utils added

utils/advisor.py

@@ -0,0 +1,7 @@
+from models.llm import StyleSavvy
+
+advisor = StyleSavvy()
+
+def get_advice(items, body_type, face_shape, gender,occasion):
+    return advisor.advise(items, body_type, face_shape, gender, 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)

utils/test_llm.py

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+# # test_llm.py
+# """
+# Test harness for StyleSavvy LLM prompts.
+# Defines multiple prompt templates and evaluates the generated outputs,
+# checking for the expected number of bullet-point style tips.
+# """
+# from models.llm import StyleSavvy
+
+# # Variant prompt templates with placeholders
+# PROMPT_TEMPLATES = {
+#     "occasion_driven": (
+#         "You are an expert fashion stylist. A client is preparing for {occasion}. "
+#         "They have a {body_type}-shaped body and a {face_shape} face. They’re currently wearing: {items}. "
+#         "Give 3 to 5 *distinct* style tips focused on making them look their best at the event. "
+#         "Make the suggestions relevant to the setting, weather, and formality of the occasion. "
+#         "Avoid repeating any advice."
+#     ),
+
+#     "function_based": (
+#         "You're advising someone with a {body_type} build and {face_shape} face. "
+#         "They're attending a {occasion} and are wearing {items}. "
+#         "Suggest 3–5 concise fashion improvements or enhancements. "
+#         "Each suggestion should be unique and tailored to the event. "
+#         "Include practical choices for color, layering, accessories, or footwear. "
+#         "Avoid repeating words or phrases."
+#     ),
+
+#     "intent_style": (
+#         "Act as a high-end personal stylist. Your client has a {body_type} body shape and a {face_shape} face. "
+#         "They're going to a {occasion} and are wearing {items}. "
+#         "Write 3 to 5 brief but powerful styling suggestions to elevate their look. "
+#         "Focus on intent—what feeling or impression each style choice creates for the event."
+#     ),
+# }
+
+
+# # Test parameters
+# BODY_TYPE = "Slim"
+# FACE_SHAPE = "Round"
+# OCCASION = "Rooftop Evening Party"
+# ITEMS = ["shirt", "jeans", "jacket","shoes"]
+
+# if __name__ == "__main__":
+#     advisor = StyleSavvy()
+
+#     for name, template in PROMPT_TEMPLATES.items():
+#         # Build prompt by replacing placeholders
+#         prompt = template.format(
+#             body_type=BODY_TYPE,
+#             face_shape=FACE_SHAPE,
+#             occasion=OCCASION,
+#             items=", ".join(ITEMS)
+#         )
+#         print(f"=== Testing template: {name} ===")
+#         print("Prompt:")
+#         print(prompt)
+
+#         # Generate output (use only supported args)
+#         result = advisor.pipe(
+#             prompt,
+#             max_length=advisor.max_length,
+#             early_stopping=True,
+#             do_sample=False
+#         )[0]["generated_text"].strip()
+
+#         print("Generated output:")
+#         print(result)
+
+#         # Extract bullet lines
+#         bullets = [ln for ln in result.splitlines() if ln.strip().startswith("- ")]
+#         print(f"Number of bullets detected: {len(bullets)}")
+#         for i, b in enumerate(bullets, start=1):
+#             print(f" {i}. {b}")
+#         print("" + "-"*40)
+
+
+# test_llm.py
+"""
+Test harness for StyleSavvy LLM prompts.
+Evaluates multiple prompt templates and parses the generated outputs into distinct tips.
+"""
+
+from models.llm import StyleSavvy
+
+# Variant prompt templates with placeholders
+# PROMPTS = {
+#     "direct_instruction": (
+#         "You are a professional fashion stylist. A client with a {body_type}-shaped body "
+#         "and {face_shape} face is preparing for {occasion}. They are currently wearing: {items}. "
+#         "Give exactly five distinct styling tips to improve their outfit. "
+#         "Each tip should be concise, actionable, and start on a new line."
+#     ),
+#     "category_expansion": (
+#         "As a high-end fashion advisor, provide five styling tips for a {body_type}-shaped person "
+#         "with a {face_shape} face attending {occasion}. They are wearing {items}. "
+#         "Offer one tip each for silhouette, color, accessories, footwear, and layering, "
+#         "each on its own line."
+#     ),
+#     "event_aesthetic": (
+#         "Imagine curating the perfect outfit for a {body_type}-shaped individual with a {face_shape} face "
+#         "at {occasion}. They are wearing {items}. Suggest 5 ways to enhance their style, "
+#         "focusing on event-appropriate aesthetics. Separate each tip with a newline."
+#     ),
+#     "fashion_editor": (
