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a63cedf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | from pydantic import BaseModel
from typing import List, Optional
class HealthCheckResponse(BaseModel):
status: str
yolo_available: bool
class Photo(BaseModel):
id: str
filename: str
creation_date: str # ISO date string YYYY-MM-DD
uploaded_at: str
analysis: Optional[object] = None # Can be List[dict] (legacy) or dict (new with comparison)
analysis_date: Optional[str] = None
local_content: Optional[str] = None # Base64 data for client-side storage
# Response model for the timeline: a list of either Photo (single) or VirtualDirectory (group)
# In Pydantic V2 we might use Union, but for simplicity/JSON serialization,
# we can return a list of objects that have a 'type' field.
class TimelineItem(BaseModel):
type: str # 'photo' or 'directory'
date: str
data: Optional[Photo] = None # If type is photo
items: Optional[List[Photo]] = None # If type is directory
from app.config import INTERPRETER_MARGIN_THRESHOLD
class SinglePhotoAnalysisRequest(BaseModel):
# Default labels for zero-shot classification from centralized config
candidate_labels: Optional[List[str]] = None
model: Optional[str] = "medsiglip" # "medsiglip" only now
base64_image: Optional[str] = None # Client-side image data
margin_threshold: Optional[float] = INTERPRETER_MARGIN_THRESHOLD
class SinglePhotoAnalysisResponse(BaseModel):
photo_id: str
predictions: List[dict]
primary_model_name: Optional[str] = None
analysis_date: Optional[str] = None
prepared_image_base64: Optional[str] = None
saliency_base64: Optional[str] = None # Returning saliency as base64
interpretation: Optional[dict] = None
preprocess_strategy: Optional[dict] = None
execution_times: Optional[dict] = None
class SaliencyRequest(BaseModel):
base64_image: str
target_label: str
class SaliencyResponse(BaseModel):
photo_id: str
saliency_base64: str
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