app.py

import gradio as gr 
import model_builder as mb 
from torchvision import transforms
import torch 

device = torch.device("cpu")

normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], 
        std=[0.229, 0.224, 0.225])

manual_transform = transforms.Compose([
    transforms.ToPILImage(), 
    transforms.Resize(size=(224, 224)), 
    transforms.ToTensor(), 
    normalize
])

class_names = ['Fresh Banana',
  'Fresh Lemon',
  'Fresh Lulo',
  'Fresh Mango',
  'Fresh Orange',
  'Fresh Strawberry',
  'Fresh Tamarillo',
  'Fresh Tomato',
  'Spoiled Banana',
  'Spoiled Lemon',
  'Spoiled Lulo',
  'Spoiled Mango',
  'Spoiled Orange',
  'Spoiled Strawberry',
  'Spoiled Tamarillo',
  'Spoiled Tomato']

model_0 = mb.create_model_baseline_effnetb0(out_feats=len(class_names), device=device)
model_0.load_state_dict(torch.load(f="models/effnetb0_fruitsvegs0_5_epochs.pt", map_location="cpu"))

def pred(img):
    model_0.eval()
    transformed = manual_transform(img).to(device)
    with torch.inference_mode():
        logits = model_0(transformed.unsqueeze(dim=0))
        pred = torch.softmax(logits, dim=-1)
    return f"prediction: {class_names[pred.argmax(dim=-1).item()]} | confidence: {pred.max():.3f}"

demo = gr.Interface(pred, gr.Image(), outputs="text")
demo.launch()