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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 | |
class_names = ['dew', | |
'fogsmog', | |
'frost', | |
'glaze', | |
'hail', | |
'lightning', | |
'rain', | |
'rainbow', | |
'rime', | |
'sandstorm', | |
'snow'] | |
vitb16, vitb16_transforms = create_vit_model(num_classes=len(class_names)) | |
vitb16.load_state_dict( | |
torch.load("vitb16_feature_extractor_weather_rcg.pth", | |
map_location=torch.device("cpu") | |
) | |
) | |
def predict(img): | |
start_timer = timer() | |
img = vitb16_transforms(img).unsqueeze(0) | |
vitb16.eval() | |
with torch.inference_mode(): | |
pred_probs = torch.softmax(vitb16(img), dim=1) | |
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
pred_timer = round(timer()- start_timer, 4) | |
return pred_labels_and_probs, pred_timer | |
title = "Wather Recognition" | |
description = "A ViTb16 Feature Extractor CV model to recognize weather conditions" | |
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=11, label="Predictions"), | |
gr.Number(label="Prediction time(s)")], | |
examples=example_list, | |
title=title, | |
description=description | |
) | |
demo.launch() | |