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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_vit16_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
with open("class_names.txt", "r") as f: # reading them in from class_names.txt
class_names = [food_name.strip() for food_name in f.readlines()]
### 2. Model and transforms preparation ###
# Create model
vit16, vit16_transforms = create_vit16_model(
num_classes=101, # could also use len(class_names)
)
vit16.load_state_dict(
torch.load(
f="model_food101_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 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 = vit16_transforms(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
vit16.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(vit16(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 = "FoodVision ViT 🍔👁"
description = "A ViT_B_16 feature extractor computer vision model to classify images of food into 101 different classes using 20% of the data."
article = ""
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create Gradio interface
demo = gr.Interface(
fn=predict,
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 app!
demo.launch()
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