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from model import create_effnetb2
from typing import Tuple, Dict
from PIL import Image
from time import time
import torch
import torchvision
import gradio as gr
import os
from pathlib import Path
class_names = ["pizza", "steak", "sushi"]
effnetb2 , effnetb2_transforms = create_effnetb2()
# Load weights
PATH = "09_pretrained_effnetb2_feature_extractor_pizza20%_10epochs.pth"
effnetb2.load_state_dict(torch.load(f=PATH,
map_location=torch.device('cpu')
))
effnetb2.eval()
def predict(img) ->Tuple[Dict, float]:
start_time = time()
img_tr = img
img_tr = effnetb2_transforms(img_tr).unsqueeze(0)
#predict
effnetb2.eval()
with torch.inference_mode():
pred_prob = torch.softmax(effnetb2(img_tr), dim=1)
pred_labesls_and_pobs ={class_names[i]:pred_prob[0][i] for i in range(len(class_names)) }
end_time = time()
pred_time = round(end_time - start_time,4)
return pred_labesls_and_pobs ,pred_time
example_list = [["examples/"+example for example in os.listdir("examples") ]]
# Create title, description and article strings
title = "FoodVision Classification"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
article = "Created at [Using pre-trained model efficientnet_b2](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)."
# Create the Gradio 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)
# Launch the demo!
demo.launch(debug=False)
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