app.py

import gradio as gr
from PIL import Image
import numpy as np
import json
from torchvision import models
import torch.nn.functional as F
from torch import nn
import torch
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from huggingface_hub import HfApi

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def load_checkpoint(filepath):
    """Builds PyTorch Model from saved model
    Returns built model

    Arguments: string, filepath of saved PyTorch model
    """

    # Load pretrained weights
    weights = "IMAGENET1K_V1"

    # Load model using pretrained weights
    model = models.maxvit_t(weights=weights)

    # Load checkpoint
    checkpoint = torch.load(filepath, map_location=torch.device("cpu"))

    # Get new classifier from checkpoint
    new_classifier = checkpoint["classifier"]

    # Replace pretrained model output classifier layer[5] with newly created classifier
    model.classifier[5] = new_classifier

    # Load model weights
    model.load_state_dict(checkpoint["state_dict"])

    # Load class to index mapping
    model.class_to_idx = checkpoint["class_to_idx"]

    return model


class Network(nn.Module):
    def __init__(self, input_size, hidden_layers, output_size=102, drop_p=0.2):
        """Builds a feedforward network with arbitrary hidden layers.

        Arguments
        ---------
        input_size: integer, size of the input layer
        output_size: integer, size of the output layer
        hidden_layers: list of integers, the sizes of the hidden layers
        drop_p: float, dropout probability
        """
        super().__init__()

        self.drop_p = drop_p

        # Input to a hidden layer
        self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])

        # Add a variable number of more hidden layers
        layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])
        self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])

        self.output = nn.Linear(hidden_layers[-1], output_size)

        print(
            f"\nNumber of layers: {len(self.hidden_layers)}"
            f"\nNumber of units in layers:{hidden_layers}"
        )

    def forward(self, x):
        """Forward pass through the network, returns the output logits"""

        for each in self.hidden_layers:
            x = F.relu(each(x))
            x = F.dropout(x, self.drop_p)
        x = self.output(x)

        return F.log_softmax(x, dim=1)
    
    def process_image(img_path):
        """Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns a Numpy array

        Arguments
        ---------
        image: path of the image to be processed
        """
    inp = Image.open(img_path)
    exif = inp.getexif()

    # Get image size
    w, h = inp.size

    # Create inference directory for prediction
    os.makedirs("inference", exist_ok=True)

    # Remove non alphanumeric characters
    image_path = re.sub(r"\W+", "", img_path)
    image_path = str(datetime.now()) + ".png"

    # Join to directory path
    inf_image = os.path.join("inference", img_path)

    # Use repo for inference
    inp.save(inf_image, exif=exif)
    HfApi().upload_file(
        path_or_fileobj=inf_image,
        path_in_repo=image_path,
        repo_id="DanielPFlorian/flower-image-classifier",
        repo_type="dataset",
        token=HF_TOKEN,
    )

    # resize image so shortest side is 256 preserving aspect ratio
    if w > h:
        inp.thumbnail((10000, 256))
    elif h > w:
        inp.thumbnail((256, 10000))
    else:
        inp.thumbnail((256, 256))

    # crop center 224x224
    w, h = inp.size
    left = (w - 224) // 2
    top = (h - 224) // 2
    right = (w + 224) // 2
    bottom = (h + 224) // 2
    image = inp.crop((left, top, right, bottom))

    # Convert pil image to numpy array and scale color channels to [0, 1]
    np_image = np.array(image) / 255

    # Normalize image
    mean = np.array([0.485, 0.456, 0.406])  # Mean
    std = np.array([0.229, 0.224, 0.225])  # Standard deviation
    np_image = (np_image - mean) / std

    # Move color channels to first dimension
    np_image = np_image.transpose((2, 0, 1))

    return np_image

def predict(image_path, model=model, category_names=cat_to_name, topk=5):
    """Predict the class (or classes) of an image using a trained deep learning model.
    Arguments
    ---------
    image_path: path of the image to be processed
    model: model to be used for prediction
    topk: number of top predicted classes to return
    """
    # Process image function
    image = process_image(image_path)

    # Convert image to float tensor with batch size of 1
    image = torch.as_tensor(image).view((1, 3, 224, 224)).float()

    # Set model to evaluation mode/ inference mode
    model.eval()

    # Turn off gradients to speed up this part
    with torch.no_grad():
        # Forward Pass. Ouputs log probabilities of classes
        log_ps = model.forward(image)

        # Exponential of log probabilities for each class
        ps = torch.exp(log_ps)

        # Get top k predictions. Returns probabilities and class indexes
        top_probs, idx = ps.topk(topk, dim=1)

        # Convert tensors to lists. Index[0] returns unnested List
        top_probs, idx = top_probs.tolist()[0], idx.tolist()[0]

        # Convert top_probs to percentages
        percentages = [round(prob * 100.00, 2) for prob in top_probs]

        # Converts class_labels:indexes to indexes:class_labels
        idx_to_class = {val: key for key, val in model.class_to_idx.items()}

        # get class labels from indexes
        top_labels = [idx_to_class[lab] for lab in idx]

        # Get names from labels
        if category_names:
            top_labels = [category_names[str(lab)] for lab in top_labels]

        # Plot Functionality

        image = Image.open(image_path)
        fig, (ax1, ax2) = plt.subplots(ncols=2)
        ax1.imshow(image)
        ax1.axis("off")
        ax2.barh(np.arange(len(top_labels)), percentages)
        asp = np.diff(ax2.get_xlim())[0] / np.diff(ax2.get_ylim())[0]
        ax2.set_aspect(asp)
        ax2.set_yticks(np.arange(len(top_labels)))
        ax2.set_yticklabels(top_labels)
        ax2.invert_yaxis()
        ax2.xaxis.set_major_formatter(ticker.PercentFormatter())
        plt.tight_layout()
        ax2.set_title("Class Probability")
        plt.show()

        return fig
    
gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload a flower image", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=5),
    title="What kind of flower is this?",
).launch()