7 PM – AI / ML – Shawn

Today We Did

1. Continued streamlit app

Homework

1. Try writing the rest of the server.py as shown:
   import streamlit as stimport urllib.request
   from fastai.vision.all import *

   def label_func(f): return f[0].isupper()
   # Load the pre-trained model
   model = load_learner(‘my_model.pkl’)

   # Define a function to make predictions
   def predict(image):
   img = PILImage.create(image) # Use PILImage.create to open the image
   pred_class, pred_idx, outputs = model.predict(img)
   likelihood_is_cat = outputs[1].item()
   if likelihood_is_cat > 0.9:
   return “Cat”
   elif likelihood_is_cat < 0.1:
   return “Dog”
   else:
   return “Not sure… try another picture!”

   # Streamlit app title and description
   st.title(“Cat vs. Dog Classifier”)
   st.write(“Upload an image, and I’ll tell you whether it’s a cat or a dog!”)

   # File uploader widget
   uploaded_file = st.file_uploader(“Choose an image…”, type=[“jpg”, “png”, “jpeg”])

   if uploaded_file is not None:
   # Display the uploaded image
   st.image(uploaded_file, caption=”Uploaded Image”, use_column_width=True)

   # Make predictions on the uploaded image
   if st.button(“Predict”):
   prediction = predict(uploaded_file)
   st.write(prediction)

   # Add a footer
   st.text(“Built with Streamlit and Fastai”)

2. Run streamlit run server.py in the terminal to see the website in action! For windows users, it should be py -m streamlit run server.py

Email me at szhuang@ayclogic.com if you have any questions.