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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
class_names = ["pizza", "steak", "sushi"]
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
effnetb2.load_state_dict(torch.load("09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", map_location=torch.device("cpu")))
def predict(img) -> Tuple[Dict, float]:
start_time = timer()
img = effnetb2_transforms(img).unsqueeze(0)
effnetb2.eval()
with torch.inference_mode():
pred_probs = torch.softmax(effnetb2(img), dim=1)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
end_timer = timer()
pred_time = round(end_timer-start_time, 4)
return pred_labels_and_probs, pred_time
example_list =[["examples/" + example] for example in os.listdir("examples")]
import gradio as gr
title="FoodVision Mini 🍕🥩🍣"
description = "An EfficientNetB2 feature extractor model that predicts pizza, steak and sushi"
article= "Created as a test"
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(debug=False, share=False)
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