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+This is demo to use Visual question answering model using E2Ecloud ML.
+This is based on gradio UI app with Azure cloud backend
+Azure components used are storage,database,VM,CDN , load balancer.
+Webserver is run Azure VM.
+User can load images from client , that is stored in Azure backendand used by processos to access and process.
+The processing transformer VQA module ( dandelin/vilt-b32-finetuned-vqa ) is loaded from huggingface interface. 
+
+The cloud backend is used for scaling

vqa.py

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+import gradio as gr
+
+from transformers import ViltProcessor, ViltForQuestionAnswering
+
+
+def getResult(query, image):
+    # prepare image + question
+    #image = Image.open(BytesIO(base64.b64decode(base64_encoded_image)))
+    text = query
+
+    processor = ViltProcessor.from_pretrained(
+        "dandelin/vilt-b32-finetuned-vqa")
+    model = ViltForQuestionAnswering.from_pretrained(
+        "dandelin/vilt-b32-finetuned-vqa")
+
+    # prepare inputs
+    encoding = processor(image, text, return_tensors="pt")
+
+    # forward pass
+    outputs = model(**encoding)
+    logits = outputs.logits
+    idx = logits.argmax(-1).item()
+    print("Predicted answer:", model.config.id2label[idx])
+    return model.config.id2label[idx]
+
+
+iface = gr.Interface(fn=getResult, inputs=[
+                     "text", gr.Image(type="pil")], outputs="text")
+iface.launch(server_name="0.0.0.0",share=True)