Create app.py

app.py

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+from transformers import ViltProcessor, ViltForQuestionAnswering
+import torch
+from PIL import Image
+import gradio as gr
+
+# Load the model and processor
+processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+def answer_question(image, text):
+    # Convert the uploaded image to PIL format
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+
+    # Process the image and text
+    encoding = processor(images=image, text=text, return_tensors="pt", padding=True)
+
+    # Forward pass
+    with torch.no_grad():
+        outputs = model(**encoding)
+
+    logits = outputs.logits
+    idx = logits.argmax(-1).item()
+    predicted_answer = model.config.id2label[idx]
+
+    # Return the predicted answer
+    return predicted_answer
+
+# Define Gradio inputs and outputs
+image = gr.Image(type="numpy", label="Upload Image")
+question = gr.Textbox(lines=2, label="Question")
+answer = gr.Textbox(label="Predicted Answer")
+
+# Create Gradio Interface
+gr.Interface(
+    fn=answer_question, 
+    inputs=[image, question], 
+    outputs=answer,
+    title="Image Based Visual Question Answering",
+    description="This is a demonstration of ViLT (Vision and Language Transformer) using Gradio, which has been fine-tuned on VQAv2 to answer questions based on images. To get a predicted answer, please provide an image and type in your question, then press the submit button."
+).launch()