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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 |