Add app,py

app.py

@@ -0,0 +1,23 @@
+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)