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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_vit_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 perparation ###
vit, vit_transforms = create_vit_model(
num_classes=325)
# Load save weights
vit.load_state_dict(
torch.load(
f="bird_classification_vit.pth",
map_location=torch.device("cpu") # load the model to the CPU
)
)
### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with EffNetB2
img = vit_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
# Put model into eval mode, make prediction
vit.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probaiblities
pred_probs = torch.softmax(vit(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 = "Bird Species Classification"
description = "A [Transformer] computer vision model to classify Bird species.."
article = "Created by Chidera Stanley [Transformer using PyTorch]"
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
app = gr.Interface(fn=predict, # maps inputs to outputs
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!
app.launch()
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