app.py

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, TypedDict

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

# Create EffNetB2 model instance and transform
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))

# Load model weights
effnetb2.load_state_dict(
    torch.load(
        os.path.join("models", "09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth"),
        map_location=torch.device("cpu")
        )
    )

# 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_probs = torch.softmax(effnetb2(img), dim=-1)

  # Create a prediction label and predcition probability
  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(class_names)}

  # Calculate pred time and pred dict
  pred_time = round(timer() - start_time, 5)

  return pred_labels_and_probs, pred_time

# Gradio app
# Create title, description and article strings
title = "FoodVision Mini 🍕🥩🍣"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."

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

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

# Launch the demo!
demo.launch(debug=False,
            share=True)