Create app.py

app.py

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+import os
+import numpy as np
+import cv2
+import gradio as gr
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+
+# ==============================================================================
+# 1. PENGUNDUHAN & INISIALISASI MODEL ONNX FROM HUB
+# ==============================================================================
+REPO_ID = "ASomeoneWhoInterestedWithAI/LookThemV7_Caltech256-ONNX"
+
+print("📡 Mengunduh sasis LookThem V7-W dari Hugging Face Hub...")
+onnx_path = hf_hub_download(repo_id=REPO_ID, filename="LookThemV7_Caltech256.onnx")
+onnx_data_path = hf_hub_download(repo_id=REPO_ID, filename="LookThemV7_Caltech256.onnx.data")
+
+print("🧠 Membakar Graf ke ONNX Runtime Session...")
+session = ort.InferenceSession(onnx_path)
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
+
+# ==============================================================================
+# 2. STRUKTUR MAP RESMI 257 LABEL SEMANTIK CALTECH-256
+# ==============================================================================
+CLASSES = [
+    # 001 - 050
+    "ak-47", "american-flag", "backpack", "baseball-bat", "baseball-glove", "basketball-hoop", "bat", "bathtub", "bear", "beer-mug",
+    "billiards", "binoculars", "birdbath", "blimp", "bonsai-101", "boom-box", "bowling-ball", "bowling-pin", "boxing-glove", "brain-101",
+    "breadmaker", "buddha-101", "bulldozer", "butterfly", "cactus", "cake", "calculator", "camel", "cannon", "canoe",
+    "car-tire", "cart", "castle", "cat", "cd", "chair", "chimp", "chopsticks", "church-101", "clapperboard",
+    "computer-monitor", "computer-mouse", "couch", "crab", "crane", "croquet-ball", "crutch", "cuff-links", "cup", "dart-board",
+    # 051 - 100
+    "dining-table", "dog", "dolphindir", "door", "donkey", "dove", "duck", "dumbbell", "electronic-calc", "electricity-meter",
+    "faces-easy", "ferry", "fire-hydrant", "fire-truck", "fireworks", "flashlight", "floppy-disk", "football-helmet", "fox", "gramophone",
+    "grill", "hand-pump", "harp", "hot-air-balloon", "hot-dog", "hot-tub", "hourglass", "ibex", "ibis-101", "icon",
+    "igloo", "ipod", "iris", "iso-motorcycle", "jaguar", "kangaroo", "ketch", "keypad", "lamp", "laptop",
+    "leotard", "light-bulb", "lighter", "lizard", "lobster", "lotus", "mandolin", "marsupial", "microphone", "minimax",
+    # 101 - 150
+    "miniskirt", "modern-chair", "motorcycle", "mountain-bike", "mouse", "mule", "mussels", "necktie", "octopus", "owl",
+    "palm-tree", "piggy-bank", "pikachu", "pipe", "pistol", "pizza", "platypus", "pool-table", "pretzel", "pumice-stone",
+    "pumpkin", "puncher", "python", "quartz-clock", "rabbit", "racoon", "ray", "remote-control", "rhinoceros", "roller-skate",
+    "rose", "rotary-phone", "roller-blades", "saddle", "sand-sculptr", "saturn", "school-bus", "schooner", "squirrel", "stapler",
+    "starfish", "stepladder", "stingray", "stocking", "stopwatch", "sumo", "sunflower", "swiss-army-knife", "sword", "syringe",
+    # 151 - 200
+    "table-lamp", "teapot", "teddy-bear", "teepee", "telephone-box", "tennis-ball", "tennis-court", "tennis-racket", "theodolite", "toaster",
+    "tomato", "tombstone", "trefoil", "triceratops", "unicycle", "usb-drive", "vase", "violin", "voltaic-pile", "walking-cane",
+    "wall-clock", "water-bottle", "watermelon", "welding-mask", "wheelbarrow", "windmill", "wine-bottle", "xlophone", "yarmulke", "yo-yo",
+    "zebra", "airplanes-101", "car-side-101", "faces-easy-101", "greyhound", "tennis-shoes", "toad", "clutter",
+    # 201 - 257 (Additional & Overlap Categories)
