yay!

app.py

@@ -3,6 +3,7 @@ import sys
 import pathlib
 from pathlib import Path
 from fastai.vision.all import load_learner, PILImage
+from torchvision.models import resnet18
 import gradio
 from gradio import Interface, Image, Label
 import logging
@@ -10,11 +11,16 @@ logging.basicConfig(level=logging.INFO)
 def custom_resnet_splitter(model):
     resnet = model[0]
     return [params(resnet[0]), params(resnet[1]), params(resnet[4]), params(resnet[5]), params(resnet[6]), params(resnet[7]), params(model[1])]
-
 def predict_parrot_species(image):
     try:
-        model_path = Path('parrotclass.pkl')  # Use Path instead of a string
-        learn_inf = load_learner(model_path)
+        model_path = Path('parrotclass_state_dict.pth')
+        state_dict = torch.load(model_path)
+        
+        # Load the model architecture
+        model = resnet18(num_classes=len(parrot_types))
+        model.load_state_dict(state_dict)
+        learn_inf = Learner(dls, model)
+
         pred, _, _ = learn_inf.predict(image)
         return pred
     except Exception as e:
@@ -23,6 +29,7 @@ def predict_parrot_species(image):
 
 
 
+
 input_image = Image(shape=(224, 224))
 output_label = Label()
 

importpickle.py

@@ -0,0 +1,14 @@
+import pickle
+import torch
+from pathlib import Path
+
+def convert_pickle_path(model_path, output_path):
+    with open(model_path, 'rb') as f:
+        model = pickle.load(f)
+
+    torch.save(model, output_path, pickle_module=pickle, pickle_protocol=2, _use_new_zipfile_serialization=False)
+
+if __name__ == '__main__':
+    input_model_path = Path('parrotclass.pkl')
+    output_model_path = Path('parrotclass_converted.pkl')
+    convert_pickle_path(input_model_path, output_model_path)

parrotclass_state_dict.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03d128964b45f9a76cc9e32efdfc853ef2c94c882831117b96041ac7f0c7b99f
+size 46917395