page that will display the results of the music video generation. This page will be rendered after the user uploads a file and the model processes it. The page should ideally show some information about the generated video and provide a way to view or download it. Here is a basic implementation of `result.html`: ```html Music Video Result

Generated Music Video for {{ filename }}

Download Video
``` ### Explanation: 1. **HTML Structure**: The HTML structure includes a header, a video container, and a download link. 2. **CSS Styling**: Basic CSS styling is added to center the content and make it visually appealing. 3. **Video Display**: The video is displayed using the `
Changes on model.py Since `model.py` is a new file, you need to implement the neural network architecture for processing audio and generating video frames. Below is a concrete implementation plan for this file: ### Step-by-Step Implementation 1. **Import Necessary Libraries**: Import libraries such as PyTorch for building the model. 2. **Define the Model Class**: Create a class for the model that inherits from `nn.Module`. 3. **Initialize Layers**: Define the layers required for the model in the `__init__` method. 4. **Forward Pass**: Implement the forward pass method to define how data flows through the model. ### Implementation ```python import torch import torch.nn as nn import torch.nn.functional as F class MusicVideoGeneratorModel(nn.Module): def __init__(self, audio_length, video_frame_size): super(MusicVideoGeneratorModel, self).__init__() # Example layers for audio processing self.audio_conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1) self.audio_conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1) self.audio_fc = nn.Linear(32 * audio_length, 128) # Assuming audio_length is the length of the audio sequence # Example layers for video frame generation self.video_fc1 = nn.Linear(128, 256) self.video_fc2 = nn.Linear(256, 512) self.video_fc3 = nn.Linear(512, video_frame_size) # Assuming video_frame_size is the size of the video frame def forward(self, audio_input): # Audio processing x = F.relu(self.audio_conv1(audio_input)) x = F.relu(self.audio_conv2(x)) x = x.view(x.size(0), -1) # Flatten the tensor x = F.relu(self.audio_fc(x)) # Video frame generation x = F.relu(self.video_fc1(x)) x = F.relu(self.video_fc2(x)) video_output = torch.sigmoid(self.video_fc3(x)) # Assuming output is in the range [0, 1] return video_output # Example usage if __name__ == "__main__": audio_length = 100 # Example length of the audio sequence video_frame_size = 1024 # Example size of the video frame model = MusicVideoGeneratorModel(audio_length, video_frame_size) audio_input = torch.randn(1, 1, audio_length) # Example input video_output = model(audio_input) print(video_output) ``` ### Explanation 1. **Import Libraries**: The necessary libraries (`torch`, `torch.nn`, and `torch.nn.functional`) are imported. 2. **Define Model Class**: The `MusicVideoGeneratorModel` class is defined, inheriting from `nn.Module`. 3. **Initialize Layers**: The `__init__` method initializes the layers for audio processing and video frame generation. 4. **Forward Pass**: The `forward` method defines the data flow through the model, processing the audio input and generating the video output. This implementation provides a basic structure for the neural network model. You may need to adjust the layer dimensions and types according to your actual data and model design.
Changes on app.py To implement the Flask web application in `app.py`, follow these steps: 1. **Set Up Flask**: Install Flask if you haven't already by adding it to your `requirements.txt` and running `pip install -r requirements.txt`. 2. **Initialize Flask App**: Create a basic Flask application. 3. **Define Routes**: Create routes for the main page, file upload, and any other necessary endpoints. 4. **Create Templates**: Use HTML templates to render the web pages. 5. **Handle File Uploads**: Implement logic to handle file uploads and process them. 6. **Integrate Model**: Connect the web interface with the model to generate music videos. Here's a basic implementation of `app.py`: ```python from flask import Flask, request, render_template, redirect, url_for import os app = Flask(__name__) app.config['UPLOAD_FOLDER'] = 'uploads/' # Ensure the upload folder exists os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) @app.route('/') def index(): return render_template('index.html') @app.route('/upload', methods=['POST']) def upload_file(): if 'file' not in request.files: return redirect(request.url) file = request.files['file'] if file.filename == '': return redirect(request.url) if file: filepath = os.path.join(app.config[' task involves creating a pull request to push changes for a project that includes a Flask web application for uploading music files, a machine learning model for generating music videos, and the necessary training scripts. The solution involves setting up the required files and ensuring they work together seamlessly. # How to implement ## File: requirements.txt We need to list all the Python packages required to run the project. This includes libraries for machine learning, data processing, and web development. ```plaintext numpy==1.21.2 pandas==1.3.3 scikit-learn==0.24.2 tensorflow==2.6.0 # or torch==1.9.0 if using PyTorch opencv-python==4.5.3.56 flask==2.0.1 flask-cors==3.0.10 matplotlib==3.4.3 requests==2.26.0 gunicorn==20.1.0 ``` ## File: app.py We will create a Flask web application to handle file uploads and integrate with the model to generate music videos. ```python from flask import Flask, request, render_template, redirect, url_for import os app = Flask(__name__) app.config['UPLOAD_FOLDER'] = 'uploads/' os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) @app.route('/') def index(): return render_template('index.html') @app.route('/upload', methods=['POST']) def upload_file(): if 'file' not in request.files: return redirect(request.url) file = request.files['file'] if file.filename == '': return redirect(request.url) if file: filepath = os.path.join(app.config['UPLOAD_FOLDER'], file.filename) file.save(filepath) # Here you would call your model to process the file # For example: result = generate_music_video(filepath) return redirect(url_for('result', filename=file.filename)) @app.route('/result/') def result(filename): # Here you would retrieve and display the result # For example: result = get_result_for_file(filename) return render_template('result.html', filename=filename) if __name__ == '__main__': app.run(debug=True) ``` ### Templates Create a folder named `templates` in the same directory as `app.py` and add the following HTML files: #### `index.html` ```html Music Video Generator

Upload a Music File

