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Pranav4datasc
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Upload 2 files
Browse files- app.py +56 -0
- requirements.txt +8 -0
app.py
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import streamlit as st
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from PIL import Image
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import requests
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from io import BytesIO
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from transformers import ViltProcessor, ViltForQuestionAnswering
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st.set_page_config(layout='wide',page_title='VQA')
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#Vilt model
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processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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model =ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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def get_answer(image,text):
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try:
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#load and process the image
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img = Image.open(BytesIO(image)).convert('RGB')
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encoding = processor(img,text,return_tensors="pt")
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#forward pass
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outputs = model(**encoding)
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logits = outputs.logits
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idx = logits.argmax(-1).item()
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answer = model.config.id2label[idx]
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return answer
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except Exception as e:
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return str(e)
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st.title("Visual Question Answering App")
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st.write("Update an image and enter qustion to get and answer")
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col1,col2 = st.columns(2)
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with col1:
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uploaded_file = st.file_uploader("Upload your own image",type=['jpg','png','jpeg'])
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st.image(uploaded_file,use_column_width=True)
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with col2:
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question = st.text_input("Question")
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if uploaded_file and question is not None:
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if st.button("Ask Question"):
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image = Image.open(uploaded_file)
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image_byte_array = BytesIO()
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image.save(image_byte_array,format="JPEG")
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image_bytes = image_byte_array.getvalue()
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answer = get_answer(image_bytes,question)
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#st.show(answer)
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st.info("Your Question:" + question)
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st.success("Answer:" + answer)
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requirements.txt
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@@ -0,0 +1,8 @@
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transformers
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torch
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requests
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pillow
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fastapi
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uvicorn
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streamlit
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python-multipart
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