app.py

### 1. Imports

import gradio as gr
import os
import torch

from PIL import Image

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

### 2. Model and transforms preparation ###

# Create model_alexnet
model_alexnet, transforms = create_model_alexnet( num_classes=2)

# Load saved weights
model_alexnet.load_state_dict(torch.load(f="cat_dog_classifier.pth", map_location=torch.device("cpu")))  # load to CPU

### 3. Predict function ###

# Create predict function
def predict(img):

    # Start the timer
    start_time = timer()

    model_alexnet.eval()

    # Reading the image and size transformation
    features = Image.open(img)
    img = transforms(features).unsqueeze(0)

    with torch.inference_mode():
      output = model_alexnet(img)
    _, predicted = torch.max(output, 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 = 'Cat' if predicted.item() ==1  else 'Dog'

    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)

    # Return the prediction dictionary and prediction time
    return pred_labels, pred_time


### 4. Gradio app ###

import gradio as gr

# Create title, description and article strings
title = "Classification Demo"
description = "Cat/Dog classification - Transfer Learning "

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

# Launch the demo!
demo.launch()