Update app.py

app.py

@@ -1,8 +1,5 @@
 import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-
+import cv2
 from openvino.runtime import Core
 
 ie = Core()
@@ -14,3 +11,32 @@ for device in devices:
     st.write("Device", device)
     st.write("Device Name", device_name)
 
+
+model = ie.read_model(model="v3-small_224_1.0_float.xml")
+compiled_model = ie.compile_model(model=model, device_name="CPU")
+
+output_layer = compiled_model.output(0)
+
+# The MobileNet model expects images in RGB format.
+image = cv2.cvtColor(cv2.imread(filename="coco.jpg"), code=cv2.COLOR_BGR2RGB)
+
+# Resize to MobileNet image shape.
+input_image = cv2.resize(src=image, dsize=(224, 224))
+
+# Reshape to model input shape.
+input_image = np.expand_dims(input_image, 0)
+st.image(image, caption='Input Image')
+
+result_infer = compiled_model([input_image])[output_layer]
+result_index = np.argmax(result_infer)
+
+# Convert the inference result to a class name.
+imagenet_classes = open("imagenet_2012.txt").read().splitlines()
+
+# The model description states that for this model, class 0 is a background.
+# Therefore, a background must be added at the beginning of imagenet_classes.
+imagenet_classes = ['background'] + imagenet_classes
+
+final_result=imagenet_classes[result_index]
+
+st.write("Inference Result:", final_result)