add app.py

app.py

@@ -0,0 +1,39 @@
+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()