| """
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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
|
| """
|
|
|
| import streamlit as st
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| from app import device, load_demo_image
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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_base", "BLIP_large"])
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|
|
| sampling_method = st.sidebar.selectbox(
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| "Sampling method:", ["Beam search", "Nucleus sampling"]
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| )
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|
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| st.markdown(
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| "<h1 style='text-align: center;'>Image Description Generation</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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|
|
| use_beam = sampling_method == "Beam search"
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|
|
| col1, col2 = st.columns(2)
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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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|
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| col1.header("Image")
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|
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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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|
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| col1.image(resized_image, use_column_width=True)
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| col2.header("Description")
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|
|
| cap_button = st.button("Generate")
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|
|
|
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| vis_processor = load_processor("blip_image_eval").build(image_size=384)
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|
|
| if cap_button:
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| if model_type.startswith("BLIP"):
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| blip_type = model_type.split("_")[1].lower()
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| model = load_model_cache(
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| "blip_caption",
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| model_type=f"{blip_type}_coco",
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| is_eval=True,
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| device=device,
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| )
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|
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| img = vis_processor(raw_img).unsqueeze(0).to(device)
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| captions = generate_caption(
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| model=model, image=img, use_nucleus_sampling=not use_beam
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| )
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|
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| col2.write("\n\n".join(captions), use_column_width=True)
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|
|
|
|
| def generate_caption(
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| model, image, use_nucleus_sampling=False, num_beams=3, max_length=40, min_length=5
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| ):
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| samples = {"image": image}
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|
|
| captions = []
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| if use_nucleus_sampling:
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| for _ in range(5):
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| caption = model.generate(
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| samples,
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| use_nucleus_sampling=True,
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| max_length=max_length,
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| min_length=min_length,
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| top_p=0.9,
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| )
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| captions.append(caption[0])
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| else:
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| caption = model.generate(
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| samples,
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| use_nucleus_sampling=False,
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| num_beams=num_beams,
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| max_length=max_length,
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| min_length=min_length,
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| )
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| captions.append(caption[0])
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|
|
| return captions
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|
|
