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utils/advisor.py

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+from models.llm import StyleSavvy
+
+advisor = StyleSavvy()
+
+def get_advice(items, body_type, face_shape, occasion):
+    return advisor.advise(items, body_type, face_shape, occasion)
+

utils/bg_removal.py

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+import os, requests
+from io import BytesIO
+from PIL import Image
+from dotenv import load_dotenv
+
+load_dotenv()
+
+API_KEY = os.getenv("REMOVE_BG_API_KEY")
+ENDPOINT = "https://api.remove.bg/v1.0/removebg"
+
+def remove_background(image_bytes: bytes) -> Image.Image:
+    resp = requests.post(
+        ENDPOINT,
+        files ={"image_file": ("image.jpg", image_bytes, "image/jpeg")},
+        data = {"size": "auto"},
+        headers = {"X-Api-Key": API_KEY},
+    )
+    resp.raise_for_status()
+    return Image.open(BytesIO(resp.content))
+

utils/detector.py

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+
+from io import BytesIO
+from PIL import Image
+from models.vision import VisionModel
+from utils.bg_removal import remove_background
+
+vision = VisionModel()
+FASHION_LABELS = {
+    "shirt", "t-shirt", "blouse", "tank top", "sweater", "hoodie", "jacket",
+    "coat", "overcoat", "raincoat", "windbreaker", "cardigan", "blazer",
+    "pants", "jeans", "shorts", "leggings", "tights", "skirt", "dress",
+    "suit", "jumpsuit", "romper", "vest", "sports bra", "tracksuit",
+    "belt", "tie", "scarf", "hat", "cap", "gloves", "socks",
+    "shoe", "sneakers", "boots", "sandals", "heels",
+    "watch", "necklace", "bracelet", "earrings", "ring",
+    "backpack", "handbag", "purse", "wallet"
+}
+
+def detect_clothing(image_input, do_bg_remove: bool = False):
+    # 1) Load into a PIL.Image if it's a filepath
+    if isinstance(image_input, str):
+        img = Image.open(image_input)
+    else:
+        img = image_input
+
+    # 2) Optionally remove background (works on bytes)
+    if do_bg_remove:
+        buf = BytesIO()
+        img.convert("RGB").save(buf, format="JPEG")
+        img_bytes = buf.getvalue()
+        img = remove_background(img_bytes)
+    else:
+        # ensure you drop any alpha channel
+        img = img.convert("RGB")
+
+    # 3) Run detection
+    raw_detections = vision.detect(img)
+
+    # 4) Filter and deduplicate
+    filtered = {}
+    for det in raw_detections:
+        label = det["label"].lower()
+        if label in FASHION_LABELS:
+            # Only keep the first or highest score if multiple detected
+            if label not in filtered or det["score"] > filtered[label]["score"]:
+                filtered[label] = {
+                    "label": label,
+                    "score": det["score"],
+                    "box": det.get("box", [])
+                }
+
+    # 5) Return dict or fallback if empty
+    if not filtered:
+        return {"outfit": {"label": "outfit", "score": 1.0, "box": []}}
+
+    return filtered
+
+
+

utils/test_detector.py

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+# test_detector.py
+
+from detector import detect_clothing
+from PIL import Image, ImageDraw
+import os
+
+def visualize_and_print(image_path, do_bg_remove=False, output_dir="vis"):
+    # Ensure output folder exists
+    os.makedirs(output_dir, exist_ok=True)
+
+    img = Image.open(image_path).convert("RGB")
+    print(f"\n--- Testing {os.path.basename(image_path)} (bg_remove={do_bg_remove}) ---")
+    
+    # Run your detector
+    dets = detect_clothing(img, do_bg_remove=do_bg_remove)
+    if not dets:
+        print("No detections!")
+        return
+
+    # Print raw detections
+    # Print raw detections
+    for i, d in enumerate(dets.values(), 1):
+        lbl = d["label"]
+        scr = d["score"]
+        box = d.get("box", [])
+        print(f" {i}. {lbl:12s} @ {scr:.2f} → {box}")
+
+    # Draw boxes
+    vis = img.copy()
+    draw = ImageDraw.Draw(vis)
+    for d in dets.values():
+        if d.get("box"):
+            x0, y0, x1, y1 = d["box"]
+            draw.rectangle([x0, y0, x1, y1], outline="red", width=2)
+            draw.text((x0, y0 - 10), f"{d['label']}:{d['score']:.2f}", fill="red")
+        # Save visualization
+        out_path = os.path.join(output_dir, os.path.basename(image_path))
+        vis.save(out_path)
+        print(f" Visualization saved to {out_path}")
+    
+if __name__ == "__main__":
+    # List your test images here
+    samples = [
+        "/Users/tanzimfarhan/Desktop/Python/Codes/SLU/CS5930/FinalProject/StyleSavvy/images/casual.jpg",
+        "/Users/tanzimfarhan/Desktop/Python/Codes/SLU/CS5930/FinalProject/StyleSavvy/images/WomenCasual.jpg",
+    ]
+    for img_path in samples:
+        visualize_and_print(img_path, do_bg_remove=False)
+        # visualize_and_print(img_path, do_bg_remove=True)