torch

app.py

@@ -6,7 +6,17 @@ import platform
 plt = platform.system()
 if plt == 'Windows': pathlib.PosixPath=pathlib.WindowsPath
 from pathlib import Path
-path_to_model=Path('export.pkl')
+import torch
+def load_learner_safe(fname, map_location=None, pickle_module=None):
+    # This is to ensure it does not attempt to instantiate a WindowsPath
+        with open(fname, 'rb') as f:
+        # Load the model manually using torch.load and avoid WindowsPath instantiation
+        model1 = torch.load(f, map_location=map_location, pickle_module=pickle_module)
+        return model1
+
+# Use this safe method to load the learner model
+learn = load_learner_safe('export.pkl')
+
 # %% Untitled.ipynb 3
 def predict(img):
     labels=learn.dls.vocab
@@ -15,7 +25,7 @@ def predict(img):
     return {labels[i]:float(probs[i]) for i in range (len(labels))}
 
 # %% Untitled.ipynb 5
-learn=load_learner(path_to_model)
+learn = load_learner_safe('export.pkl')
 
 # %% Untitled.ipynb 8
 examples = ["covid-19.jpg", "normal.jpg", "viral pneumonia.jpg"]

export.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0547e427005dca86ae3139e4b62758c6ebdd2276bd480c0a8e75c1e2e81b399a
+oid sha256:23e72cbae2206747d84c84e0250fb10b4653a9dc58599e581465c5e62111b87d
 size 242096000