Refine code

.gitignore

@@ -1 +1,3 @@
 overview_docs/
+__pycache__/
+*.pyc

app.py

@@ -185,8 +185,6 @@ def _report_to_html(text: str) -> str:
     return f'<pre id="report-pre">{escaped}</pre>'
 
 
-# Accent: sky blue (#38BDF8 / #7DD3FC) on the existing dark navy/black backgrounds.
-# Much higher contrast than dark purple; still clinical and calm.
 CUSTOM_CSS = """
 /* ── Base font scale ─────────────────────────────────────── */
 html { font-size: 22px !important; }

export.py

@@ -8,11 +8,11 @@ from PIL import Image as PILImage
 PAGE_W   = 210
 PAGE_H   = 297
 MARGIN   = 15
-USABLE_W = PAGE_W - 2 * MARGIN   # 180 mm
+USABLE_W = PAGE_W - 2 * MARGIN
 
 N_COLS  = 3
 COL_GAP = 3
-IMG_W   = (USABLE_W - (N_COLS - 1) * COL_GAP) / N_COLS   # ~58 mm
+IMG_W   = (USABLE_W - (N_COLS - 1) * COL_GAP) / N_COLS
 IMG_H   = IMG_W
 CAP_H   = 5
 
@@ -93,12 +93,6 @@ def _section(pdf: FPDF, title: str, gap_before: float = 7):
 
 
 def _image_grid(pdf: FPDF, items: list):
-    """
-    3-column grid of (caption, PIL Image).
-    Uses set_xy + image(w, h) [no explicit y] per image.
-    Caption placed with pdf.text() — zero cursor impact.
-    row_y is a local variable, never derived from get_y() after image placement.
-    """
     row_y = None
 
     for i, (caption, img) in enumerate(items):
@@ -112,12 +106,9 @@ def _image_grid(pdf: FPDF, items: list):
 
         x = MARGIN + col * (IMG_W + COL_GAP)
 
-        # Place image at (x, row_y) without specifying y explicitly
         pdf.set_xy(x, row_y)
         pdf.image(_png(img), w=IMG_W, h=IMG_H)
-        # fpdf2 advances cursor to (x + IMG_W, row_y + IMG_H) — we ignore it
 
-        # Caption: pdf.text() places text at absolute coords, does NOT move cursor
         pdf.set_font("Helvetica", style='', size=FS_SMALL)
         pdf.set_text_color(*MID_GRAY)
         cap = _safe(caption)
@@ -125,24 +116,17 @@ def _image_grid(pdf: FPDF, items: list):
         pdf.text(x + (IMG_W - tw) / 2, row_y + IMG_H + 4, cap)
         pdf.set_text_color(*BLACK)
 
-        # After last column (or last item), advance cursor past the row
         if col == N_COLS - 1 or i == len(items) - 1:
             pdf.set_xy(MARGIN, row_y + IMG_H + CAP_H + 4)
 
 
 def _table(pdf: FPDF, metrics: dict):
-    """
-    2-column table.  Uses new_y='TOP' for first cell (y stays),
-    new_y='NEXT' for last cell (y advances one row).
-    No TMARGIN — that resets y to page top and breaks layout.
-    """
     lw = USABLE_W * 0.68
     vw = USABLE_W * 0.32
     rh = 7
 
     pdf.set_draw_color(*BLACK)
 
-    # Header row
     pdf.set_font("Helvetica", style='B', size=FS_SMALL)
     pdf.set_text_color(*BLACK)
     pdf.cell(lw, rh, "BIOMARKER / SCORE", border=1,
@@ -150,7 +134,6 @@ def _table(pdf: FPDF, metrics: dict):
     pdf.cell(vw, rh, "VALUE", border=1,
              new_x='LMARGIN', new_y='NEXT')
 
-    # Data rows
     for k, v in metrics.items():
         pdf.set_font("Helvetica", style='', size=FS_BODY)
         pdf.set_text_color(*BLACK)
@@ -167,7 +150,6 @@ def generate_pdf_report(data: dict) -> str:
     pdf.set_auto_page_break(auto=True, margin=MARGIN)
     pdf.add_page()
 
-    # ── TITLE ────────────────────────────────────────────────────
     pdf.set_font("Helvetica", style='B', size=FS_TITLE)
     pdf.set_text_color(*DARK_GRAY)
     pdf.cell(0, 9, "GlauNET - Glaucoma Screening Report",
@@ -187,7 +169,6 @@ def generate_pdf_report(data: dict) -> str:
     _hline(pdf)
     pdf.set_text_color(*BLACK)
 
