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 Tuple, Dict

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

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

#Load the saved weights
effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
                         map_location=torch.device("cpu") # Load the model to the CPU
)
)

### 3. Predict function
def predict(img) -> Tuple [dict, float]:
  # Start a timer
  start_time = timer()

  # TRansform the input image
  transformed_image = effnetb2_transforms(img).unsqueeze(dim=0)

  # Put model into eval mode, make prediction
  effnetb2.eval()
  with torch.inference_mode():
    logits = effnetb2(transformed_image)
    preds = torch.softmax(logits, dim=1)

  # Create a prediction label and prediction probability dictionary
  label = class_names[torch.argmax(preds)]
  pred_labels_and_probs = {class_names[i]:float(preds[0][i]) for i in range(len(class_names))}

  # Calaulate pred time
  end_time = timer()
  duration = round(end_time - start_time, 4)

  # retrun pred dict and pred time
  return pred_labels_and_probs, duration


# Create title, description and article
title = "Foodvision mini"
description="An EfficientNetB2 feature extractor computer vision model to classify images as pizza, steak or sushi."
article = "Created at 09. PyTorch model deployment ZTM course"

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

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

# Launch the demo
demo.launch(debug=False) # No debug on Huggingface