Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from openvino.inference_engine import IECore
+import cv2
+import numpy as np
+
+# Load the OpenVINO model
+ie = IECore()
+net = ie.read_network(model='openvino/best.xml', weights='openvino/best.bin')
+exec_net = ie.load_network(network=net, device_name="CPU")
+
+# Define a function for prediction
+def predict(image):
+    # Preprocess the image
+    input_blob = next(iter(net.input_info))
+    input_shape = net.input_info[input_blob].input_data.shape
+    image_resized = cv2.resize(image, (input_shape[3], input_shape[2]))
+    image_transposed = image_resized.transpose((2, 0, 1))
+    image_batch = np.expand_dims(image_transposed, axis=0)
+
+    # Perform inference
+    result = exec_net.infer(inputs={input_blob: image_batch})
+    output_blob = next(iter(net.outputs))
+    detections = result[output_blob]
+
+    # Post-process the results
+    boxes = []
+    for detection in detections[0][0]:
+        confidence = detection[2]
+        if confidence > 0.5:  # Threshold
+            xmin = int(detection[3] * image.shape[1])
+            ymin = int(detection[4] * image.shape[0])
+            xmax = int(detection[5] * image.shape[1])
+            ymax = int(detection[6] * image.shape[0])
+            boxes.append((xmin, ymin, xmax, ymax, confidence))
+
+    # Draw bounding boxes on the image
+    for (xmin, ymin, xmax, ymax, confidence) in boxes:
+        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+
+    return image
+
+# Create a Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="numpy"), outputs=gr.Image(type="numpy"))
+
+# Launch the Gradio app
+iface.launch()