Delete utils.py

utils.py

@@ -1,62 +0,0 @@
-
-from transformers import AutoModelForImageClassification, AutoProcessor
-import torch
-from PIL import Image
-
-# Load models once
-bline_model = AutoModelForImageClassification.from_pretrained("RTLucassen/B-line_detection")
-bline_processor = AutoProcessor.from_pretrained("RTLucassen/B-line_detection")
-
-hamdan_model = AutoModelForImageClassification.from_pretrained("hamdan07/UltraSound-Lung")
-hamdan_processor = AutoProcessor.from_pretrained("hamdan07/UltraSound-Lung")
-
-def analyze_image(image):
-    print("🔍 Running B-line detection...")
-    inputs_bline = bline_processor(images=image, return_tensors="pt")
-    outputs_bline = bline_model(**inputs_bline)
-    logits_bline = outputs_bline.logits
-    print("B-line logits:", logits_bline.tolist())
-    pred_bline = torch.argmax(logits_bline, dim=1).item()
-    print("B-line prediction:", pred_bline)
-
-    bline_labels = ["No B-lines", "Few", "Multiple", "Coalescent"]
-    bline_label = bline_labels[pred_bline]
-    bline_count = [0, 2, 5, 10][pred_bline]
-
-    print(f"→ B-line label: {bline_label} | Estimated count: {bline_count}")
-
-    print("🔍 Running Consolidation detection...")
-    inputs_hamdan = hamdan_processor(images=image, return_tensors="pt")
-    outputs_hamdan = hamdan_model(**inputs_hamdan)
-    logits_hamdan = outputs_hamdan.logits
-    print("Consolidation logits:", logits_hamdan.tolist())
-    pred_hamdan = torch.argmax(logits_hamdan, dim=1).item()
-    print("Consolidation prediction:", pred_hamdan)
-
-    consolidation_label = "No consolidation ✅" if pred_hamdan == 0 else "Possible consolidation ⚠️"
-
-    if pred_hamdan != 0:
-        score = 3
-        interpretation = "Consolidation detected — severe abnormality"
-    elif bline_count == 0:
-        score = 0
-        interpretation = "Normal image with A-lines"
-    elif bline_count <= 2:
-        score = 1
-        interpretation = "Mild abnormality with few B-lines"
-    elif bline_count <= 6:
-        score = 2
-        interpretation = "Moderate abnormality with multiple B-lines"
-    else:
-        score = 3
-        interpretation = "Severe abnormality — B-lines + possible consolidation"
-
-    print(f"🧮 Final LUS Score: {score} → {interpretation}")
-
-    return {
-        "bline_count": bline_count,
-        "bline_label": bline_label,
-        "consolidation_label": consolidation_label,
-        "lus_score": score,
-        "lus_interpretation": interpretation
-    }