# Quick Start Guide

This guide will help you get started with Mood Detector in just a few minutes.

# Installation

# Option 1: Python Package

pip install mood-detector

# Option 2: Docker (Recommended for non-Python developers)

docker run -p 8000:8000 usr-wwelsh/mood-detector

# Option 3: Local Development

git clone https://github.com/usr-wwelsh/mood-detector
cd mood-detector
pip install -e .

# Quick Usage Examples

# Python Usage

from mood_detector import analyze_audio

# Analyze an audio file
result = analyze_audio("song.mp3")
print(f"Mood: {result.mood}")
print(f"Energy: {result.energy}")
print(f"Tempo: {result.tempo} BPM")

# API Usage

curl -X POST http://localhost:8000/analyze \
  -F "file=@song.mp3"

# CLI Usage

mood analyze song.mp3

# First Analysis

# If using Python:

1. Install the package: pip install mood-detector
2. Run Python:

   from mood_detector import analyze_audio
   
   result = analyze_audio("your-song.mp3")
   print(f"Mood: {result.mood}")
   

# If using Docker API:

1. Start the server: docker run -p 8000:8000 usr-wwelsh/mood-detector
2. In another terminal, run: curl -X POST http://localhost:8000/analyze -F "file=@song.mp3"

# If using CLI:

1. Install the package: pip install mood-detector
2. Run: mood analyze song.mp3

# Supported Audio Formats

• MP3
• WAV
• FLAC
• M4A
• AAC
• OGG

# Troubleshooting

# Common Issues

“File not found” error:

• Make sure the file path is correct and the file exists

“Unsupported file format” error:

• Check that your file is in one of the supported formats

“Docker container won’t start”:

• Ensure Docker is running on your machine
• Check that port 8000 is not already in use

# Performance Notes

• Large audio files may take longer to process
• The first analysis may be slower as the system loads necessary components
• For best results, use audio files up to 5 minutes in length