``` #### `result.html` ```html Music Video Result

Generated Music Video for {{ filename }}

Download Video
``` ## File: train.py We will implement the training logic for the Music Video Generator model. ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset import numpy as np import os from model import MusicVideoGeneratorModel # Assuming the model is defined in model.py class MusicVideoDataset(Dataset): def __init__(self, data_dir): self.data_dir = data_dir self.data = self.load_data() def load_data(self): data = [] for file in os.listdir(self.data_dir): if file.endswith('.npy'): data.append(np.load(os.path.join(self.data_dir, file))) return data def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] def get_data_loaders(data_dir, batch_size=32, shuffle=True): dataset = MusicVideoDataset(data_dir) data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle) return data_loader def train_model(model, data_loader, criterion, optimizer, num_epochs=25): for epoch in range(num_epochs): model.train() running_loss = 0.0 for inputs in data_loader: inputs = inputs.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, inputs) loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) epoch_loss = running_loss / len(data_loader.dataset) print(f'Epoch {epoch}/{num_epochs - 1}, Loss: {epoch_loss:.4f}') return model def evaluate_model(model, data_loader, criterion): model.eval() running_loss = 0.0 with torch.no_grad(): for inputs in data_loader: inputs = inputs.to(device) outputs = model(inputs) loss = criterion(outputs, inputs) running_loss += loss.item() * inputs.size(0) total_loss = running_loss / len(data_loader.dataset) print(f'Evaluation Loss: {total_loss:.4f}') return total_loss def save_model(model, path='music_video_generator.pth'): torch.save(model.state_dict(), path) if __name__ == "__main__": data_dir = 'path/to/data' batch_size = 32 num_epochs = 25 learning_rate = 0.001 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = MusicVideoGeneratorModel().to(device) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) data_loader = get_data_loaders(data_dir, batch_size) trained_model = train_model(model, data_loader, criterion, optimizer, num_epochs) evaluate_model(trained_model, data_loader, criterion) save_model(trained_model) ``` ## File: model.py We will define the architecture of the Music Video Generator model. ```python import torch import torch.nn as nn import torch.nn.functional as F class MusicVideoGeneratorModel(nn.Module): def __init__(self, audio_length, video_frame_size): super(MusicVideoGeneratorModel, self).__init__() self.audio_conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1) self.audio_conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1) self.audio_fc = nn.Linear(32 * audio_length, 128) # Assuming audio_length is the length of the audio sequence self.video_fc1 = nn.Linear(128, 256) self.video_fc2 = nn.Linear(256, 512) self.video_fc3 = nn.Linear(512, video_frame_size) # Assuming video_frame_size is the size of the video frame def forward(self, audio_input): x = F.relu(self.audio_conv1(audio_input)) x = F.relu(self.audio_conv2(x)) x = x.view(x.size(0), -1) # Flatten the tensor x = F.relu(self.audio_fc(x)) x = F.relu(self.video_fc1(x)) x = F.relu(self.video_fc2(x)) video_output = torch.sigmoid(self.video_fc3(x)) # Assuming output is in the range [0, 1] return video_output # Example usage if __name__ == "__main__": audio_length = 100 # Example length of the audio sequence video_frame_size = 1024 # Example size of the video frame model = MusicVideoGeneratorModel(audio_length, video_frame_size) audio_input = torch.randn(1, 1, audio_length) # Example input video_output = model(audio_input) print(video_output) ``` [Click here to create a Pull Request with the proposed solution](https://api.codeautopilot.com/create-pr?task=32997&hash=4950a1f1a7273cfed71ab337401f8156f318ca10914839dea4ed110f81048749) **Files used for this task:**
Changes on templates/index.html To create the HTML form for file uploads in `templates/index.html`, follow these steps: 1. **Create the `templates` Directory**: Ensure that you have a directory named `templates` in the same directory as `app.py`. 2. **Create `index.html`**: Inside the `templates` directory, create a file named `index.html`. 3. **Add HTML Content**: Add the necessary HTML content to create a form for file uploads. Here is the content for `index.html`: ```html Music Video Generator

Upload a Music File

