Create app.py

app.py

@@ -0,0 +1,30 @@
+import cv2
+import gradio as gr
+from main import read_number_plate, add_text, add_rect
+from detection import resize
+
+def predict_fn(im):
+    im = resize(im)
+
+    boxes, texts = read_number_plate(im)
+    print(texts)
+    for box, text in zip(boxes, texts):
+        im = add_rect(im, *box)
+        im = add_text(im, text, box)
+    return im
+
+
+demo = gr.Interface(
+    fn=predict_fn,
+    inputs=[gr.inputs.Image(label="Input Image")],
+    outputs=[gr.inputs.Image(label="Prediction")],
+    title="Emotion Recognition Demo",
+    description="Emotion Classification Model trained on FER Dataset",
+    examples=[
+        ["example/car1.jpg"],
+        ["example/car2.jpg"],
+        ["example/car3.jpg"]
+    ],
+)
+
+demo.launch()