Added the model pkl, the example jpg, and evapp.py to the folder

.gitattributes

@@ -32,3 +32,4 @@ 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
+*.jpg filter=lfs diff=lfs merge=lfs -text

evapp.py

@@ -0,0 +1,38 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: evclassifierapp.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'labels', 'title', 'description', 'interpretation', 'examples', 'enable_queue', 'intf', 'classify_image']
+
+# %% evclassifierapp.ipynb 1
+from fastai.vision.all import *
+
+# %% evclassifierapp.ipynb 2
+learn = load_learner('export.pkl')
+
+# %% evclassifierapp.ipynb 4
+labels = learn.dls.vocab
+
+def classify_image(img):
+    img = PILImage.create(img)
+    pred,idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# %% evclassifierapp.ipynb 5
+title = "EV Car Classifier"
+description = "This model will recognise the EV car in question"
+interpretation='default'
+examples = ['bmw ix.jpg','merc eqc.jpg','tesla model 3.jpg']
+enable_queue=True
+
+# %% evclassifierapp.ipynb 6
+import gradio as gr
+
+intf = gr.Interface(fn=classify_image,
+             inputs=gr.inputs.Image(shape=(512, 512)),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             examples=examples,
+             interpretation=interpretation,
+             enable_queue=enable_queue)
+intf.launch()

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8f6b1327e9cc02ee81185305acf46fc643e1eef2b3b71338e6b58d87ae3c05c
+size 46956766