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from PIL import Image
import gradio as gr
from transformers import ViTFeatureExtractor, ViTForImageClassification
import torch
model = ViTForImageClassification.from_pretrained('sreeramajay/pollution')
transforms = ViTFeatureExtractor.from_pretrained('sreeramajay/pollution')
def predict(image):
labels = {0:"Air Pollution", 1: "Land Pollution" , 2: "Water Pollution"}
inputs = transforms(image, return_tensors='pt')
output = model(**inputs)
probability = output.logits.softmax(1)
values, indices = torch.topk(probability, k=3)
return {labels[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
gr.Interface(
predict,
inputs = gr.inputs.Image(type="pil", label="Chosen Image"),
outputs = 'label',
theme="seafoam",
).launch(debug=True)