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 preparation ###

# Create EffNetB2 model
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=3, # len(class_names) would also work
)

# Load 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 to CPU
    )
)

### 3. Predict function ###

# Create predict function
def predict(img) -> Tuple[Dict, float]:
    """Transforms and performs a prediction on img and returns prediction and time taken.
    """
    # Start the timer
    start_time = timer()
    
    # Transform the target image and add a batch dimension
    img = effnetb2_transforms(img).unsqueeze(0)
    
    # Put model into evaluation mode and turn on inference mode
    effnetb2.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(effnetb2(img), dim=1)
    
    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
    
    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)
    
    # Return the prediction dictionary and prediction time 
    return pred_labels_and_probs, pred_time

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

# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
                    inputs=gr.Image(type="pil"), # what are the inputs?
                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                    # Create examples list from "examples/" directory
                    examples=example_list, 
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()
# 1. Imports and class name
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 name
class_names = ['pizza', 'steak', 'sushi']

# 2. Model and transforms preparation

# create effnetb2 model
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)

# load saved weights
effnetb2.load_state_dict(
    torch.load(f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent',
               map_location = torch.device('cpu')
              )
)

# 3. predict function

# create predict function
def predict(img) -> Tuple[Dict, float]:
    """
    Transforms and performs a prediction on img and returns prediction and time taken.
    """
    # start the timer
    start_time = timer()

    # transform the target img and add a batch dimension
    img = effnetb2_transforms(img).unsqueeze(dim=0)

    # put model into evaluation mode and turn on inference mode
    effnetb2.eval()
    with torch.inference_mode():
        # pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(effnetb2(img), dim=1)
    # create a prediction label and prediction probability dictionary for each prediction class
    # this is the required format for Gradio's output
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][1]) for i in range(len(class_names))}
    
    # end time
    end_time = timer()

    # calculate the prediction time
    pred_time = round(end_time - start_time, 5)

    return pred_labels_and_probs, pred_time

# 4. Gradio App

# Create titme, 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 = "Learned from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."

# create examples list from 'examples/' directory
example_list = [[f'examples/{example}'] for example 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='Prediction'),
                            gr.Number(label='Prediction time(s)')],
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# launch the demo!
demo.launch()