Initial commit with model and app

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import cv2
+import numpy as np
+from anomalib.deploy import OpenVINOInferencer
+from huggingface_hub import hf_hub_download
+
+# Load the model
+model_path = hf_hub_download(repo_id="suidinpa/bottle-anomaly-detection", filename="model.bin")
+metadata_path = hf_hub_download(repo_id="suidinpa/bottle-anomaly-detection", filename="metadata.json")
+
+# Initialize OpenVINO inferencer
+inferencer = OpenVINOInferencer(
+    path=model_path,
+    metadata=metadata_path,
+    device="CPU",
+    task="classification"
+)
+
+def predict(image):
+    """Function to process the image and predict with the model"""
+    # Convert image to a format suitable for the model
+    img_array = np.array(image)
+    img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+
+    # Predict anomaly
+    predictions = inferencer.predict(img_bgr)
+
+    # Return results
+    return predictions.pred_label, predictions.pred_score
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict,  # The prediction function
+    inputs=gr.Image(type="numpy", label="Upload Image"),  # Image input from the user
+    outputs=["text", "number"],  # Text (for label) and number (for prediction score)
+    live=True  # Make the app live for real-time predictions
+)
+
+# Launch the app
+interface.launch()