+#         "As a fashion editor, outline five unique styling tips for a {body_type}-shaped reader with a {face_shape} face "
+#         "attending {occasion}. They wear {items}. Each recommendation should reflect expertise and relevance. "
+#         "List each tip on a new line."
+#     ),
+#     "influencer_style": (
+#         "You’re an influencer giving sharp styling advice. A follower with a {body_type} body and {face_shape} face "
+#         "is going to {occasion}, wearing {items}. Reply with five snappy, modern style tips, "
+#         "each on its own line."
+#     ),
+# }
+
+PROMPTS = {
+    "direct_instruction": (
+        "You are a world-renowned fashion stylist celebrated for your bold creativity and attention to detail. "
+        "Your {gender} client has a {body_type}-shaped silhouette and a {face_shape} face, preparing for the {occasion}. "
+        "They’re wearing {items}. In vivid, sensory-rich language, provide one transformative styling recommendation that considers the event’s ambiance, lighting, and dress code. "
+        "Use dynamic adjectives and actionable insight to elevate their entire look."
+    ),
+    "category_expansion": (
+        "As a top-tier fashion advisor, craft one impactful styling suggestion for a {gender} individual with a {body_type} body "
+        "and {face_shape} face attending the {occasion}. They have on {items}. "
+        "Highlight a strategic enhancement in silhouette, color scheme, accessory choice, or footwear to elevate their look."
+    ),
+    "event_aesthetic": (
+        "Imagine you are curating an immersive style experience for a {gender} attendee with a {body_type} silhouette and {face_shape} face at the {occasion}. "
+        "They’re currently wearing {items}. Provide one highly descriptive recommendation that harmonizes fabric textures, color temperature, silhouette, and accessory accents with the event’s specific ambiance, lighting conditions, and seasonal atmosphere."
+    ),
+    "fashion_editor": (
+        "You are the Editor-in-Chief of a prestigious fashion publication. Advise a {gender} trendsetter with a {body_type} frame and {face_shape} face attending the {occasion}, "
+        "currently in {items}. Offer one magazine-cover-worthy styling tip—highlight a trending color palette, editorial-worthy silhouette, and innovative accessory placement that will resonate with a discerning audience."
+    ),
+    "influencer_style": (
+        "As a cutting-edge style influencer with millions of followers, recommend one eye-catching flair tip for a {gender} follower with a {body_type} physique and {face_shape} face, "
+        "heading to the {occasion} in {items}. Frame it as a social-media-caption-ready moment: mention a statement accessory, bold color pop, or texture twist that will go viral."
+    ),
+    "seasonal_trend": (
+        "As a seasonal style expert specializing in spring/summer trends, guide a {gender} individual with a {body_type} shape and {face_shape} face preparing for the {occasion}. "
+        "They currently wear {items}. Provide one tip incorporating current seasonal motifs—think floral prints, breathable linens, or eco-friendly fabrics—that elevates their ensemble."
+    ),
+}
+
+
+# Test parameters
+BODY_TYPE = "Slim"
+FACE_SHAPE = "SQUARE"
+OCCASION = "BEACH PARTY"
+ITEMS = ["jeans", "jacket", "shoes",'shirt']
+GENDER = "Male"
+
+if __name__ == "__main__":
+    advisor = StyleSavvy()
+
+    for name, template in PROMPTS.items():
+        print(f"=== Testing template: {name} ===")
+
+        # Build prompt
+        prompt = template.format(
+            body_type=BODY_TYPE,
+            face_shape=FACE_SHAPE,
+            occasion=OCCASION,
+            gender = GENDER,
+            items=", ".join(ITEMS)
+
+        )
+        print("Prompt:\n" + prompt)
+
+        # Generate response
+        result = advisor.pipe(
+            prompt,
+            max_length=advisor.max_length,
+            early_stopping=True,
+            num_beams=4,
+            no_repeat_ngram_size=3,  
+            do_sample=False)[0]["generated_text"].strip()
+
+        print("\nRaw generated output:\n" + result)
+
+        # Parse into tips (bullets or sentence)
+        lines = result.splitlines()
+        tips = [ln.strip("-*0123456789. ").strip() for ln in lines if ln.strip()]
+        if len(tips) < 3:
+            # fallback to sentence split
+            tips = [p.strip() for p in result.split(".") if p.strip()]
+        tips = list(dict.fromkeys(tips))  # remove duplicates
+
+        print(f"\n💡 Parsed {len(tips)} style tips:")
+        for i, tip in enumerate(tips[:5], 1):
+            print(f"{i}. {tip}")
+        print("-" * 40)