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3469608 | 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 | """Central configuration for the DR Clinical Report Generator.
Settings for the reused diabetic-retinopathy grading model, the training-matched
image preprocessing, and the Gemini report-generation model.
"""
from pathlib import Path
import os
from dotenv import load_dotenv
load_dotenv()
PROJECT_ROOT = Path(__file__).resolve().parent.parent
# --- DR grading model (reused from the trained DR Grading project) -------
# The 430 MB weights live in the public HuggingFace Space and are downloaded
# automatically on first run (no token needed).
DR_WEIGHTS_REPO = "DRG-Group-34/Diabetic_Retinopathy_Grading"
DR_WEIGHTS_REPO_TYPE = "space"
DR_WEIGHTS_FILE = "model_epoch_011_qwk_0.8121.pth"
EFFICIENTNET_NAME = "efficientnet_b4.ra2_in1k"
SWIN_NAME = "swin_base_patch4_window12_384.ms_in22k_ft_in1k"
NUM_CLASSES = 5
FUSION_HIDDEN_DIM = 1024
FUSION_DROPOUT = 0.3
IMAGE_SIZE = 384
NORM_MEAN = [0.485, 0.456, 0.406]
NORM_STD = [0.229, 0.224, 0.225]
CLASS_NAMES = {
0: "No DR",
1: "Mild DR",
2: "Moderate DR",
3: "Severe DR",
4: "Proliferative DR",
}
# --- Preprocessing (matches training: crop -> pad -> resize -> CLAHE) ----
CROP_MODE = "auto" # "auto" applies the fundus crop; "none" skips it
CROP_THRESHOLD = 15
CROP_MARGIN = 0.02
CLAHE_CLIP_LIMIT = 2.0
CLAHE_TILE_GRID = (8, 8)
# --- Gemini (vision-language LLM that writes the report) -----------------
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
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