``` ### Explanation: - **DOCTYPE and HTML Structure**: The `` declaration defines the document type and version of HTML. - **Head Section**: Contains meta tags for character set and viewport settings, and a title for the page. - **Body Section**: Contains a heading and a form. - **Form**: The form uses the `POST` method and `multipart/form-data` encoding type to handle file uploads. - **File Input**: An input field of type `file` for selecting the file to upload. - **Submit Button**: A button to submit the form. This `index.html` file will provide a simple interface for users to upload music files, which will then be processed by the Flask application defined in `app.py`.
Changes on requirements.txt To complete the `requirements.txt` file, you need to list all the Python packages required to run the project. Based on the tasks for the other files, the project involves machine learning model training, web application development, and possibly data processing. Here is a practical proposal for the `requirements.txt` file: 1. **Machine Learning and Data Processing Libraries**: - `numpy`: For numerical operations. - `pandas`: For data manipulation and analysis. - `scikit-learn`: For machine learning utilities. - `tensorflow` or `pytorch`: Depending on which framework you are using for the model. - `opencv-python`: For video processing. 2. **Web Development Libraries**: - `flask`: For creating the web interface. - `flask-cors`: For handling Cross-Origin Resource Sharing (CORS) if your frontend and backend are on different servers. 3. **Miscellaneous**: - `matplotlib`: For plotting and visualization. - `requests`: For making HTTP requests if needed. - `gunicorn`: For deploying the Flask app. Here is a sample `requirements.txt` file: ```plaintext numpy==1.21.2 pandas==1.3.3 scikit-learn==0.24.2 tensorflow==2.6.0 # or torch==1.9.0 if using PyTorch opencv-python==4.5.3.56 flask==2.0.1 flask-cors==3.0.10 matplotlib==3.4.3 requests==2.26.0 gunicorn==20.1.0 ``` Make sure to adjust the versions according to your project's compatibility and requirements. If you are using additional libraries specific to your project, include them as well.
Changes on templates/result.html To create the `result.html` file, we need to design an HTML page that will display the results of the music video generation. This page will be rendered after the user uploads a file and the model processes it. The page should ideally show some information about the generated video and provide a way to view or download it. Here is a basic implementation of `result.html`: ```html Music Video Result

