Let's see how this goes

app.py

@@ -1,7 +1,27 @@
+__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+from fastai.vision.all import *
 import gradio as gr
+import timm
+
+# Some magic according to https://forums.fast.ai/t/lesson-2-official-topic/96033/376?page=17
+
+import sys
+
+# Upload your model
+learn = load_learner('niclangf-bird-labeler.pkl')
+
+categories = learn.dls.vocab
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+image = gr.Image()
+label = gr.Label()
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Upload your own images and link them
+examples = ['basset.jpg']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()

requirements.txt

@@ -0,0 +1 @@
+gradio