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import gradio as gr | |
import os | |
import torch | |
from model import create_effnetb2 | |
from timeit import default_timer as timer | |
def main(): | |
# # Device agnostic | |
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
# # Setup class names | |
class_names = ["pizza", "steak", "sushi"] | |
# # Create model | |
effnetb2, effnetb2_transforms = create_effnetb2( | |
out_features=len(class_names), | |
device=device) | |
effnetb2.load_state_dict(torch.load( | |
f="effnetb2.pth", | |
map_location=torch.device(device))) | |
def predict(img): | |
""" | |
Transforms and performs a prediction on img | |
Returns prediction and time taken. | |
""" | |
start_time = timer() | |
transformed_img = effnetb2_transforms(img).unsqueeze(0).to(device) | |
effnetb2.to(device) | |
effnetb2.eval() | |
with torch.inference_mode(): | |
pred_logit = effnetb2(transformed_img) | |
pred_prob = torch.softmax(input=pred_logit, dim=1) | |
pred_labels_and_probs = {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))} # noqa 5501 | |
pred_time = round(timer() - start_time, 5) | |
return pred_labels_and_probs, pred_time | |
# # Gradio app | |
title = "FoodVision Mini ππ₯©π£" | |
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi." # noqa 5501 | |
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." # noqa 5501 | |
example_list = [["examples/" + example] for example in os.listdir("examples")] # noqa 5501 | |
# Create demo | |
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) | |
demo.launch() | |
if __name__ == "__main__": | |
main() | |