diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..842a3f7766fd757119bd937d6137edf267937f4a
--- /dev/null
+++ b/app.py
@@ -0,0 +1,132 @@
+import time
+
+import gradio as gr
+import numpy as np
+from pathlib import Path
+import time
+from anomalib.deploy import OpenVINOInferencer
+from openvino.runtime import Core
+# Initialize the Core
+core = Core()
+
+# Get the available devices
+devices = core.available_devices
+
+inferencer = None
+
+example_list = [["bottle/examples/000.png", "anomaly_map", "bottle", "CPU"],
+ ["pill/examples/010.png", "heat_map", "pill", "CPU"],
+ ["zipper/examples/001.png", "pred_mask", "zipper", "CPU"],
+ ["grid/examples/005.png", "segmentations", "grid", "CPU"],
+ ["cubes/examples/005.jpg", "heat_map", "cubes", "CPU"]]
+
+def OV_compilemodel(category_choice, device):
+ global inferencer
+ #Get the available models
+ openvino_model_path = Path.cwd() / category_choice / "run" / "weights" / "openvino" / "model.bin"
+ metadata_path = Path.cwd() / category_choice / "run" / "weights" / "openvino" / "metadata.json"
+
+ inferencer = OpenVINOInferencer(
+ path=openvino_model_path, # Path to the OpenVINO IR model.
+ metadata_path=metadata_path, # Path to the metadata file.
+ device=device, # We would like to run it on an Intel CPU.
+ config= {"INFERENCE_PRECISION_HINT": "f16" } if device != "CPU" else {}
+ )
+
+ return inferencer
+
+def OV_inference(input_img, operation, category_choice, device):
+
+ start_time = time.time()
+ predictions = inferencer.predict(image=input_img)
+ stop_time = time.time()
+ inference_time = stop_time - start_time
+ confidence = predictions.pred_score
+
+ if operation == "original":
+ output_img1 = predictions.image
+ elif operation == "anomaly_map":
+ output_img1 = predictions.anomaly_map
+ elif operation == "heat_map":
+ output_img1 = predictions.heat_map
+ elif operation == "pred_mask":
+ output_img1 = predictions.pred_mask
+ elif operation == "segmentations":
+ output_img1 = predictions.segmentations
+ else:
+ output_img1 = predictions.image
+ return output_img1, round(inference_time*1000), round(confidence*100,2)
+
+with gr.Blocks() as demo:
+ gr.Markdown(
+ """
+ 
+ 
+
+ 🚀 Anomaly detection 🚀
+
+ Experience the power of the state-of-the-art anomaly detection with Anomalib-OpenVINO Anomaly detection toolbox. This interactive APP leverages the robust capabilities of Anomalib and OpenVINO.
+
+ All model are FP32 precision, if you select GPU it will automatically change precision to FP16. Using Anomalib you can also quantize your model in INT8 using NNCF.
+
+
+
+ 
+
+
+
+ """
+ )
+
+ gr.Markdown("## 1. Select the category over you want to detect anormalities.")
+ category_choice = gr.Radio(["bottle", "grid", "pill", "zipper", "cubes"], label="Choose the category")
+
+ gr.Markdown(
+ """
+ ## 2. Select the Intel device
+ Device Name | CPU | GPU.0 | GPU.1
+ ------------- | ------------ |------------- | -------------
+ Intel Device | CPU | Integrated GPU | Discrete GPU
+
+
+ """
+ )
+ device_choice = gr.Dropdown(devices, label="Choose the device")
+
+ gr.Markdown("## 3. Compile the model")
+ compile_btn = gr.Button("Compile Model")
+
+ gr.Markdown("## 4. Choose the output you want to visualize.")
+ output_choice = gr.Radio(["original", "anomaly_map", "heat_map", "pred_mask", "segmentations"], label="Choose the output")
+
+ gr.Markdown("## 5. Drop the image in the input image box and run the inference")
+ with gr.Row():
+ with gr.Column():
+ image = gr.Image(type="numpy", label= "Input image")
+
+
+ with gr.Column():
+ output_img = gr.outputs.Image(type="numpy", label="Anomalib Output")
+
+ inference_btn = gr.Button("Run Inference")
+
+ with gr.Row():
+ # Create your output components
+ #output_prediction = gr.Textbox(label="Prediction")
+ output_confidence = gr.Textbox(label="Confidence [%]")
+ output_time = gr.Textbox(label="Inference Time [ms]")
+
+ gr.Markdown("Note: Change the image and run the inference again. If you want to change the object you need to recompile the model, that means you need to start from step 1.")
