initial version

.gitignore

@@ -0,0 +1,3 @@
+
+*.jpg
+model.pkl

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/fastai_deeplearning.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="$USER_HOME$/mambaforge" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="$USER_HOME$/mambaforge" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/fastai_deeplearning.iml" filepath="$PROJECT_DIR$/.idea/fastai_deeplearning.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+from fastai.vision.core import PILImage
+from fastai.learner import load_learner
+
+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 = "Bear Classifier"
+description = ("A bear classifier trained on black/grizzly/teddy bear images downloaded from internet with fastai. "
+               "Created as a demo for Gradio and HuggingFace Spaces.")
+examples = ['black.jpg', 'grizzly.jpg', 'teddy.jpg']
+grif = gr.Interface(fn=predict,
+                    inputs=gr.inputs.Image(shape=(512, 512)),
+                    outputs=gr.outputs.Label(num_top_classes=3),
+                    title=title,
+                    description=description,
+                    examples=examples)
+grif.launch(share=True)