app.py

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