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"""
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# Copyright (c) 2022, salesforce.com, inc.
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# All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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"""
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import streamlit as st
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from app import load_demo_image, device
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from app.utils import load_model_cache
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from lavis.processors import load_processor
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from PIL import Image
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def app():
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model_type = st.sidebar.selectbox("Model:", ["BLIP"])
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st.markdown(
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"<h1 style='text-align: center;'>Visual Question Answering</h1>",
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unsafe_allow_html=True,
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)
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instructions = """Try the provided image or upload your own:"""
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file = st.file_uploader(instructions)
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col1, col2 = st.columns(2)
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col1.header("Image")
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if file:
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raw_img = Image.open(file).convert("RGB")
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else:
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raw_img = load_demo_image()
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w, h = raw_img.size
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scaling_factor = 720 / w
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resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor)))
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col1.image(resized_image, use_column_width=True)
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col2.header("Question")
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user_question = col2.text_input("Input your question!", "What are objects there?")
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qa_button = st.button("Submit")
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col2.header("Answer")
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vis_processor = load_processor("blip_image_eval").build(image_size=480)
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text_processor = load_processor("blip_question").build()
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if qa_button:
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if model_type.startswith("BLIP"):
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model = load_model_cache(
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"blip_vqa", model_type="vqav2", is_eval=True, device=device
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)
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img = vis_processor(raw_img).unsqueeze(0).to(device)
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question = text_processor(user_question)
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vqa_samples = {"image": img, "text_input": [question]}
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answers = model.predict_answers(vqa_samples, inference_method="generate")
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col2.write("\n".join(answers), use_column_width=True)
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