Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio
+from transformers import ViltProcessor, ViltForQuestionAnswering
+from PIL import Image
+
+processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+
+def predict_answer(image, question):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    encoding = processor(image, question, return_tensors="pt")
+    outputs = model(**encoding)
+    logits = outputs.logits
+    probs = logits.softmax(dim=-1)
+    sorted_probs, sorted_indices = probs[0].sort(descending=True)
+    answer_list = []
+    for i in range(5):
+        prob = sorted_probs[i].item()
+        if prob > 0.00:
+            idx = sorted_indices[i].item()
+            answer = model.config.id2label[idx]
+            answer_list.append(f"{answer}: {prob:.2%}")
+    return answer_list
+
+
+inputs = [
+    gradio.components.Image(label="Image"),
+    gradio.components.Textbox(label="Question", placeholder="Enter your question here.")
+]
+
+outputs = [
+    gradio.components.Textbox(label="Answer 1"),
+    gradio.components.Textbox(label="Answer 2"),
+    gradio.components.Textbox(label="Answer 3"),
+    gradio.components.Textbox(label="Answer 4"),
+    gradio.components.Textbox(label="Answer 5")
+]
+
+title = "Visual Question Answering (vilt-b32-finetuned-vqa)"
+
+gradio.Interface(fn=predict_answer, inputs=inputs, outputs=outputs, title=title, allow_flagging="never",
+                 css="footer{display:none !important}").launch(share=True)