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### 1. Imports and class names setup ### | |
import gradio as gr | |
import os | |
import torch | |
from model import create_effnetb0 | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
# Setup class names | |
class_names = ['cup_cakes', 'donuts', 'french_fries', 'ice_cream'] | |
### 2. Model and transforms preparation ### | |
# Create EffNetB0 model | |
effnetb0, effnetb0_transforms = create_effnetb0() | |
# Load saved weights | |
effnetb0.load_state_dict( | |
torch.load( | |
f="effnetb0_data20_10epoch.pth", | |
map_location=torch.device("cpu"), # load to CPU | |
) | |
) | |
### 3. Predict function ### | |
# Create predict function | |
def predict(img) -> Tuple[Dict, float]: | |
"""Transforms and performs a prediction on img and returns prediction and time taken. | |
""" | |
# Start the timer | |
start_time = timer() | |
# Transform the target image and add a batch dimension | |
img = effnetb0_transforms(img).unsqueeze(0) | |
# Put model into evaluation mode and turn on inference mode | |
effnetb0.eval() | |
with torch.inference_mode(): | |
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
pred_probs = torch.softmax(effnetb0(img), dim=1) | |
# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter) | |
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 = "Food Classifier" | |
description = "An EfficientNetB0 feature extractor computer vision model to classify images of French_fries, Cup_cakes, Ice_Cream, and Donuts." | |
article = "Created at google collab. Documentation at https://medium.com/me/stories/public, Code repository at https://github.com/Alyxx-The-Sniper/CNN " | |
# 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"), # what are the inputs? | |
outputs=[gr.Label(num_top_classes=4, label="Predictions"), # what are the outputs? | |
gr.Number(label="Prediction time (s)")], | |
# our fn has two outputs, therefore we have two outputs | |
# Create examples list from "examples/" directory | |
examples=example_list, | |
title=title, | |
description=description, | |
article=article) | |
# Launch the demo! | |
demo.launch() | |