Spaces:
Sleeping
Sleeping
File size: 2,636 Bytes
ca3a209 9e3f4bf ca3a209 371aecd ca3a209 9bad780 8a44c10 | 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_vit_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ["pizza", "steak", "sushi"]
### 2. Model and transforms preparation ###
vit, vit_transforms = create_vit_model(num_classes = 3)
# Load saved weights
vit.load_state_dict(
torch.load(
f = "09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.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 ViT
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 probabilities
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 = "Mini Food Image Classifier 🍕🥩🍣"
description = "A [ViT transformer feature extractor](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#vit-b-16) computer vision model to classify images as pizza, steak, or sushi."
article = "Created at [turtlemb's GitHub](https://github.com/turtlemb)."
# 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 = 3, label = "Predictions"),
gr.Number(label = "Prediction time (s)")],
examples = example_list,
title = title,
description = description,
article = article)
# Launch the demo
demo.launch(debug = False, # print errors locally?
share = True, # generate a publicly shareable URL
inline = False) # to fix blank screen when embedding Gradio app in Jupyter Lab
|