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### 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
# Set up class names
class_names = ['pizza', 'steak', 'sushi']
### 2. Model and transforms preparation ###
# Create EffNetB2 model
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes=3
)
# Load saved weights
effnetb2.load_state_dict(
torch.load(
f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
map_location=torch.device("cpu") # load to CPU
)
)
### 3. Predict function ###
# Create predict function
def predict(img) -> Tuple[Dict, float]:
"""
Transforms and peforms a prediction on img and retunrs predictions
"""
# Start the timer
start_time = timer()
# Transform the target image and add a batch dimension
img = effnetb2_transforms(img).unsqueeze(0)
# put the model into evaluation mode and turn on inference mode
effnetb2.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(effnetb2(img), dim=1)
# Create a prediction label and predictoin probability dictionary Gradio interface
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
# Calculate the prediction time
pred_time = round(timer() - start_time, 5)
# Return the prediction dictionary and prediction time
return pred_labels_and_probs, pred_time
### 4. Gradio app ###
# Create title, description and article strings
title = "FoodVision Mini"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizze, steak, or sushi"
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
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()
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