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

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


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

# Load save weights
effnetb2.load_state_dict(
    torch.load(
        f = "17_pretrained_effnetb2_20_percent.pth",
        map_location = torch.device("cpu")  # load the model to the cpu because model was trained on gpu.
        )
)
    

### 3. Predict function (predict()) ###
def predict(img):
    # Start a timer
    start_time = timer()
    
    # Transform the input image for use with EffNetB2
    img = effnetb2_transforms(img).unsqueeze(dim = 0)  # unsqueeze = add batch dimension on 0th index.
    
    # Put model into eval mode, make prediction
    effnetb2.eval()
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probabilities.
        pred_probs = torch.softmax(effnetb2(img), dim = 1)
    
    # Create a prediction label and prediction probability dictionary
    pred_labels_and_probs = {class_names[i]: float(pred_probs[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 dict and pred time
    return pred_labels_and_probs, pred_time



### 4. Gradio app - our Gradio interface + launch command ###
# 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 17-Pytorch-Model-Deployment"

# Create example_list
example_list = [[os.path.join("examples", example)] for example in os.listdir("examples")]

# Create the Gradio demo
demo = gr.Interface(fn = predict, # maps inputs to outputs
                    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
                    )

# Launch the demo.
demo.launch(debug = True,  # Print erros locally
           )