-    # ── DIAGNOSIS ────────────────────────────────────────────────
     _section(pdf, "Automated Diagnosis", gap_before=5)
     decision    = data["decision"]
     is_positive = "SUSPECTED" in decision
@@ -195,17 +176,15 @@ def generate_pdf_report(data: dict) -> str:
     pdf.set_text_color(*BLACK)
     pdf.cell(0, 9, _safe(decision), align='C', new_x='LMARGIN', new_y='NEXT')
 
-    # ── INPUT FUNDUS IMAGE ───────────────────────────────────────
     _section(pdf, "Input Fundus Image")
 
     if pdf.get_y() + INPUT_W + 8 > PAGE_H - MARGIN:
         pdf.add_page()
 
     x_img   = MARGIN + (USABLE_W - INPUT_W) / 2
-    img_top = pdf.get_y()                           # save y BEFORE image
+    img_top = pdf.get_y()
     pdf.set_xy(x_img, img_top)
     pdf.image(_png(data["input_image"]), w=INPUT_W, h=INPUT_W)
-    # fpdf2 cursor is now at img_top + INPUT_W — we IGNORE it and use img_top
 
     pdf.set_font("Helvetica", style='I', size=FS_SMALL)
     pdf.set_text_color(*MID_GRAY)
@@ -214,24 +193,19 @@ def generate_pdf_report(data: dict) -> str:
              img_top + INPUT_W + 4, cap)
     pdf.set_text_color(*BLACK)
 
-    # Advance cursor manually: img_top + image height + caption gap
     pdf.set_xy(MARGIN, img_top + INPUT_W + 8)
 
-    # ── SEGMENTATION PIPELINE (first 5 images) ───────────────────
     all_images = list(data["images"].items())
     _section(pdf, "Segmentation Pipeline")
     _image_grid(pdf, all_images[:5])
 
-    # ── GRADCAM ANALYSIS (last 4 images) ─────────────────────────
     _section(pdf, "GradCAM Analysis")
     _image_grid(pdf, all_images[5:])
 
-    # ── CLINICAL BIOMARKERS ──────────────────────────────────────
     _section(pdf, "Clinical Biomarkers")
     _table(pdf, data["metrics"])
     pdf.ln(3)
 
-    # ── MEDGEMMA CLINICAL REPORT ─────────────────────────────────
     _section(pdf, "MedGemma Clinical Report")
     pdf.set_font("Courier", style='', size=FS_MONO)
     pdf.set_text_color(*DARK_GRAY)
@@ -243,7 +217,6 @@ def generate_pdf_report(data: dict) -> str:
         pdf.cell(0, 5, "(No report generated)", new_x='LMARGIN', new_y='NEXT')
     pdf.set_text_color(*BLACK)
 
-
     tmp = tempfile.NamedTemporaryFile(
         suffix=".pdf", prefix="glaunet_report_", delete=False
     )

models.py

@@ -52,7 +52,7 @@ def conv_block(in_ch, out_ch):
     )
 
 
-class UNetEfficientNetB5(nn.Module):
+class UNetEfficientNetB4(nn.Module):
     def __init__(self, num_classes=3, pretrained=False):
         super().__init__()
         self.encoder = timm.create_model(
@@ -132,7 +132,7 @@ def load_yolo(path: str):
 
 
 def load_unet(path: str):
-    model = UNetEfficientNetB5(num_classes=3, pretrained=False).to(DEVICE)
+    model = UNetEfficientNetB4(num_classes=3, pretrained=False).to(DEVICE)
     state_dict = torch.load(path, map_location=DEVICE)
     model.load_state_dict(state_dict, strict=True)
     model.eval()

pipeline.py

@@ -1,4 +1,5 @@
 import cv2
+import math
 import numpy as np
 import torch
 import torchvision.transforms.functional as TF
@@ -178,9 +179,18 @@ def predict_glaucoma(image_path: str, yolo_model, unet_model, eff_model,
     si_compliant, si_ratios = compute_si_ratio(od_mask, oc_mask)
     nrr_area                = compute_nrr_area(od_mask, oc_mask)
 
-    p_holistic = run_efficientnet(image, eff_model, device)
-
-    if seg_failed:
+    try:
+        p_holistic = run_efficientnet(image, eff_model, device)
+        eff_failed = math.isnan(p_holistic) or math.isinf(p_holistic)
+    except Exception:
+        p_holistic = None
+        eff_failed = True
+
+    if seg_failed and eff_failed:
+        raise ValueError("Segmentarea și clasificarea holistică au eșuat. Analiza nu poate fi efectuată.")
+    elif eff_failed:
+        raise ValueError("Clasificarea holistică a eșuat. Analiza nu poate fi efectuată.")
+    elif seg_failed:
         p_fused = p_holistic
     else:
         p_fused = fusion_model.predict_proba([[vCDR, p_holistic]])[0, 1]