Initial model

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

.gitignore

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+gradio_queue.db

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('export.pkl')
+
+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))}
+
+title = "Emotion Classifier"
+description = "An emotion classifier trained on AffectNet with fastai. Identifies emotions of anger, contempt, disgust, fear, happy, neutral, surprise, and sad."
+
+examples = ["happy.jpg", "neutral.jpg", "sad.jpg", "angry.jpg", "contempt.jpg", "fear.jpg", "surprise.jpg", "disgust.jpg"]
+
+gr.Interface(
+        fn=predict,
+        inputs=gr.inputs.Image(shape=(224, 224)),
+        outputs=gr.outputs.Label(num_top_classes=3),
+        title=title,
+        description=description,
+        examples=examples,
+        enable_queue=True,
+).launch()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc5eb70037524e6ec9d011ba612e208a9ac95ed9a9feec0a9d0d84ffac3e3e5
+size 242786647

requirements.txt

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+fastai
+scikit-image