File size: 1,692 Bytes
8908af0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from consts import class_names
# Model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes=101,
)
# Load saved weights
effnetb2.load_state_dict(
torch.load(f="09_effnetb2_food101.pth", map_location=("cpu")) # load the model to the CPU
)
# prediction function
def predict(img) -> tuple:
start_time = timer()
img = effnetb2_transforms(img).unsqueeze(0)
effnetb2.eval()
with torch.inference_mode():
pred_probs = torch.softmax(effnetb2(img), dim=1)
pred_labesl_and_probs = {
class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
}
end_time = timer()
pred_time = round(end_time - start_time, 4)
return pred_labesl_and_probs, pred_time
# gradio app
title = "FoodVision Food101 🌮🍣🍕🍣🍝"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of classes Food101 dataset."
article = "Created at 09 PyTorch Model Deployment."
# create example list
foodvision_min_examples_path = "examples"
example_list = [
[os.path.join(foodvision_min_examples_path, file)]
for file in os.listdir(foodvision_min_examples_path)
if file.lower().endswith((".jpg", ".jpeg", ".png"))
]
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=5, label="Predictions"), gr.Number(label="Prediction time (s)")],
title=title,
description=description,
article=article,
examples=example_list
)
demo.launch(share=False, server_name="0.0.0.0", debug=False)
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