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### imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# setting up class names
with open("class_names.txt", "r") as f:
class_names = [food.strip() for food in f.readlines()]
### model and transforms prepration ###
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101,
seed=29)
# loading the saved weights
effnetb2.load_state_dict(
torch.load(
f="pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
map_location=torch.device("cpu") # loading the model to cpu
)
)
### predict function ###
def predict(img) -> Tuple[Dict, float]:
# start a timer
start_time = timer()
# transforming the input image
img = effnetb2_transforms(img).unsqueeze(0)
# putting the model into eval mode & making prediction
effnetb2.eval()
with torch.inference_mode():
# passing transformed img through the model and turn pred logits into probs
pred_probs = torch.softmax(effnetb2(img), dim=1)
# creating a prediction label & 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
### gradio app ###
# creating title, description and article
title = "FoodVision Big"
description = "An EfficientNetB2 feature extractor computer vision model to classify images in 101 different classes!"
# creating an example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# creating 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)
# launching the demo
demo.launch()
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