app.py

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

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

from helper import load_model

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

# 2. Model generation and weight
model = load_model("models/20230727-13521690480331-all-images.h5")


# 3. Prefict function
# Define image size
IMG_SIZE = 224


def process_image(image_path):
    """
    Takes an image file path and turns it into a Tensor.
    """
    # Read in image file
    image = tf.io.read_file(image_path)
    # Turn the jpeg image into numerical Tensor with 3 colour channels (Red, Green, Blue)
    image = tf.image.decode_jpeg(image, channels=3)
    # Convert the colour channel values from 0-225 values to 0-1 values
    image = tf.image.convert_image_dtype(image, tf.float32)
    # Resize the image to our desired size (224, 244)
    image = tf.image.resize(image, size=[IMG_SIZE, IMG_SIZE])
    return image


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 = process_image(img)

    img = tf.expand_dims(img, axis=0)

    # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
    pred_probs = model(img)

    # 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 = {
        unique_breeds[i]: float(pred_probs[0][i]) for i in range(len(unique_breeds))
    }

    # 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 = "120 Dog Breed Vision classifier 🐶🐩🐕🐕‍🦺"
description = (
    "An mobilenet feature extractor computer vision model to classify 120 dog breeds."
)
article = "Modele from [mobilenet](https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5)."
example_list = example_list = [["examples/" + str(p)] for p in os.listdir("examples/")]

# 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(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
    examples=example_list,
    title=title,
    description=description,
    article=article,
)

# Launch the demo!
demo.launch(debug=False)