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app.py

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+import cv2
+from matplotlib import pyplot as plt
+import gradio as gr
+
+
+def my_app(img):
+    # Opening image
+    # img = cv2.imread("image.jpg")
+
+# OpenCV opens images as BRG
+# but we want it as RGB We'll
+# also need a grayscale version
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+
+# Use minSize because for not
+# bothering with extra-small
+# dots that would look like STOP signs
+    stop_data = cv2.CascadeClassifier('stop_data.xml')
+
+    found = stop_data.detectMultiScale(img_gray,
+                                       minSize=(20, 20))
+
+# Don't do anything if there's
+# no sign
+    amount_found = len(found)
+
+    if amount_found != 0:
+
+        # There may be more than one
+        # sign in the image
+        for (x, y, width, height) in found:
+
+            # We draw a green rectangle around
+            # every recognized sign
+            cv2.rectangle(img_rgb, (x, y),
+                          (x + height, y + width),
+                          (0, 255, 0), 5)
+
+# Creates the environment of
+# the picture and shows it
+    plt.subplot(1, 1, 1)
+    plt.imshow(img_rgb)
+    plt.show()
+
+
+gr.interface.Interface(fn=my_app, live=True, inputs=gr.Image(
+    source='webcam', streaming=True), outputs="text").launch()

requirements.txt

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+opencv-python
+matplotlib
+gradio