Upload 4 files

app (2).py

@@ -0,0 +1,68 @@
+
+import gradio as gr
+import torch
+from PIL import Image
+from torchvision import transforms
+from model_loader import load_model
+from index_to_attr import index_to_attr
+
+# Modell laden
+model = load_model("model/AttrPredModel_StateDict.pth")
+
+# taskName pro Index extrahieren
+def get_task_map(index_to_attr):
+    task_map = {}
+    for idx, desc in index_to_attr.items():
+        if "(" in desc and ")" in desc:
+            task = desc.split("(")[-1].split(")")[0]
+            task_map[idx] = task
+    return task_map
+
+task_map = get_task_map(index_to_attr)
+
+# Bildverarbeitungspipeline
+preprocess = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.6765, 0.6347, 0.6207],
+                         std=[0.3284, 0.3371, 0.3379])
+])
+
+# Inferenz-Funktion mit Markierung für unsichere Kategorien
+def predict(image):
+    image = image.convert("RGB")
+    input_tensor = preprocess(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(input_tensor)
+        probs = torch.sigmoid(output).squeeze().numpy()
+
+    result = {}
+    threshold = 0.5
+    top_per_task = {}
+
+    for idx, score in enumerate(probs):
+        task = task_map.get(idx, "unknown")
+        if task not in top_per_task or score > top_per_task[task][1]:
+            top_per_task[task] = (idx, score)
+
+    for task, (idx, score) in top_per_task.items():
+        label = index_to_attr.get(idx, f"Unknown ({idx})").split(" (")[0]
+        result[task] = {
+            "label": label,
+            "score": round(float(score), 4),
+            "confidence": "low" if score < threshold else "high"
+        }
+
+    return result
+
+# Gradio Interface – stabil und einfach
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload image"),
+    outputs="json",
+    title="Fashion Attribute Predictor (mit Confidence)",
+    description="Zeigt pro Attributgruppe die wahrscheinlichste Vorhersage + Confidence ('high' / 'low')."
+)
+
+if __name__ == "__main__":
+    demo.launch()

