app.py

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

from model import create_effnet_b2_model
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
with open("class_names.txt", "r") as f:
  class_names = [food.strip() for food in f.readlines()]

### 2. Model and transforms preparation ###
effnetb2, effnetb2_transforms = create_effnet_b2_model(num_classes=101)

# Load save weights
effnetb2.load_state_dict(torch.load(
    f='09_pretrained_effnetb2_feature_extractor_food101_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 timage for use with EffNetB2
  img = effnetb2_transforms(img).unsqueeze(dim=0) # add batch dimension

  # Put model into eval mode, make prediction
  effnetb2.eval()
  with torch.inference_mode():
    # Pass transformed image through the model and turn prediciton logits into probabilities
    pred_probs = torch.softmax(effnetb2(img), dim=1)

  # Create a prediciton label and predicition probability dictionary
  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 pred time
  return pred_labels_and_probs, pred_time

### 4. Gradio app ###

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

# Create title, description and article
title = "FoodVision Big - 🥟"
description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images [101 classes of food from the Food101 dataset](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#11-turning-our-foodvision-big-model-into-a-deployable-app)."

demo = gr.Interface(
    fn=predict, # maps inputs to outputs,
    inputs = gr.Image(type="pil"),
    outputs = [gr.Label(num_top_classes=5, label = "Predicitons"),
               gr.Number(label= "Prediction time (s)")],
    examples=example_list,
    title=title,
    description=description,
    article=article)

# Launch the demo
demo.launch()