built a simple streamlit app and updated dependencies

Makefile

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+install:
+	pip install --upgrade pip && \
+	pip install -r requirements.txt
+
+#format:
+#	python -m black *.py
+
+# lint:
+# 	pylint --disable=R,C *.py
+
+#test:
+#	python -m pytest -vv -----------------

app.py

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+import streamlit as st
+from fastai.vision.all import *
+
+def is_cat(x):
+    return x[0].isupper() 
+
+model = load_learner('model.pkl') 
+
+categories = ('Dog', 'Cat')
+
+def clasify_image(img):
+    img = PILImage.create(img)
+    pred,idx,probs = model.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+
+def main():
+    
+    st.title("Dog or Cat predictor")
+    
+    #The input is an image
+    image = st.file_uploader("Upload an image", "jpg")
+    # Display the image once it has been uploaded
+    if image:
+        disp = Image.open(image)
+        st.image(disp, width=150)
+    # Make the prediction
+    if st.button("Predict", use_container_width=True):
+        result = clasify_image(image)
+        for key, value in result.items():
+            st.progress(value, f"Probabiity that its a {key} is {value:.15f}%")
+    
+    #There should be examples you can pick from to put into the input interface
+    images = [
+        Image.open('photos/cat/001.jpg'),
+        Image.open('photos/dog/006.jpg'),
+        Image.open('photos/dog/007.jpg'),
+        Image.open('photos/cat/003.jpg')
+        
+    ]
+      
+    col1, col2, col3, col4 = st.columns(4)
+    
+    col1.image(images[0], width=150)
+    col2.image(images[1], width=150)
+    col3.image(images[2], width=150)
+    col4.image(images[3], width=150)
+        
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit==1.28.0
+fastai==2.7.13
+git-lfs==1.6