diff --git a/workspace/sprint_1_2/CODEBASE/infra/tests/test_predict_inflammation.py b/workspace/sprint_1_2/CODEBASE/infra/tests/test_predict_inflammation.py
new file mode 100644
index 0000000..0a6ee3d
--- /dev/null
+++ b/workspace/sprint_1_2/CODEBASE/infra/tests/test_predict_inflammation.py
@@ -0,0 +1,138 @@
+import json
+import time
+from pathlib import Path
+import numpy as np
+from PIL import Image
+import requests
+
+# Server configuration
+TRITON_URL = "https://dtj-tran--triton-s3-service-unified-triton-server.modal.run"
+TARGET_MODEL = "workspace/sprint_1_2/CODEBASE/infra/tests/models/efficientnet_b0_ultrasound_2_class.pth"  # Target a single sub-model directly
+IMFLAMMATION_CLASSES = ["Không viêm", "Có viêm"]
+
+BASE_DIR = Path(__file__).resolve().parent
+
+
+def load_image(path: Path) -> Image.Image:
+    if not path.exists():
+        raise FileNotFoundError(f"Test image not found at: {path}")
+    return Image.open(path).convert("RGB")
+
+
+def preprocess_224(img: Image.Image) -> np.ndarray:
+    """Preprocesses image to NCHW FP32 [1, 3, 224, 224] matching ResNet50 input requirements"""
+    img_resized = img.resize((224, 224), Image.Resampling.BILINEAR)
+    arr = np.asarray(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)
+    arr = (arr - mean) / std
+    arr = arr.transpose(2, 0, 1)  # HWC -> CHW
+    arr = np.expand_dims(arr, axis=0)  # Add batch dim -> NCHW
+    return arr
+
+
+def softmax(x: np.ndarray) -> np.ndarray:
+    e = np.exp(x - np.max(x))
+    return e / np.sum(e)
+
+def build_binary_request(input_tensor: np.ndarray, model_name: str) -> tuple[bytes, dict]:
+    """Constructs a strict KServe v2 binary payload for a single input tensor"""
+    # NOTE: Check your individual model's config.pbtxt to verify if the
+    # expected input name is 'input_image', 'input_0', etc.
+    input_name = "input_image"
+
+    inputs = [
+        {
+            "name": input_name,
+            "shape": list(input_tensor.shape),
+            "datatype": "FP32",
+            "parameters": {"binary_data_size": input_tensor.nbytes},
+        }
+    ]
+
+    # We request the standard 'logits' output tensor from this model
+    outputs = [{"name": "logits"}]
+
+    metadata = {
+        "model_name": model_name,
+        "model_version": "",
+        "inputs": inputs,
+        "outputs": outputs
+    }
+
+    metadata_bytes = json.dumps(metadata).encode("utf-8")
+    body = metadata_bytes + input_tensor.tobytes()
+
+    headers = {
+        "Inference-Header-Content-Length": str(len(metadata_bytes)),
+        "Content-Type": "application/octet-stream"
+    }
+    return body, headers
+
+def parse_binary_response(resp: requests.Response) -> np.ndarray:
+    """Parses Triton's binary response stream for a single output tensor"""
+    header_length = int(resp.headers.get("Inference-Header-Content-Length", "0"))
+    response_bytes = resp.content
+
+    metadata = json.loads(response_bytes[:header_length].decode("utf-8"))
+    binary_data = response_bytes[header_length:]
+
+    # Extract the first available output tensor
+    output_desc = metadata["outputs"][0]
+    shape = output_desc["shape"]
+
+    # Unpack raw bytes back into numpy array
+    arr = np.frombuffer(binary_data, dtype=np.float32).reshape(shape)
+    return arr
+
+def main():
+    # Define a path to a real local image to test with
+    image_path = "test_images/sup-up-long_positive/58e7a7ef-de3e-11ee-97e2-0a580a5f5b60_11.png"
+
+    print(f"Targeting Individual Model: {TARGET_MODEL}")
+    print(f"Loading image from: {image_path}")
+
+    try:
+        # 1. Prepare image
+        img = load_image(image_path)
+        input_data = preprocess_224(img)
+
+        # 2. Build the payload
+        body, headers = build_binary_request(input_data, TARGET_MODEL)
+
+        # 3. Fire request to the targeted model endpoint
+        url = f"{TRITON_URL}/v2/models/{TARGET_MODEL}/infer"
+        print("Sending request to Triton server...")
+
+        t0 = time.time()
+        resp = requests.post(url, data=body, headers=headers, timeout=30)
+        resp.raise_for_status()
+        latency = time.time() - t0
+
+        # 4. Parse output logits
+        logits = parse_binary_response(resp)
+        logits = np.squeeze(logits)  # Drop batch dimension
+
+        # 5. Decode probabilities
+        probs = softmax(logits)
+        predicted_idx = int(np.argmax(probs))
+
+        print("\n" + "=" * 40)
+        print("🎉 SUCCESSFUL INFERENCE")
+        print(f"Network Roundtrip Time: {latency:.4f}s")
+        print("-" * 40)
+        print("Class Probabilities:")
+        for cls_name, prob in zip(IMFLAMMATION_CLASSES, probs):
+            print(f"  {cls_name:<15}: {prob * 100:.2f}%")
+        print("-" * 40)
+        print(f"Prediction: {IMFLAMMATION_CLASSES[predicted_idx]} ({probs[predicted_idx] * 100:.2f}%)")
+        print("=" * 40)
+
+    except Exception as e:
+        print(f"\n❌ Inference Failed: {e}")
+        if 'resp' in locals() and hasattr(resp, 'text'):
+            print(f"Server Error Message: {resp.text}")
+
+
+if __name__ == "__main__":
+    main()
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