Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+import degirum as dg
+from PIL import Image
+
+zoo=dg.connect(dg.CLOUD,zoo_url='https://cs.degirum.com/degirum/ultralytics_v6',token=st.secrets["DG_TOKEN"])
+
+st.title('DeGirum Cloud Platform Demo')
+
+st.header('Specify Model Options Below')
+runtime_agent_device=st.radio("Choose runtime agent device combo",("TFLite-EdgeTPU","N2X-ORCA","N2X-ORCA1","OpenVINO-CPU"),index=2)
+runtime_agent,device=runtime_agent_device.split('-')[0],runtime_agent_device.split('-')[1]
+model_options=zoo.list_models(device=device,runtime=runtime_agent)
+st.header('Choose and Run a Model')
+st.text('Select a model and upload an image. Then click on the submit button')
+with st.form("model_form"):
+    model_name=st.selectbox("Choose a Model from the list", model_options)
+    uploaded_file=st.file_uploader('input image')
+    submitted = st.form_submit_button("Submit")
+    if submitted:
+        model=zoo.load_model(model_name)
+        model.overlay_font_scale=3
+        model.overlay_line_width=6
+        model.image_backend='pil'
+        if model.output_postprocess_type=='PoseDetection':
+            model.overlay_show_labels=False
+        st.write("Model loaded successfully")
+        image = Image.open(uploaded_file)
+        predictions=model(image)
+        if model.output_postprocess_type=='Classification' or model.output_postprocess_type=='DetectionYoloPlates':
+            st.image(predictions.image,caption='Original Image')
+            st.write(predictions.results)
+        else:
+            st.image(predictions.image_overlay,caption='Image with Bounding Boxes/Keypoints')