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import gradio as gr | |
import os | |
import torch | |
from model import create_vit | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
class_names = ["buildings", "forest", "glacier", "mountain", "sea", "street"] | |
vit_model, vit_transforms = create_vit(num_classes=len(class_names), | |
seed=42) | |
vit_model.load_state_dict( | |
torch.load( | |
f="pretrained_vit_feature_extractor_scene_recognition.pth", | |
map_location=torch.device("cpu") | |
) | |
) | |
def predict(img): | |
start_timer = timer() | |
img = vit_transforms(img).unsqueeze(0) | |
vit_model.eval() | |
with torch.inference_mode(): | |
pred_prob = torch.softmax(vit_model(img), dim=1) | |
pred_labels_and_probs = {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))} | |
pred_time = round(timer() - start_timer, 5) | |
return pred_labels_and_probs, pred_time | |
title = "Scene Recognition: Intel Image Classification" | |
description = "A ViT feature extractor Computer Vision model to classify images of scenes from 1 out of 6 classes." | |
article = "Access project repository at [GitHub](https://github.com/Ammar2k/intel_image_classification)" | |
example_list = [["examples/" + example] for example in os.listdir("examples")] | |
demo = gr.Interface(fn=predict, | |
inputs=gr.Image(type="pil"), | |
outputs=[gr.Label(num_top_classes=6, label="Predictions"), | |
gr.Number(label="Prediction time(s)")], | |
examples=example_list, | |
title=title, | |
description=description, | |
article=article | |
) | |
demo.launch() | |