app.py

###1. 라이브러리, 클래스 이름 불러오기
import gradio as gr
import os
import torch

from model import create_effnetb1_model
from timeit import default_timer as timer
from typing import Tuple, Dict

with open("class_names.txt", "r") as f:
  class_names = [food_name.strip() for food_name in f.readlines()]

### 2. Model, transforms 준비
effnetb1, effnetb1_transforms = create_effnetb1_model(num_classes=101)

#load saved weights
effnetb1.load_state_dict(
    torch.load(f="09_pretrained_effentb1_feature_extractor_food_101_20_percent.pth",
               map_location=torch.device("cpu"))
)

### 3. Predict functions
def predict(img) -> Tuple[Dict, float]:
  #timer 시작
  start_time = timer()
  #image effnetb1 입력형태로 변환
  img = effnetb1_transforms(img).unsqueeze(0) #batch dimension 0번째 차원에 더하기
  #예측하기
  effnetb1.eval()
  with torch.inference_mode():
    #image -> prediction logits -> prediction probability
    pred_probs = torch.softmax(effnetb1(img), dim=1)

  #prediction label, pred probability dictionary
  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
  #예측 시간 계산
  end_time = timer()
  pred_time = round(end_time-start_time, 4)
  #pred dict, pred time 돌려주기
  return pred_labels_and_probs, pred_time

### 4. Gradio app ###

title = "FoodVision Big"
description = "An EfficientNetB1 feature extractor for 101 classes of food "
article = "Created at 09. PyTorch Model Deployment."

#example list를 demo app 내부 경로로 수정

#예시 그림의 파일 경로 가져오기!
example_list = [["examples/" + example] for example in os.listdir("examples")]
example_list

#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)
#데모 실행
demo.launch(debug=False) #디버그 방식 끄기