app.py

### 1. Imports and class names setup ###
import gradio as gr
import os 
import torch

from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Dict, Tuple

# Setup class names
class_names = ['pizza', 'steak', 'sushi']

### 2. Model and transforms preparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)

# Load saved weights

effnetb2.load_state_dict(torch.load(f="pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
                                    map_location=torch.device("cpu")))

### 3. Predict function ###

def predict(img) -> Tuple[Dict, 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.inference_mode():
    pred_logits = effnetb2(img)
    pred_probs = torch.softmax(pred_logits, dim = 1)
  # Create a prediction label and prediction probability dict
  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

  # Calculate pred time
  pred_time = round(timer() - start_time, 4)
  # Return pred dict and time
  return pred_labels_and_probs, pred_time

### 4. Gradio app ###

# Create title, description, and Article
title = "FoodVision Mini"
description = "An [EfficientNetB2 feature extractor]"
article = "Created on colab"


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

# Create the Gradio Demo
demo = gr.Interface(fn=predict, # maps inputs to outputs
                    inputs=gr.Image(type="pil"),
                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
                             gr.Number(label="Prediction time (s)")],
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)
# launch the demo
demo.launch(debug=False)