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### 1. Imports and class names setup ###
from model import create_vitB16_model
import torch
from typing import Tuple, Dict
from timeit import default_timer as timer
import gradio as gr
import os
class_names = []
# Open Cifar class names file and read each line into a list
with open('class_names.txt', 'r') as f:
class_names = [obj.strip() for obj in f]
### 2. Model and transforms preparation ###
model, model_transforms = create_vitB16_model(num_classes=len(class_names))
# Load save weights
model.load_state_dict(torch.load(f='09_pretrained_vit_feature_extractor_cifar_60_percent.pth',
map_location='cpu'))
# 3. Predict Function
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with vitB16
img = model_transforms(img).unsqueeze(dim=0)
# 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 probabilities
pred_logit = model(img)
pred_prob = torch.softmax(pred_logit, dim=1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {class_names[i]: float(pred_prob[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 = "Object Detection 🤖"
description = "A [vision Transformer B16 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html) computer vision model to classify 10 classes of Objects from the Cifar10 dataset. [Cifar10](https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html?highlight=cifar)"
article = "Created with 🤎 (and a mixture of mathematics, statistics, and tons of calculations 👩🏽🔬) by Arpit Vaghela [GitHub](https://github.com/magnifiques)"
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
demo = gr.Interface(fn=predict,
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=False, # print errors locally?
share=True) # generate a publically shareable URL
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