create catvsdog.py

catvsdog.py

@@ -0,0 +1,28 @@
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+from fastai.vision.all import *
+import gradio as gr
+
+share = True
+def is_cat(x): return x[0].isupper()
+
+learn = load_learner('/kaggle/input/cvd1/pytorch/default/1/model.pkl')
+
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image = "image"
+label = "label"
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
+intf.launch(inline=False, share = True)