Add application files

antelopeClassifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fc1f1326b6f9e729437e175d734b6477608c474f58ccab16837c12436c472fa
+size 47372647

app.py

@@ -0,0 +1,47 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: antelopeInference.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% antelopeInference.ipynb 3
+#Imports
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+#hide
+#[ -e /content ] 
+#pip install -Uqq fastbook
+
+# import fastbook
+# fastbook.setup_book()
+
+#hide
+# from fastbook import *
+# from fastai.vision.widgets import *
+
+# pip install fastai
+
+
+# %% antelopeInference.ipynb 4
+from fastai.vision.all import *
+import gradio as gr
+
+# %% antelopeInference.ipynb 8
+learn = load_learner('antelopeClassifier.pkl')
+
+categories = ('Eland', 'Greater Kudu', 'Hartebeest', 'Oryx', 'Defassa Waterbuck', 'Sitatunga', 'Impala ', 'The lesser Kudu', 'Grant’s Gazelle','Reedbuck','Uganda Kob','Forest duiker','Harvery’s red duiker', 'Blue duiker', 'Peter’s duiker','Black fronted duiker','Grey duiker','Oribi','Klipspringer','Guenther’s')
+
+# %% antelopeInference.ipynb 26
+def classify_image(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories, map(float,probs)))
+
+# %% antelopeInference.ipynb 31
+#create gradio interface
+image = gr.inputs.Image(shape=(128,128))
+label = gr.outputs.Label()
+examples = ['antelopeA.jpeg', 'antelopeB.jpeg', 'antelopeC.jpeg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples )
+intf.launch(inline=False)

requirements.txt

@@ -0,0 +1,4 @@
+torch
+fastai
+numpy
+pandas