working based with multipage nav

.gitignore

@@ -0,0 +1 @@
+.venv/

README.md

@@ -5,8 +5,20 @@ colorFrom: blue
 colorTo: indigo
 sdk: streamlit
 sdk_version: 1.38.0
-app_file: app.py
+app_file: Home.py
 pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+## Dev install virtual environment -- python 3.11.5
+```python -m venv .venv```
+```source .venv/bin/activate```
+
+## Update requirements.txt
+```pip freeze > requirements.txt```
+
+## Manual Testing
+To run in google cloud shell: 
+```streamlit run src/app.py --browser.serverAddress 8501-$WEB_HOST --browser.serverPort 80```

app.py

@@ -1,4 +0,0 @@
-import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)

src/About.py

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+import streamlit as st
+
+import logging
+
+st.set_page_config(page_title='About TopShelf', layout="wide",
+                   page_icon="🥅")
+
+st.title('Welcome To Top Shelf :goal_net:',
+         help=':video_camera: + :ice_hockey_stick_and_puck: = :clipboard:')
+st.subheader('Artificial Intelligence for Hockey Coaches and Players',
+             help='Proof of concept application')
+
+overview = '''**Top Shelf** helps coaches and players analyze their gameplay, providing helpful suggestions on areas for improvement.
+
+The secret behind **Top Shelf** is *Computer Vision* AI technology that recognizes various hockey related objects in videos. 
+This model can recognize players, nets, referees, rink markings and more. 
+
+**Top Shelf** uses this technology to analyze game play and provide insightful suggestions on areas for improvement. 
+'''
+st.markdown(overview)

src/Hockey_Breeds.py

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+import streamlit as st
+
+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.subheader('Image Classification',
+             help='Proof of concept application')
+
+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.
+*Hockey Breeds* is a hockey slant on this common theme in Computer Vision educational materials.'''
+
+st.markdown(img_class)
+
+st.subheader("Hockey Image Classification")
+desc = '''This "Hockey Breeds" model was built using 50 hockey related images found on the web and in my own collection. I started with a pretrained *ResNet18* model (resnet18 is trained on *ImageNet*, a very large dataset with millions of images).  I fine tuned the model by labeling the hockey photos, then training using python (*Fast.ai* & *PyTorch* libraries).
+
+The total training time for this was approximately 5 minutes running on a low-end GPU. It’s impressive how accurate this quick / small model can be!'''
+
+st.markdown(desc)
+
+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 ")
+

src/Home.py

@@ -0,0 +1,26 @@
+import streamlit as st
+
+import logging
+
+st.set_page_config(page_title='TopShelf POC', layout="wide",
+                   page_icon="🥅")
+
+st.title('Welcome To Top Shelf :goal_net:',
+         help=':video_camera: + :ice_hockey_stick_and_puck: = :clipboard:')
+st.subheader('Artificial Intelligence for Hockey Coaches and Players',
+             help='Proof of concept application')
+
+overview = '''**Top Shelf** helps coaches and players analyze their gameplay, providing helpful suggestions on areas for improvement.  
+
+We're starting with a focus on ice hockey, however this same technology could apply to other "invasion" games and sports, for example lacrosse, basketball, soccer, etc.
+
+The special sauce behind **Top Shelf** is AI *Computer Vision*  technology that recognizes various hockey related objects in videos. 
+The foundation of the technology is an AI model that can recognize players, nets, referees, pucks, and rink areas. 
+
+**Top Shelf** uses this technology to analyze game play and provide insightful suggestions on areas for improvement. 
+'''
+st.markdown(overview)
+
+st.subheader('Getting Started')
+st.markdown('''We're currently in the training and testing phase of **Top Shelf** development.  This is a proof of concept application that friends of **Top Shelf** can use to help in development. 
+To help us understand how our *Computer Vision* model is working you can upload hockey pictures and then the app will display what hockey objects were found. ''')

src/app.py

@@ -0,0 +1,12 @@
+import streamlit as st
+
+import logging
+
+app = st.navigation(
+    {"App": [st.Page("Home.py", title="Home", icon=":material/home:"),
+             st.Page("About.py", icon="🥅")],
+     "Models": [st.Page("Hockey_Breeds.py", icon=":material/gradient:"),
+                st.Page("hockey_object_detection.py", title="Hockey Object Detection", icon=":material/filter_b_and_w:")]
+     }
+)
+app.run()