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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
# Setup class names
foodvision101_class_names_path = "class_names.txt"
with open(foodvision101_class_names_path, "r") as f:
class_names = [food.strip() for food in f.readlines()]
# Model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes=101)
# Load save weights
effnetb2.load_state_dict(
torch.load(
f="09_pretrained_effnetb2_feature_extractor_food101.pth",
map_location=torch.device("cpu")
)
)
# Predict function
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with EffNetB2
img = effnetb2_transforms(img).unsqueeze(0)
# Put model into eval mode, make prediction
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image through the model
pred_probs = torch.softmax(effnetb2(img), dim=1)
# Create prediction label and 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
# Create title, description and article
title = "FoodVision 101 Learning Practice"
description = "An EfficientNetB2 feature extractor computer vision model to classify images"
article = "Created at PyTorch Model Deployment"
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo= gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3, label="Predicitons"),
gr.Number(label="Prediction Time (s)")],
examples=example_list,
title=title,
description=description,
article=article)
# Launching the demo
demo.launch()
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