app.py

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

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

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


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

# Load save weights
effnetb2.load_state_dict(
    torch.load(
        f="10_pretrained_effnetb2_20_percent.pth",
        map_location=torch.device('cpu')           # ensure it loads in cpu
    )
)


### 3. Predict function  ###
def predict(img) -> Tuple[Dict, float]:
    # Start a timer
    start_time = timer()
        
    # Transform the input image for use with EffNetB2
    transformed_image = effnetb2_transforms(img).unsqueeze(0)  # Adding batch_dim

    # Put model into eval mode, make prediction
    effnetb2.eval()
    with torch.inference_mode():
        pred_prob = torch.softmax(effnetb2(transformed_image), dim=1)
    
    # Create a prediction label and prediction probability dict
    pred_labels_and_probs = {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))}
    
    # Calculate pred time
    end_time = timer()
    pred_time = round(end_time - start_time, 4)
    
    return pred_labels_and_probs, pred_time



### 4. Gradio App

# Create title, description and article
title = "FoodVision Mini 🥩🍕🍥"
description = "An EfficientNetB2 feature extractor CV model to classify food"
article = "Created at 10. PyTorch Model Deployment"

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

# Create the Gradio Demo
demo = gr.Interface(fn=predict,  #maps input to output
                   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)

demo.launch(debug=False)