index_to_attr (3).py

@@ -0,0 +1,230 @@
+index_to_attr = {
+    0: 'Argyle (pattern)',
+    1: 'Asymmetric (style)',
+    2: 'Athletic Pants (category)',
+    3: 'Athletic Sets (category)',
+    4: 'Athletic Shirts (category)',
+    5: 'Athletic Shorts (category)',
+    6: 'Backless Dresses (neckline)',
+    7: 'Baggy Jeans (category)',
+    8: 'Bandage (style)',
+    9: 'Bandeaus (style)',
+    10: 'Batwing Tops (category)',
+    11: 'Beach & Swim Wear (category)',
+    12: 'Beaded (style)',
+    13: 'Beige (color)',
+    14: 'Bikinis (category)',
+    15: 'Binders (category)',
+    16: 'Black (color)',
+    17: 'Blouses (category)',
+    18: 'Blue (color)',
+    19: 'Bodycon (style)',
+    20: 'Bodysuits (category)',
+    21: 'Boots (category)',
+    22: 'Bra Straps (category)',
+    23: 'Bronze (color)',
+    24: 'Brown (color)',
+    25: 'Bubble Coats (category)',
+    26: 'Business Shoes (category)',
+    27: 'Camouflage (pattern)',
+    28: 'Canvas (material)',
+    29: 'Capes & Capelets (category)',
+    30: 'Capri Pants (category)',
+    31: 'Cardigans (category)',
+    32: 'Cargo Pants (category)',
+    33: 'Cargo Shorts (category)',
+    34: 'Cashmere (material)',
+    35: 'Casual Dresses (category)',
+    36: 'Casual Pants (category)',
+    37: 'Casual Shirts (category)',
+    38: 'Casual Shoes (category)',
+    39: 'Casual Shorts (category)',
+    40: 'Chambray (material)',
+    41: 'Checkered (pattern)',
+    42: 'Chevron (pattern)',
+    43: 'Chiffon (material)',
+    44: 'Clear (color)',
+    45: 'Cleats (category)',
+    46: 'Clubbing Dresses (category)',
+    47: 'Cocktail Dresses (category)',
+    48: 'Collared (neckline)',
+    49: 'Corduroy (material)',
+    50: 'Corsets (category)',
+    51: 'Costumes & Cosplay (category)',
+    52: 'Cotton (material)',
+    53: 'Criss Cross (style)',
+    54: 'Crochet (pattern)',
+    55: 'Crop Tops (category)',
+    56: 'Custom Made Clothing (category)',
+    57: 'Dance Wear (category)',
+    58: 'Denim (material)',
+    59: 'Drawstring Pants (category)',
+    60: 'Dress Shirts (category)',
+    61: 'Dresses (category)',
+    62: 'Embroidered (style)',
+    63: 'Fashion Sets (category)',
+    64: 'Faux Fur (material)',
+    65: 'Female (gender)',
+    66: 'Flannel (material)',
+    67: 'Flats (category)',
+    68: 'Fleece (material)',
+    69: 'Floral (pattern)',
+    70: 'Formal Dresses (category)',
+    71: 'Fringe (pattern)',
+    72: 'Furry (style)',
+    73: 'Galaxy (pattern)',
+    74: 'Geometric (pattern)',
+    75: 'Gingham (material)',
+    76: 'Gold (color)',
+    77: 'Gray (color)',
+    78: 'Green (color)',
+    79: 'Halter Tops (category)',
+    80: 'Harem Pants (category)',
+    81: 'Hearts (pattern)',
+    82: 'Heels (category)',
+    83: 'Herringbone (pattern)',
+    84: 'Hi-Lo (style)',
+    85: 'Hiking Boots (category)',
+    86: 'Hollow-Out (style)',
+    87: 'Hoodies & Sweatshirts (category)',
+    88: 'Hosiery, Stockings, Tights (category)',
+    89: 'Houndstooth (pattern)',
+    90: 'Jackets (category)',
+    91: 'Jeans (category)',
+    92: 'Jerseys (category)',
+    93: 'Jilbaab (category)',
+    94: 'Jumpsuits Overalls & Rompers (category)',
+    95: 'Kimonos (category)',
+    96: 'Knit (material)',
+    97: 'Lace (material)',
+    98: 'Leather (material)',
+    99: 'Leggings (category)',
+    100: 'Leopard And Cheetah (pattern)',
+    101: 'Linen (material)',
+    102: 'Lingerie Sleepwear & Underwear (category)',
+    103: 'Loafers & Slip-on Shoes (category)',
+    104: 'Long Sleeved (sleeve)',
+    105: 'Male (gender)',
+    106: 'Marbled (pattern)',
+    107: 'Maroon (color)',
+    108: 'Maternity (category)',
+    109: 'Mesh (pattern)',
+    110: 'Multi Color (color)',
+    111: 'Neoprene (material)',
+    112: 'Neutral (gender)',
+    113: 'Nightgowns (category)',
+    114: 'Nylon (material)',
+    115: 'Off The Shoulder (neckline)',
+    116: 'Orange (color)',
+    117: 'Organza (material)',
+    118: 'Padded Bras (category)',
+    119: 'Paisley (pattern)',
+    120: 'Pajamas (category)',
+    121: 'Party Dresses (category)',
+    122: 'Pasties (category)',
+    123: 'Patent (material)',
+    124: 'Peach (color)',
+    125: 'Peacoats (category)',
+    126: 'Pencil Skirts (category)',
+    127: 'Peplum (style)',
+    128: 'Petticoats (category)',
