Update app.py

app.py

@@ -1,50 +1,48 @@
 import streamlit as st
 import degirum as dg
 from PIL import Image
+import degirum_tools
 
-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')
-
-with st.sidebar:
-    st.header('Specify Model Options Below')
-    runtime_agent_device=st.radio("Choose runtime agent device combo",("N2X-ORCA1","TFLite-EdgeTPU","OpenVINO-CPU"),index=0,horizontal=True)
-    activation_option=st.radio( 'Select activation function', ['relu6', 'silu'],horizontal=True)
-    dataset_option=st.radio( 'Select a dataset option', ['coco', 'face','lp','car','hand'],horizontal=True)
-    show_labels=st.toggle('Show labels in output',value=True)
-    show_probabilities=st.toggle('Show probabilities in output',value=False)
-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')
+# hw_location: Where you want to run inference.
+#     Use "@cloud" to use DeGirum cloud.
+#     Use "@local" to run on local machine.
+#     Use an IP address for AI server inference.
+hw_location = "@cloud"
+
+# face_model_zoo_url: URL/path for the face model zoo.
+#     Use cloud_zoo_url for @cloud, @local, and AI server inference options.
+#     Use '' for an AI server serving models from a local folder.
+#     Use a path to a JSON file for a single model zoo in case of @local inference.
+face_model_zoo_url = "https://cs.degirum.com/degirum/ultralytics_v6"
+
+# face_model_name: Name of the model for face detection.
+face_model_name = "yolov8n_relu6_face--640x640_quant_n2x_orca1_1"
+
+# gender_model_zoo_url: URL/path for the gender model zoo.
+gender_model_zoo_url = "https://cs.degirum.com/degirum/openvino"
+
+# gender_model_name: Name of the model for gender detection.
+gender_model_name = "mobilenet_v2_gender--160x160_float_openvino_cpu_1"
+
+# Connect to AI inference engine getting token from env.ini file
+face_zoo = dg.connect(hw_location, face_model_zoo_url, token=st.secrets["DG_TOKEN"])
+gender_zoo = dg.connect(hw_location, gender_model_zoo_url, token=st.secrets["DG_TOKEN"])
+
+# Load models
+face_model = face_zoo.load_model(face_model_name)
+gender_model= gender_zoo.load_model(gender_model_name)
+# Create a compound cropping model with 50% crop extent
+crop_model = degirum_tools.CroppingAndClassifyingCompoundModel(
+    face_model, gender_model, 50.0
+    )
+
+st.title('DeGirum Cloud Platform Demo of Face and Gender Detection Model')
+
+st.text('Upload an image. Then click on the submit button')
 with st.form("model_form"):
-    filtered_model_list=[]
-    for model in model_options:
-        if activation_option in model and dataset_option in model:
-            filtered_model_list.append(model)
-    st.write('Number of models found = ', len(filtered_model_list))
-    model_name=st.selectbox("Choose a Model from the list", filtered_model_list)
     uploaded_file=st.file_uploader('input image')
     submitted = st.form_submit_button("Submit")
     if submitted:
-        model=zoo.load_model(model_name,
-                             overlay_show_labels=show_labels,
-                             overlay_show_probabilities=show_probabilities,
-                             overlay_font_scale=3,
-                             overlay_line_width=6,
-                             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')
-        model.measure_time=True
-        predictions=model(image)
-        stats=model.time_stats()
-        st.write('Expected Frames per second for the model= ', 1000.0/stats["CoreInferenceDuration_ms"].avg)
+        inference_results=crop_model(image)
+        st.image(inference_results.image_overlay,caption='Image with Bounding Boxes')