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import streamlit as st
from transformers import ViltProcessor, ViltForQuestionAnswering
from PIL import Image
def load_model():
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
return processor, model
def predict(image, text, processor, model):
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
return model.config.id2label[idx]
def main():
st.title("VQA")
st.write("Upload an image and input a question to get an answer.")
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
question = st.text_input("Question about the image:")
if question:
processor, model = load_model()
answer = predict(image, question, processor, model)
col1, col2 = st.columns(2)
with col1:
st.image(image, caption='Uploaded Image.', use_column_width=True)
with col2:
st.write(f"**Question:** {question}")
st.write(f"**Answer:** {answer}")
if __name__ == "__main__":
main()
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