3467dbcf03
Full-stack application for predicting where humans look in images using DeepGaze saliency models. Includes heatmap overlays, gaze sequence prediction, hotspot detection, AOI analysis, rule-based insights, optional Claude AI design analysis, and professional PDF report generation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
import os
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from pathlib import Path
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import aiofiles
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from app.config import settings
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class LocalStorage:
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def __init__(self):
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self.base_dir = Path(settings.UPLOAD_DIR)
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self.base_dir.mkdir(parents=True, exist_ok=True)
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def analysis_dir(self, analysis_id: str) -> Path:
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path = self.base_dir / analysis_id
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path.mkdir(parents=True, exist_ok=True)
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return path
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async def save_bytes(self, data: bytes, analysis_id: str, filename: str) -> str:
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dir_path = self.analysis_dir(analysis_id)
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file_path = dir_path / filename
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async with aiofiles.open(file_path, "wb") as f:
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await f.write(data)
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return str(file_path)
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async def load_bytes(self, analysis_id: str, filename: str) -> bytes:
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file_path = self.analysis_dir(analysis_id) / filename
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async with aiofiles.open(file_path, "rb") as f:
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return await f.read()
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def get_path(self, analysis_id: str, filename: str) -> Path:
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return self.analysis_dir(analysis_id) / filename
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def exists(self, analysis_id: str, filename: str) -> bool:
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return (self.analysis_dir(analysis_id) / filename).exists()
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async def delete_analysis(self, analysis_id: str) -> None:
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import shutil
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dir_path = self.base_dir / analysis_id
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if dir_path.exists():
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shutil.rmtree(dir_path)
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storage = LocalStorage()
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