app.py

import timm
import torch
import torch.nn.functional as nnf
import gradio as gr
import numpy as np
import json

class GELU(torch.nn.Module):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return torch.nn.functional.gelu(input)
torch.nn.modules.activation.GELU = GELU

model = torch.load("/home/user/app/trained_model_2e960a3n_model.pkl",map_location=torch.device('cpu')) 


with open('/home/user/app/val.json', 'r') as handle:
    parsed = json.load(handle)
    
classes = []
for i in range(len(parsed["categories"])):
  if parsed["categories"][i]['supercategory'] == 'Plants':
    classes.append(parsed["categories"][i]['name'])
    
classes = set(classes)

classes = list(classes)
classes.sort()

labels = classes

def classify_image(inp):
  print(inp.shape)
  inp = inp.astype(np.uint8).reshape((-1, 3, 224, 224))
  print(inp.shape)
  inp = torch.from_numpy(inp).float()
  #confidences = model(inp)

  preds = nnf.softmax(model(inp).data[0], dim=0)
  preds = [pred.cpu() for pred in preds]
  preds = [pred.detach().numpy() for pred in preds]

  #confidences_dict = {classes[i]: float(confidences.data[0][i]) for i in range(len(confidences.data[0]))}
  confidences_dict = {classes[i]: float(preds[i]) for i in range(len(preds))}

  return confidences_dict

gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(224, 224)),
             outputs=gr.Label(num_top_classes=3)).launch(debug = True)