Create app.py

app.py

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+import gradio as gr
+from transformers import ViltProcessor, ViltForQuestionAnswering
+from PIL import Image
+import torch
+
+# Load the processor and model
+processor = ViltProcessor.from_pretrained("MariaK/vilt_finetuned_200")
+model = ViltForQuestionAnswering.from_pretrained("MariaK/vilt_finetuned_200")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def predict(image, question):
+    # prepare inputs
+    inputs = processor(image, question, return_tensors="pt").to(device)
+
+    # forward pass
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    idx = logits.argmax(-1).item()
+    predicted_answer = model.config.id2label[idx]
+    return predicted_answer
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Textbox(lines=1, placeholder="Enter your question here..."),
+    ],
+    outputs="text",
+    title="Visual Question Answering with Fine-tuned Vilt",
+    description="Upload an image and ask a question about it!",
+)
+
+# Launch the interface
+iface.launch(share=True)  # Set share=True to share the space