Upload folder using huggingface_hub

.github/workflows/main.yml

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+name: Deploy to Hot Dog Classifier Demo Space
+
+on:
+  push:
+    branches: [ main ]  # Change this to the branch you want to trigger the deployment from
+  workflow_dispatch:  # Allows manual triggering
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+        
+    - name: Install dependencies
+      run: pip install huggingface_hub streamlit pillow
+        
+    - name: Configure Hugging Face Token
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        mkdir -p ~/.huggingface
+        echo "$HF_TOKEN" > ~/.huggingface/token
+        
+    - name: Deploy to Hot Dog Classifier Demo Space
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        SPACE_NAME: "hwang2006/hot-dog-classifier-demo"
+      run: |
+        python -c "
+        from huggingface_hub import HfApi
+        api = HfApi()
+        api.upload_folder(
+            folder_path='.',
+            repo_id='$SPACE_NAME',
+            repo_type='space',
+        )
+        "

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Streamlit
+.streamlit

Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+test:
+	python -m pytest -vv --cov=hello test_hello.py
+
+format:
+	black *.py
+
+lint:
+	pylint --disable=R,C hello.py
+
+all: install lint test format

README.md

@@ -1,13 +1,4 @@
 ---
-title: Hot Dog Classifier Demo
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
 sdk: streamlit
-sdk_version: 1.41.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+sdk_version: 1.41.1 # The latest supported version
+---

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+st.title("Hot Dog? Or Not?")
+
+file_name = st.file_uploader("Upload a hot dog candidate image")
+
+if file_name is not None:
+    col1, col2 = st.columns(2)
+
+    image = Image.open(file_name)
+    col1.image(image, use_column_width=True)
+    predictions = pipeline(image)
+
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")

requirements.txt

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+transformers
+torch
+streamlit