app.py

from fastai.vision.all import *
import gradio as gr
import glob
import timm
from timm.models import convnext
convnext_model = 'convnext_tiny_in22k'
model_architecture=timm.create_model(convnext_model)

import torch
class FastaiConvNext(torch.nn.Module):
    def __init__(self, original_model):
        super().__init__()
        self.features = original_model

    def forward(self, x):
        x = self.features(x)
        return x

model = FastaiConvNext(model_architecture)

#class Hook():
#  def hook_func(self, m, i, o): self.stored = o.detach().clone()

#learn = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True)

#categories = ('arbanasi', 'filibe', 'gjirokoster', 'iskodra', 'kula', 'kuzguncuk', 'larissa_ampelakia', 'mardin', 'ohrid', 'pristina', 'safranbolu', 'selanik', 'sozopol_suzebolu', 'tiran', 'varna')

learn = load_learner("convnext_mixup_0_33.pkl", cpu=True)
categories = ('arbanasi', 'filibe', 'gjirokoster', 'iskodra', 'kula', 'kuzguncuk', 'larissa_ampelakia', 'mardin', 'ohrid', 'pristina', 'safranbolu', 'selanik', 'sozopol_suzebolu', 'tiran', 'varna')
def classify_img(img):
  pred,idx,probs=learn.predict(img)
  return dict(zip(categories, map(float, probs)))

image=gr.inputs.Image(shape=(128,128))
label=gr.outputs.Label()
examples_=[]
for i in glob.glob("valid/**/*.jpg", recursive=True):
    examples_.append(i)

examples=["filibe-1-1.jpg",
          "ohrid-3-1.jpg",
          "varna-1-1.jpg"]


demo = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)

demo.launch(inline=False)