+ gr.Markdown("## Image Examples")
+
+ gr.Examples(
+ examples=example_list,
+ inputs=[image, output_choice, category_choice, device_choice],
+ outputs=[output_img, output_time, output_confidence],
+ fn=OV_inference,
+ )
+
+ compile_btn.click(OV_compilemodel, inputs=[category_choice, device_choice])
+ inference_btn.click(OV_inference, inputs=[image, output_choice], outputs=[output_img, output_time, output_confidence])
+
+demo.launch(share=True, enable_queue=True)
\ No newline at end of file
diff --git a/bottle/examples/000.png b/bottle/examples/000.png
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index 0000000000000000000000000000000000000000..2c08a66f619ed767429b22accc400267f585ae47
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diff --git a/bottle/examples/007.png b/bottle/examples/007.png
new file mode 100644
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diff --git a/bottle/examples/010.png b/bottle/examples/010.png
new file mode 100644
index 0000000000000000000000000000000000000000..3998c7519dd246661683058b48765b88d877e3db
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diff --git a/bottle/examples/018.png b/bottle/examples/018.png
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index 0000000000000000000000000000000000000000..e98feee3a39daaa808c11e558784e4b80f5eb78a
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diff --git a/bottle/run/weights/lightning/model.ckpt b/bottle/run/weights/lightning/model.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..865fef9f079e021f208a8610376144ac2b4eb293
--- /dev/null
+++ b/bottle/run/weights/lightning/model.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30ef6e09877b0d7be1e0d8e7492f203ffaa33caea9b7009b5dc7137df8220815
+size 186461500
diff --git a/bottle/run/weights/openvino/metadata.json b/bottle/run/weights/openvino/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d6802d8766051e83c31b13016da6dd9a6b4a687b
--- /dev/null
+++ b/bottle/run/weights/openvino/metadata.json
@@ -0,0 +1,52 @@
+{
+ "task": "segmentation",
+ "transform": {
+ "__version__": "1.3.0",
+ "transform": {
+ "__class_fullname__": "Compose",
+ "p": 1.0,
+ "transforms": [
+ {
+ "__class_fullname__": "Resize",
+ "always_apply": true,
+ "p": 1,
+ "height": 256,
+ "width": 256,
+ "interpolation": 1
+ },
+ {
+ "__class_fullname__": "Normalize",
+ "always_apply": false,
+ "p": 1.0,
+ "mean": [
+ 0.485,
+ 0.456,
+ 0.406
+ ],
+ "std": [
+ 0.229,
+ 0.224,
+ 0.225
+ ],
+ "max_pixel_value": 255.0
+ },
+ {
+ "__class_fullname__": "ToTensorV2",
+ "always_apply": true,
+ "p": 1.0,
+ "transpose_mask": false
+ }
+ ],
+ "bbox_params": null,
+ "keypoint_params": null,
+ "additional_targets": {
+ "image": "image",
+ "depth_image": "image"
+ }
+ }
+ },
+ "image_threshold": 14.50792121887207,
+ "pixel_threshold": 12.323184967041016,
+ "min": 0.09382443130016327,
+ "max": 54.39400100708008
+}
\ No newline at end of file
diff --git a/bottle/run/weights/openvino/model.bin b/bottle/run/weights/openvino/model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..39c17ba9ac77b3cd4d717fb1aa190e0b34aa7abd
--- /dev/null
+++ b/bottle/run/weights/openvino/model.bin
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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ee1cb6a5c082dd8669cb8ee94ac985324c49d5172ba0c3fe4f7e3cc612a8da7
+size 176605956
diff --git a/bottle/run/weights/openvino/model.mapping b/bottle/run/weights/openvino/model.mapping
new file mode 100644
index 0000000000000000000000000000000000000000..0d58c28d713dcb0e10b8e9ef390486af6d8eb581
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..17e76a3a34d98aa39d6f756c13ab6244b0fdd370
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new file mode 100644
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