Final app creation

.gitignore

@@ -1 +1,3 @@
 .venv
+flagged/
+basset.jpg

app.py

@@ -1,7 +1,35 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# %% ../app.ipynb 1
+import sys
+import subprocess
+
+subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio==3.50'])
+subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'fastai'])
+subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'ipywidgets'])
+
+from fastai.vision.all import *
+import gradio as gr 
+import timm
+
+# %% ../app.ipynb 3
+learn = load_learner('model.pkl')
+
+# %% ../app.ipynb 5
+categories = learn.dls.vocab
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(fload, probs)))
+
+# %% ../app.ipynb 7
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['basset.jpg']
+
+# %% ../app.ipynb 8
+intf = gr.Interface(fn=classify_image, inputs = image, outputs=label)
+intf.launch(inline=False)