Generated Music Video for {{ filename }}

Download Video
``` ### Explanation: 1. **HTML Structure**: The HTML structure includes a header, a video container, and a download link. 2. **CSS Styling**: Basic CSS styling is added to center the content and make it visually appealing. 3. **Video Display**: The video is displayed using the `
Changes on model.py Since `model.py` is a new file, you need to implement the neural network architecture for processing audio and generating video frames. Below is a concrete implementation plan for this file: ### Step-by-Step Implementation 1. **Import Necessary Libraries**: Import libraries such as PyTorch for building the model. 2. **Define the Model Class**: Create a class for the model that inherits from `nn.Module`. 3. **Initialize Layers**: Define the layers required for the model in the `__init__` method. 4. **Forward Pass**: Implement the forward pass method to define how data flows through the model. ### Implementation ```python import torch import torch.nn as nn import torch.nn.functional as F class MusicVideoGeneratorModel(nn.Module): def __init__(self, audio_length, video_frame_size): super(MusicVideoGeneratorModel, self).__init__() # Example layers for audio processing self.audio_conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1) self.audio_conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1) self.audio_fc = nn.Linear(32 * audio_length, 128) # Assuming audio_length is the length of the audio sequence # Example layers for video frame generation self.video_fc1 = nn.Linear(128, 256) self.video_fc2 = nn.Linear(256, 512) self.video_fc3 = nn.Linear(512, video_frame_size) # Assuming video_frame_size is the size of the video frame def forward(self, audio_input): # Audio processing x = F.relu(self.audio_conv1(audio_input)) x = F.relu(self.audio_conv2(x)) x = x.view(x.size(0), -1) # Flatten the tensor x = F.relu(self.audio_fc(x)) # Video frame generation x = F.relu(self.video_fc1(x)) x = F.relu(self.video_fc2(x)) video_output = torch.sigmoid(self.video_fc3(x)) # Assuming output is in the range [0, 1] return video_output # Example usage if __name__ == "__main__": audio_length = 100 # Example length of the audio sequence video_frame_size = 1024 # Example size of the video frame model = MusicVideoGeneratorModel(audio_length, video_frame_size) audio_input = torch.randn(1, 1, audio_length) # Example input video_output = model(audio_input) print(video_output) ``` ### Explanation 1. **Import Libraries**: The necessary libraries (`torch`, `torch.nn`, and `torch.nn.functional`) are imported. 2. **Define Model Class**: The `MusicVideoGeneratorModel` class is defined, inheriting from `nn.Module`. 3. **Initialize Layers**: The `__init__` method initializes the layers for audio processing and video frame generation. 4. **Forward Pass**: The `forward` method defines the data flow through the model, processing the audio input and generating the video output. This implementation provides a basic structure for the neural network model. You may need to adjust the layer dimensions and types according to your actual data and model design.
Changes on app.py To implement the Flask web application in `app.py`, follow these steps: 1. **Set Up Flask**: Install Flask if you haven't already by adding it to your `requirements.txt` and running `pip install -r requirements.txt`. 2. **Initialize Flask App**: Create a basic Flask application. 3. **Define Routes**: Create routes for the main page, file upload, and any other necessary endpoints. 4. **Create Templates**: Use HTML templates to render the web pages. 5. **Handle File Uploads**: Implement logic to handle file uploads and process them. 6. **Integrate Model**: Connect the web interface with the model to generate music videos. Here's a basic implementation of `app.py`: ```python from flask import Flask, request, render_template, redirect, url_for import os app = Flask(__name__) app.config['UPLOAD_FOLDER'] = 'uploads/' # Ensure the upload folder exists os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) @app.route('/') def index(): return render_template('index.html') @app.route('/upload', methods=['POST']) def upload_file(): if 'file' not in request.files: return redirect(request.url) file = request.files['file'] if file.filename == '': return redirect(request.url) if file: filepath = os.path.join(app.config['
DeclanMcrory commented 1 hour ago

Sweep can you help me with this

codeautopilot[bot] commented 1 hour ago

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