sample hockey breed images

requirements.txt

@@ -0,0 +1,42 @@
+altair==5.4.1
+attrs==24.2.0
+blinker==1.8.2
+cachetools==5.5.0
+certifi==2024.8.30
+charset-normalizer==3.3.2
+click==8.1.7
+gitdb==4.0.11
+GitPython==3.1.43
+idna==3.8
+Jinja2==3.1.4
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+narwhals==1.6.0
+numpy==2.1.0
+packaging==24.1
+pandas==2.2.2
+pillow==10.4.0
+protobuf==5.28.0
+pyarrow==17.0.0
+pydeck==0.9.1
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.1
+referencing==0.35.1
+requests==2.32.3
+rich==13.8.0
+rpds-py==0.20.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.38.0
+streamlit-image-select==0.6.0
+tenacity==8.5.0
+toml==0.10.2
+tornado==6.4.1
+typing_extensions==4.12.2
+tzdata==2024.1
+urllib3==2.2.2
+watchdog==4.0.2

src/Hockey_Breeds.py

@@ -1,14 +1,14 @@
 import streamlit as st
+from streamlit_image_select import image_select
 
 import logging
 
 st.set_page_config(page_title='Hockey Breeds', layout="wide",
                    page_icon=":frame_with_picture:")
 
-st.title('Hockey Breeds - Proof of Concept')
+st.title('Hockey Breeds - Hello Computer Vision')
 
-st.subheader('Image Classification',
-             help='Proof of concept application')
+st.subheader('Image Classification')
 
 img_class = '''Image Classification in Computer Vision is the act of determining the most appropriate label for an entire image from a set of fixed labels.  
 A popular topic of image classification in Computer Vision introductions and courses is to use an example problem of training a model to label images of various pet breeds.
@@ -27,3 +27,8 @@ st.subheader("Validation Results")
 st.markdown('Validation of the model\'s performance was done using 26 images not included in the training set.  The model performed fairly well against the validation dataset, with only 1 misclassified image.')
 st.image("src/images/samples/confusion_matrix.png", caption="Confusion Matrix for Hockey Breeds ")
 
+st.subheader("Try it out")
+
+# unzip the sample images
+
+img = image_select(label="Select an image and hockey breeds will guess a label", images=["src/images/"])