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import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
# Setup class names
with open("class_names.txt", 'r') as f:
classes = [name.strip() for name in f]
# Model and transforms
model, transform = create_effnetb2_model(
num_classes=len(classes)
)
model.load_state_dict(
torch.load(
f="model_v3.pth",
map_location=torch.device("cpu")
)
)
# Predict function
def predict(img):
start_time = timer()
# Transform the target image and add a batch dimension
img = transform(img).unsqueeze(0)
model.eval()
with torch.inference_mode():
predictions = torch.softmax(model(img), dim=1)
# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio)
pred_labels_and_probs = {classes[i]: float(predictions[0][i]) for i in range(len(classes))}
pred_time = round(timer() - start_time, 4)
return pred_labels_and_probs, pred_time
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Gradio interface
title = "Weather image classification ⛅❄☔"
description = "Classifies the weather conditions from an image, capable of distinguishing among 12 distinct classes."
article = "See the code on [GitHub](https://github.com/georgescutelnicu/Weather-Image-Classification)."
demo = gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=1, label="Predictions"),
gr.Number(label="Prediction time (s)")],
examples=example_list,
title=title,
description=description,
article=article)
demo.launch(debug=False,
share=False)
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