app.py

import gradio as gr
import os 
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from PIL import Image
from typing import Tuple, Dict

class_names = ['pizza', 'steak', 'sushi']
effnetb2, effnetb2_transforms = create_effnetb2_model(3, 42)
# Load save weights:
effnetb2.load_state_dict(
  torch.load(f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_precent.pth',
  map_location=torch.device('cpu')
  )
)

### Prediction function: EffNetB2 ###

def predict(img: Image.Image) -> Tuple[Dict[str, float], float]:
  # Start a timer
  start_time = timer()
  # Transform the input image for use with EffNetB2
  img = effnetb2_transforms(img).unsqueeze(0)
  # Put model into eval mode, make prediction
  effnetb2.eval()
  with torch.no_grad():
    pred_probs = torch.softmax(effnetb2(img), dim=1)
  # Create a prediction labal and prediction probability dictionary
  pred_labels_and_probs = {class_names[i]:float(pred_probs[0][i]) for i in range(len(class_names))}
  # Calculated pred time
  end_time = timer()
  pred_time = round(end_time - start_time, 4)
  # Return pred dict and pred time
  return pred_labels_and_probs, pred_time


### Gradio app ###

# Create title, description and article strings
title = "FoodVision Mini 🍕🥩🍣"
description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)'
article = 'Created by Arik Kodenzov with Pytorch model deployment'

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
                    inputs=gr.Image(type="pil"), # what are the inputs?
                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                    # Create examples list from "examples/" directory
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()