try resetting to older version of fastai

app.py

@@ -20,38 +20,13 @@ import pandas as pd
 from PIL import Image
 import matplotlib.pyplot as plt
 
+import fastai==1.0.61
 from fastai.vision import *
 
 import gradio as gr
-from fastai.vision.all import *
+# from fastai.vision.all import *
 import skimage
 
-class FlattenedLoss():
-    "Same as `func`, but flattens input and target."
-    def __init__(self, func, *args, axis:int=-1, floatify:bool=False, is_2d:bool=True, **kwargs):
-        self.func,self.axis,self.floatify,self.is_2d = func(*args,**kwargs),axis,floatify,is_2d
-        functools.update_wrapper(self, self.func)
-
-    def __repr__(self): return f"FlattenedLoss of {self.func}"
-    @property
-    def reduction(self): return self.func.reduction
-    @reduction.setter
-    def reduction(self, v): self.func.reduction = v
-
-    def __call__(self, input:Tensor, target:Tensor, **kwargs)->Rank0Tensor:
-        input = input.transpose(self.axis,-1).contiguous()
-        target = target.transpose(self.axis,-1).contiguous()
-        if self.floatify: target = target.float()
-            
-        # Label smoothing experiment
-        target = (target * 0.9 + 0.05)
-        target[:,0] = 1
-
-        input = input.view(-1,input.shape[-1]) if self.is_2d else input.view(-1)
-        return self.func.__call__(input, target.view(-1), **kwargs)
-
-learn = load_learner('stage2-resnet50-size256-export.pkl')
-
 labels = learn.dls.vocab
 def predict(img):
     img = PILImage.create(img)