Create app.py

app.py

@@ -0,0 +1,52 @@
+from fasthtml.common import *
+from fasthtml.components import *
+from fastai.vision.all import *
+from PIL import Image
+import io
+
+app, rt = fast_app()
+learn = load_learner("model.pkl")
+
+def classify_image(img):
+    char,idx,probs = learn.predict(img)
+    im = Image.open(img).to_thumb(256,256)    
+    name = " ".join([s.capitalize() for s in (char).split("_")])
+    return name, idx, probs
+
+@rt('/')
+def index():
+    return Titled("Chair vs Lamp Classifier",
+        Div(
+            H2("Example Images"),
+            Div(
+                Img(src="chair1.jpg", hx_trigger="click", hx_get="/classify", hx_target="#result"),
+                Img(src="chair2.jpg", hx_trigger="click", hx_get="/classify", hx_target="#result"),
+                Img(src="lamp1.jpg", hx_trigger="click", hx_get="/classify", hx_target="#result"),
+                Img(src="lamp2.jpg", hx_trigger="click", hx_get="/classify", hx_target="#result"),
+                cls="flex flex-wrap justify-center gap-4"
+            ),
+            H2("Upload an Image"),
+            Button("Upload Image", hx_post="/upload", hx_target="#result"),
+            Div(id="result")
+        )
+    )
+
+@rt('/classify')
+def classify(img_file: UploadFile):
+    img_bytes = img_file.files['image'].read()
+    img = Image.open(io.BytesIO(img_bytes))
+    
+    name, idx, probs = classify_image(img)
+    return Div(Div(f"This is {name}."),
+               Div(f"Probability it's {name}: {probs[idx]:.4f}"))
+    
+@rt('/upload', methods=['POST'])
+def upload(img_file: UploadFile):
+    img_bytes = img_file.files['image'].read()
+    img = Image.open(io.BytesIO(img_bytes))
+    
+    name, idx, probs = classify_image(img)    
+    return Div(Div(f"This is {name}."),
+               Div(f"Probability it's {name}: {probs[idx]:.4f}"))
+
+serve()