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299f855 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ### 1. 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
# Setup class names
with open('class_names.txt', 'r') as f:
class_names = [food_name.strip() for food_name in f.readlines()]
### 2. Model and transforms preparation ###
# Create model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes = 101
)
# Load the saved weights
effnetb2.load_state_dict(
torch.load(f = '09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth',
map_location = torch.device('cpu'))
)
### 3. Predict function ###
from typing import Tuple, Dict
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) # unsqueeze = add batch dimension on 0th index
# Put model int eval mode, make predictions
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn prediction logits into prediction probabilities
pred_probs = torch.softmax(effnetb2(img), dim = 1)
# Create a 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
### 4. Gradio app ###
# Create title, description, and article
title = 'FoodVision Big 🍔👀💪'
description = 'An EfficientNetB2 Feature extractor computer vision model to classify images 101 classes of food from the Food101 dataset '
article = '[Created at 09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#11-turning-our-foodvision-big-model-into-a-deployable-app)'
# Create example list
example_list = [['examples/' + example] for example in os.listdir('examples')]
# Create 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,
article = article)
# Launch the demo!
demo.launch(debug = True) # print errors locally
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