+    129: 'Pin Stripes (pattern)',
+    130: 'Pink (color)',
+    131: 'Plaid (pattern)',
+    132: 'Pleated (style)',
+    133: 'Plush (material)',
+    134: 'Polka Dot (pattern)',
+    135: 'Polos (category)',
+    136: 'Polyester (material)',
+    137: 'Printed (style)',
+    138: 'Prom Dresses (category)',
+    139: 'Puff Sleeves (sleeve)',
+    140: 'Pullover Sweaters (category)',
+    141: 'Purple (color)',
+    142: 'Quilted (pattern)',
+    143: 'Racerback (neckline)',
+    144: 'Rain Boots (category)',
+    145: 'Raincoats (category)',
+    146: 'Rayon (material)',
+    147: 'Red (color)',
+    148: 'Reversible (style)',
+    149: 'Rhinestone Studded (style)',
+    150: 'Ripped (pattern)',
+    151: 'Robes (category)',
+    152: 'Round Neck (neckline)',
+    153: 'Ruched (pattern)',
+    154: 'Ruffles (pattern)',
+    155: 'Running Shoes (category)',
+    156: 'Sandals (category)',
+    157: 'Satin (material)',
+    158: 'Sequins (pattern)',
+    159: 'Sheer Tops (category)',
+    160: 'Shoe Accessories (category)',
+    161: 'Shoe Inserts (category)',
+    162: 'Shoelaces (category)',
+    163: 'Short Sleeves (sleeve)',
+    164: 'Shorts (category)',
+    165: 'Shoulder Drapes (neckline)',
+    166: 'Silk (material)',
+    167: 'Silver (color)',
+    168: 'Skinny Jeans (category)',
+    169: 'Skirts (category)',
+    170: 'Sleeveless (sleeve)',
+    171: 'Slippers (category)',
+    172: 'Snakeskin (pattern)',
+    173: 'Sneakers (category)',
+    174: 'Spaghetti Straps (style)',
+    175: 'Spandex (material)',
+    176: 'Sports Bras (category)',
+    177: 'Square Necked (neckline)',
+    178: 'Stilettos (category)',
+    179: 'Strapless (sleeve)',
+    180: 'Stripes (pattern)',
+    181: 'Suede (material)',
+    182: 'Suits & Blazers (category)',
+    183: 'Summer (style)',
+    184: 'Sweatpants (category)',
+    185: 'Sweetheart Neckline (neckline)',
+    186: 'Swim Trunks (category)',
+    187: 'Swimsuit Cover-ups (category)',
+    188: 'Swimsuits (category)',
+    189: 'T-Shirts (category)',
+    190: 'Taffeta (material)',
+    191: 'Tan (color)',
+    192: 'Tank Tops (category)',
+    193: 'Teal (color)',
+    194: 'Thermal Underwear (category)',
+    195: 'Thigh Highs (category)',
+    196: 'Thongs (category)',
+    197: 'Three Piece Suits (category)',
+    198: 'Tie Dye (pattern)',
+    199: 'Trench Coats (category)',
+    200: 'Trousers (category)',
+    201: 'Tube Tops (category)',
+    202: 'Tulle (material)',
+    203: 'Tunic (style)',
+    204: 'Turtlenecks (neckline)',
+    205: 'Tutus (category)',
+    206: 'Tweed (material)',
+    207: 'Twill (material)',
+    208: 'Two-Tone (style)',
+    209: 'U-Necks (neckline)',
+    210: 'Undershirts (category)',
+    211: 'Underwear (category)',
+    212: 'Uniforms (category)',
+    213: 'V-Necks (neckline)',
+    214: 'Velour (material)',
+    215: 'Velvet (material)',
+    216: 'Vests (category)',
+    217: 'Vintage Retro (style)',
+    218: 'Vinyl (material)',
+    219: 'Wedding Dresses (category)',
+    220: 'Wedges & Platforms (category)',
+    221: 'White (color)',
+    222: 'Winter Boots (category)',
+    223: 'Wool (material)',
+    224: 'Wrap (style)',
+    225: 'Yellow (color)',
+    226: 'Yoga Pants (category)',
+    227: 'Zebra (pattern)',
+}

model_loader (1).py

@@ -0,0 +1,33 @@
+
+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+def load_model(model_path):
+    # Architektur aufbauen
+    model = models.resnet50(pretrained=False)
+    model.fc = nn.Linear(2048, 228)
+
+    # State Dict laden
+    state_dict = torch.load(model_path, map_location=torch.device("cpu"))
+
+    # Keys ggf. anpassen
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        if k.startswith("predictor."):
+            new_k = k.replace("predictor.", "")
+        else:
+            new_k = k
+        new_state_dict[new_k] = v
+
+    model.load_state_dict(new_state_dict)
+    model.eval()
+    return model
+
+def predict_attributes(model, input_tensor):
+    with torch.no_grad():
+        output = model(input_tensor)
+        prediction = torch.sigmoid(output).squeeze().numpy()
+    threshold = 0.5
+    predicted_indices = [i for i, p in enumerate(prediction) if p > threshold]
+    return predicted_indices

requirements (1).txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+gradio
+Pillow