template.py

import os
import shutil

def create_project_structure():
    # Define the project structure
    project_structure = {
        'neural_music_producer': {
            'video_analysis': {
                '__init__.py': '',
                'emotion_analyzer.py': '''
from transformers import AutoFeatureExtractor, AutoModelForVideoClassification
import torch
import torch.nn as nn

class VideoEmotionAnalyzer(nn.Module):
    def __init__(self):
        super().__init__()
        # Initialize emotion analysis components
        self.feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/vivit-b-16-224")
        self.model = AutoModelForVideoClassification.from_pretrained("microsoft/vivit-b-16-224")
        
    def forward(self, frames):
        # Process video frames and extract emotions
        pass
''',
                'frame_extractor.py': '''
import cv2
import numpy as np
from moviepy.editor import VideoFileClip

def extract_frames(video_path, fps=5):
    """Extract frames from video at specified fps"""
    video = VideoFileClip(video_path)
    frames = []
    for t in np.arange(0, video.duration, 1/fps):
        frame = video.get_frame(t)
        frames.append(frame)
    return np.array(frames)
'''
            },
            'music_generation': {
                '__init__.py': '',
                'generator.py': '''
import torch
import torch.nn as nn

class MusicGenerator(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden_size = 512
        # Initialize music generation components
        
    def forward(self, emotion_embedding):
        # Generate music based on emotion embedding
        pass
''',
                'audio_synthesis.py': '''
import librosa
import numpy as np
import soundfile as sf

class AudioSynthesizer:
    def __init__(self):
        self.sample_rate = 44100
        
    def synthesize(self, music_sequence):
        # Convert music sequence to audio
        pass
'''
            },
            'utils': {
                '__init__.py': '',
                'preprocessing.py': '''
import torch
import numpy as np

def normalize_video_frames(frames):
    """Normalize video frames for model input"""
    return frames / 255.0

def prepare_audio_features(audio):
    """Prepare audio features for synthesis"""
    pass
'''
            },
            'ui': {
                '__init__.py': '',
                'streamlit_app.py': '''
import streamlit as st
from neural_music_producer.video_analysis.emotion_analyzer import VideoEmotionAnalyzer
from neural_music_producer.music_generation.generator import MusicGenerator

def main():
    st.title("Neural Music Producer")
    
    # File uploader
    video_file = st.file_uploader("Upload your video", type=['mp4', 'mov'])
    
    if video_file:
        # Process video and generate music
        pass

if __name__ == "__main__":
    main()
'''
            },
            '__init__.py': ''
        }
    }

    def create_directories_and_files(base_path, structure):
        for name, content in structure.items():
            path = os.path.join(base_path, name)
            
            if isinstance(content, dict):
                # If it's a dictionary, it's a directory
                os.makedirs(path, exist_ok=True)
                create_directories_and_files(path, content)
            else:
                # If it's not a dictionary, it's a file
                with open(path, 'w') as f:
                    f.write(content)

    # Create the project structure
    base_dir = os.getcwd()
    project_dir = os.path.join(base_dir, 'neural_music_producer_project')
    
    # Remove existing directory if it exists
    if os.path.exists(project_dir):
        shutil.rmtree(project_dir)
    
    # Create new project structure
    os.makedirs(project_dir)
    
    # Create setup.py
    setup_py_content = '''
from setuptools import setup, find_packages

setup(
    name="neural_music_producer",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "torch>=2.0.0",
        "torchvision>=0.15.0",
        "transformers>=4.30.0",
        "librosa>=0.10.0",
        "moviepy>=1.0.3",
        "numpy>=1.24.0",
        "streamlit>=1.25.0",
        "opencv-python>=4.8.0",
        "tqdm>=4.65.0",
        "soundfile>=0.12.1",
        "python-dotenv>=1.0.0",
    ],
    author="Your Name",
    author_email="your.email@example.com",
    description="An AI system that generates custom music for videos",
    keywords="ai, music generation, computer vision, deep learning",
    python_requires=">=3.8",
)
'''
    
    with open(os.path.join(project_dir, 'setup.py'), 'w') as f:
        f.write(setup_py_content)
    
    # Create project structure
    create_directories_and_files(project_dir, project_structure)
    
    print(f"Project structure created at: {project_dir}")

if __name__ == "__main__":
    create_project_structure()