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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 🍕🥩🍣"
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()
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