+    "speedboat", "stainglass", "stegosaurus", "stop-sign", "strawberry", "sun-hat", "swan", "tank", "tick", "thimble",
+    "toad-101", "tree", "trombon", "trumpet", "tv-monitor", "typewritter", "umbrella", "ventilat", "watch", "waterfall",
+    "welding-mask-101", "wheelbarrow-101", "windmill-101", "wine-bottle-101", "wrench", "xylophone-101", "yarmulke-101", "yo-yo-101", "zebra-101", "airplanes-side-101",
+    "car-side-101-dup", "ferry-101", "ketch-101", "motorbikes-101", "schooner-101", "binocular", "bonsai", "buddha", "camera", "cannon-dup",
+    "car-side", "ceiling-fan", "cellphone", "chair-dup", "chandelier", "cougar-body", "cougar-face", "crab-dup", "crayfish", "crocodile",
+    "crocodile-head", "cup-dup", "dalmatian", "dollar-bill", "dragonfly", "electric-guitar", "clutter-bg"
+]
+
+# ==============================================================================
+# 3. FUNGSI PIPELINE INFERENCE (PREPROCESSING & RUN)
+# ==============================================================================
+def predict_image(img):
+    if img is None:
+        return "Silakan masukkan gambar terlebih dahulu."
+        
+    # --- PREPROCESSING (Sinkron dengan Transformasi PyTorch V7-W) ---
+    if len(img.shape) == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    elif img.shape[2] == 4:
+        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+        
+    img_resized = cv2.resize(img, (96, 96))
+    img_normalized = img_resized.astype(np.float32) / 255.0
+    
+    mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+    std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+    img_normalized = (img_normalized - mean) / std
+    
+    img_final = img_normalized.transpose(2, 0, 1)
+    input_tensor = np.expand_dims(img_final, axis=0)
+    
+    # --- TEMBAK RUNTIME INFERENCE ---
+    raw_outputs = session.run([output_name], {input_name: input_tensor})
+    logits = raw_outputs[0][0]
+    
+    # --- PASCA-PROSES (SOFTMAX STABIL) ---
+    exp_logits = np.exp(logits - np.max(logits))
+    probabilities = exp_logits / np.sum(exp_logits)
+    
+    # Mengambil Top 5 prediksi tertinggi untuk dashboard Gradio
+    top_5_indices = np.argsort(probabilities)[::-1][:5]
+    
+    results = {}
+    for idx in top_5_indices:
+        # Penanganan darurat jika indeks melompat di luar batas list
+        if idx < len(CLASSES):
+            class_name = CLASSES[idx]
+        else:
+            class_name = f"Unknown Class Idx-{idx}"
+            
+        confidence = float(probabilities[idx])
+        results[class_name] = confidence
+        
+    return results
+
+# ==============================================================================
+# 4. ARSITEKTUR ANTARMUKA GRADIO (UI DESIGN)
+# ==============================================================================
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🏎️ LookThem V7 (JEPA-Token Engine) - Caltech-256 Demo
+        ### Sasis Kustom Mini 0.17 MB (Graf) + External Data | Akurasi Pengujian Semantik 38.03% (*From Scratch*)!
+        Modul ini berjalan menggunakan **ONNX Runtime Engine** berbasis pelacakan Dynamo. Semua dropout latihan telah dilepas!
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="numpy", label="Input Gambar Caltech-256")
+            btn_submit = gr.Button("Analisis Karakteristik Gambar 🚀", variant="primary")
+            
+        with gr.Column():
+            output_labels = gr.Label(num_top_classes=5, label="Top 5 Prediksi Probabilitas Semantik")
+            
+    btn_submit.click(fn=predict_image, inputs=input_img, outputs=output_labels)
+
+if __name__ == "__main__":
+    demo.launch()