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import torch
import torchvision
from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
from torch import nn
from PIL import Image
from model import create_effnet_v2_model
import gradio as gr
import os
from timeit import default_timer as timer
class_names = ['Honda', 'Hyundai', 'Toyota']
effnet_v2, transforms = create_effnet_v2_model(num_classes=len(class_names), weights_path="efficient_net_s_carvision_3.pth")
def predict(image):
start_time = timer()
# image = Image.open(image_path)
image = transforms(image).unsqueeze(0)
# image = image.to(device)
output = effnet_v2(image)
effnet_v2.eval()
with torch.inference_mode():
probs = torch.softmax(output, dim=1)
pred_labels_and_probs = {class_names[i]: float(probs[0, i]) for i in range(len(class_names))}
pred_time = round(timer() - start_time, 5)
return pred_labels_and_probs, pred_time
### 4. Gradio app ###
# Create title, description and article strings
title = "CarVision 🚗🚘🚙🏎️"
description = """
An EfficientNet V2 model to classify cars as **Honda, Hyundai or Toyota**
[Dataset Used](https://github.com/nicolas-gervais/predicting-car-price-from-scraped-data/tree/master/picture-scraper)
Currently running at **85% Accuracy**
"""
article = "Created by [Akshay Ballal](www.akshaymakes.com)"
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
inputs=gr.Image(type="pil"), # what are the inputs?
outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
# Create examples list from "examples/" directory
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch() |