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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from pathlib import Path
from timeit import default_timer as timer
from typing import Tuple, Dict
from torchvision import transforms
class_names=['meme', 'non-meme']
model_path=Path("efficientNet_clf.pt")
model = torch.jit.load(model_path,map_location=torch.device('cpu'))
image_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
print(image_transform)
def predict(img) -> Tuple[Dict, float]:
"""Transforms and performs a prediction on img and returns prediction and time taken.
"""
#print("---img path is: ",img)
start_time = timer()
model.to("cpu")
model.eval()
with torch.inference_mode():
img = image_transform(img).unsqueeze(dim=0)
pred_probs = torch.softmax(model(img).to("cpu"), dim=1)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
pred_time = round(timer() - start_time, 5)
return pred_labels_and_probs, pred_time
#print(e)
#return "error",0
title = "Meme classifiication"
description = "An EfficientNetB2 model to classify images of food into 2 classes:meme and non-meme"
example_list = ["./example_imgs/"+i for i in os.listdir("./example_imgs")]
#print(example_list)
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=2, label="Predictions"),
gr.Number(label="Prediction time (s)"),
],
examples=example_list,
title=title,
description=description,
)
demo.launch()
#predict(example_list[0]) |