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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()
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