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"""
Export Utilities for EmotionMirror Application
This module provides functions for exporting analysis data to various formats,
including CSV, JSON, and visual reports.
"""
import os
import csv
import json
import logging
from typing import Dict, List, Any, Optional
from datetime import datetime
import pandas as pd
import base64
from io import BytesIO, StringIO
# Configure logging
logger = logging.getLogger(__name__)
def export_to_json(data: Dict[str, Any], filepath: Optional[str] = None) -> str:
"""
Export data to JSON format.
Args:
data: Data to export
filepath: Optional path to save the JSON file
Returns:
JSON string if filepath is None, else filepath where saved
"""
try:
json_str = json.dumps(data, indent=4)
if filepath:
with open(filepath, 'w', encoding='utf-8') as f:
f.write(json_str)
logger.info(f"Data exported to JSON file: {filepath}")
return filepath
return json_str
except Exception as e:
logger.error(f"Error exporting data to JSON: {e}")
raise
def export_to_csv(data: Dict[str, Any], filepath: Optional[str] = None) -> str:
"""
Export analysis data to CSV format.
Args:
data: Analysis data to export
filepath: Optional path to save the CSV file
Returns:
CSV string if filepath is None, else filepath where saved
"""
try:
# Flatten the data structure for CSV format
rows = []
for analysis in data.get('analyses', []):
base_row = {
'analysis_id': analysis.get('id'),
'session_id': analysis.get('session_id'),
'timestamp': analysis.get('timestamp'),
'image_path': analysis.get('image_path'),
'face_count': analysis.get('face_count')
}
# Add tags if present
if analysis.get('tags'):
base_row['tags'] = ','.join(analysis['tags'])
# Add face-specific data
for i, face in enumerate(analysis.get('faces', [])):
row = base_row.copy()
row['face_index'] = i
row['emotion'] = face.get('emotion')
row['confidence'] = face.get('confidence')
# Add feature values
for feature, value in face.get('features', {}).items():
row[f'feature_{feature}'] = value
# Add emotion values
for emotion, value in face.get('emotions', {}).items():
row[f'emotion_{emotion}'] = value
rows.append(row)
if not rows:
logger.warning("No data to export to CSV")
return "" if filepath is None else filepath
# Create a DataFrame and export to CSV
df = pd.DataFrame(rows)
if filepath:
df.to_csv(filepath, index=False)
logger.info(f"Data exported to CSV file: {filepath}")
return filepath
# Return CSV string if no filepath provided
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
return csv_buffer.getvalue()
except Exception as e:
logger.error(f"Error exporting data to CSV: {e}")
raise
def get_download_link(content: str, filename: str, mimetype: str) -> str:
"""
Generate a download link for the content.
Args:
content: String content to download
filename: Name for the downloaded file
mimetype: MIME type of the file
Returns:
HTML download link
"""
try:
b64 = base64.b64encode(content.encode()).decode()
href = f'data:{mimetype};base64,{b64}'
return f'<a href="{href}" download="{filename}" class="download-button">Download {filename}</a>'
except Exception as e:
logger.error(f"Error creating download link: {e}")
return ""
def generate_emotion_summary(data: Dict[str, Any]) -> Dict[str, Any]:
"""
Generate a summary of emotion data across multiple analyses.
Args:
data: Dictionary of analysis data
Returns:
Dictionary with summarized emotion data
"""
try:
# Initialize counters
emotion_counts = {}
total_faces = 0
emotion_confidence = {}
# Process each analysis
for analysis in data.get('analyses', []):
for face in analysis.get('faces', []):
# Count primary emotions
emotion = face.get('emotion')
if emotion:
emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1
# Track confidence values
if emotion not in emotion_confidence:
emotion_confidence[emotion] = []
emotion_confidence[emotion].append(face.get('confidence', 0))
total_faces += 1
# Calculate statistics
if total_faces > 0:
# Calculate percentages
emotion_percentages = {
emotion: (count / total_faces) * 100
for emotion, count in emotion_counts.items()
}
# Calculate average confidence per emotion
avg_confidence = {
emotion: sum(values) / len(values)
for emotion, values in emotion_confidence.items() if values
}
return {
'total_analyses': len(data.get('analyses', [])),
'total_faces': total_faces,
'emotion_counts': emotion_counts,
'emotion_percentages': emotion_percentages,
'avg_confidence': avg_confidence
}
return {
'total_analyses': len(data.get('analyses', [])),
'total_faces': 0,
'emotion_counts': {},
'emotion_percentages': {},
'avg_confidence': {}
}
except Exception as e:
logger.error(f"Error generating emotion summary: {e}")
return {
'error': str(e),
'total_analyses': 0,
'total_faces': 0
}
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