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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+whale.jpeg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+# load model
+learn = load_learner('model.pkl')
+
+# prediction function for model
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# gradio app
+title = "Whale model"
+description = "Demo Gradio app for a ML image classifier using fast.ai"
+examples = ['whale.jpeg']
+interpretation='default'  # so users can understand what parts of the input are responsible for the output
+enable_queue=True  # for lots of traffic
+
+app = gr.Interface(fn=predict,inputs="image",outputs="label",title=title,description=description,examples=examples, interpretation=interpretation,enable_queue=enable_queue)
+app.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d08a5327faaeb852473a3580f10bfa79d857dfb528c25bb9558795ac82127f2
+size 46954649

requirements.txt

@@ -0,0 +1,3 @@
+fastai
+scikit-image
+gradio