Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import cv2
+from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# Load pre-trained MobileNetV2 model
+model = MobileNetV2(weights="imagenet")
+
+def predict_objects(img):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_resized = cv2.resize(img, (224, 224))
+    img_array = img_to_array(img_resized)
+    img_array_expanded = preprocess_input(img_array.reshape((1, *img_array.shape)))
+
+    predictions = model.predict(img_array_expanded)
+    label = decode_predictions(predictions, top=1)[0][0][1]
+    
+    return img, label
+
+def draw_box(img, label):
+    height, width, _ = img.shape
+    cv2.rectangle(img, (0, 0), (width, height), (0, 255, 0), 2)
+    cv2.putText(img, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
+    return img
+
+def object_detection(frame):
+    frame, label = predict_objects(frame)
+    frame_with_box = draw_box(frame, label)
+    return frame_with_box
+
+gr.Interface(
+    fn=object_detection,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs="image",
+    live=True
+).launch()