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()