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import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
import os
import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
transformer = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(degrees=10),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)
classes=['Bus','bicycle','car']
if torch.cuda.is_available():
input_batch = input_batch.to('cuda')
model.to('cuda')
with torch.no_grad():
output = model(input_batch)
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Read the categories
with open("imagenet_classes.txt", "r") as f:
categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 3)
result = {}
for i in range(top5_prob.size(0)):
result[categories[top5_catid[i]]] = top5_prob[i].item()
return result
inputs = gr.inputs.Image(type='pil')
outputs = gr.outputs.Label(type="confidences",num_top_classes=3)
gr.Interface(inference, inputs, outputs, analytics_enabled=False).launch()
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