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app.py

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+import streamlit as st
+from PIL import Image
+import requests
+from io import BytesIO
+from transformers import ViltProcessor, ViltForQuestionAnswering
+
+st.set_page_config(layout='wide',page_title='VQA')
+
+#Vilt model
+processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+model =ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+def get_answer(image,text):
+    try:
+        #load and process the image
+        img = Image.open(BytesIO(image)).convert('RGB')
+        encoding = processor(img,text,return_tensors="pt")
+
+        #forward pass
+        outputs = model(**encoding)
+        logits = outputs.logits
+        idx = logits.argmax(-1).item()
+        answer = model.config.id2label[idx]
+
+        return answer
+    except Exception as e:
+        return str(e)
+    
+st.title("Visual Question Answering App")
+st.write("Update an image and enter qustion to get and answer")
+
+    
+col1,col2 = st.columns(2)
+
+with col1:
+    uploaded_file = st.file_uploader("Upload your own image",type=['jpg','png','jpeg'])
+    st.image(uploaded_file,use_column_width=True)
+
+with col2:
+    question = st.text_input("Question")
+
+    if uploaded_file and question is not None:
+        if st.button("Ask Question"):
+            image = Image.open(uploaded_file)
+            image_byte_array = BytesIO()
+            image.save(image_byte_array,format="JPEG")
+            image_bytes = image_byte_array.getvalue() 
+            
+        
+            answer = get_answer(image_bytes,question)
+            #st.show(answer)
+            st.info("Your Question:" + question)
+            st.success("Answer:" + answer)
+
+
+

requirements.txt

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+transformers
+torch
+requests
+pillow
+fastapi
+uvicorn
+streamlit
+python-multipart