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Parent(s):
:tada: init
Browse files- .github/workflows/check_size.yaml +17 -0
- .github/workflows/sync_to_hub.yaml +20 -0
- README.md +19 -0
- app.py +46 -0
- packages.txt +0 -0
- requirements.txt +2 -0
.github/workflows/check_size.yaml
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name: Check file size
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on:
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pull_request:
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branches: [main]
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# to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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sync-to-hub:
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runs-on: ubuntu-latest
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steps:
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- name: Check large files
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uses: ActionsDesk/lfs-warning@v2.0
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with:
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filesizelimit: 10485760 # = 10MB, so we can sync to HF spaces
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.github/workflows/sync_to_hub.yaml
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name: Sync to Hugging Face hub
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on:
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push:
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branches: [main]
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# to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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sync-to-hub:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Push to hub
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env:
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HF_TOKEN: ${{ secrets.HF_TOKEN }}
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run: git push https://nateraw:$HF_TOKEN@huggingface.co/spaces/nateraw/huggingpics-explorer main --force
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README.md
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---
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title: huggingpics-explorer
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emoji: 🤗
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colorFrom: blue
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colorTo: red
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sdk: streamlit
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app_file: app.py
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pinned: false
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---
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# huggingpics-explorer
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[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/huggingpics-explorer)
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A streamlit app for exploring image search results from HuggingPics
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---
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Autogenerated using [this template](https://github.com/nateraw/spaces-template)
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app.py
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import streamlit as st
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from huggingpics.data import get_image_urls_by_term
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def show_images_of_term(search_term, num_cols=5, num_rows=3):
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# Get the image urls
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# Arbitrarily adding 2 to make sure we have enough images in the event of a failed request
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urls = get_image_urls_by_term(search_term, count=(num_rows * num_cols) + 2)
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st.title(search_term)
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for row_id in range(num_rows):
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cols = st.columns(num_cols)
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for col_id in range(num_cols):
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cols[col_id].image(urls[row_id * num_cols + col_id], use_column_width=True)
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def explore():
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with st.sidebar:
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term_1 = st.sidebar.text_input('Search Term 1', value='shiba inu')
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term_2 = st.sidebar.text_input('Search Term 2', value='husky')
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term_3 = st.sidebar.text_input('Search Term 3', value='')
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term_4 = st.sidebar.text_input('Search Term 4', value='')
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term_5 = st.sidebar.text_input('Search Term 5', value='')
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terms = [t for t in [term_1, term_2, term_3, term_4, term_5] if t]
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for term in terms:
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show_images_of_term(term)
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def create_dataset():
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st.text("# Coming soon...")
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def main():
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with st.sidebar:
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mode = st.sidebar.selectbox("Mode", ["Explore", "Create Dataset"])
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st.sidebar.markdown("---")
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_ = explore() if mode == "Explore" else create_dataset()
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if __name__ == '__main__':
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main()
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packages.txt
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requirements.txt
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streamlit
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huggingpics
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