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import gradio as gr
import os
import torch
from model import create_vit
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
with open("class_names.txt", "r") as f:
class_names = [name.strip() for name in f.readlines()]
### Model and transforms preparation ###
# Create model and transforms
model, _, _, transforms = create_vit(output_shape=len(class_names), classes=class_names)
# Load saved weights
model.load_state_dict(
torch.load(f="vit.pth",
map_location=torch.device("cpu")) # load to CPU
)
### Predict function ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with the model
img = transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
# Put model into eval mode, make prediction
model.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probaiblities
pred_probs = torch.softmax(model(img).logits, 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 ###
# Create title, description and article
title = "A ViT cifar10 Classifier"
description = "An [ViT feature extractor](https://huggingface.co./google/vit-base-patch16-224) computer vision model to classify images on the [10 classes of the cifar10 dataset](https://huggingface.co./datasets/cifar10). [Source Code Found Here](https://colab.research.google.com/drive/1j4NbiMpCqmXN1xw9e2_r77gMdr3WpMnO?usp=drive_link)"
article = "Built with [Gradio](https://github.com/gradio-app/gradio) and [PyTorch](https://pytorch.org/). [Source Code Found Here](https://colab.research.google.com/drive/1j4NbiMpCqmXN1xw9e2_r77gMdr3WpMnO?usp=drive_link)"
# Create example list
example_list = [["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=5, label="Predictions"),
gr.Number(label="Prediction time (s)")],
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo
demo.launch()
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