first commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.h5 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,136 @@
+# Repository specific
+*datasets
+.DS_Store
+*.jpg
+*.h5
+*.jpeg
+
+# 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/

README.md

@@ -1,8 +1,8 @@
 ---
-title: Hot Dog Not Hot Dog
-emoji: 🌖
-colorFrom: indigo
-colorTo: pink
+title: Image Classification Cast Parts
+emoji: 🏃
+colorFrom: green
+colorTo: red
 sdk: gradio
 sdk_version: 2.8.14
 app_file: app.py

app.py

@@ -0,0 +1,94 @@
+### -------------------------------- ###
+###            libraries             ###
+### -------------------------------- ###
+import gradio as gr
+import numpy as np
+import os
+import tensorflow as tf
+
+### -------------------------------- ###
+###           model loading          ###
+### -------------------------------- ###
+model = tf.keras.models.load_model('model.h5')
+
+## --------------------------------- ###
+###     reading: categories.txt      ###
+### -------------------------------- ###
+labels = ['please upload categories.txt' for i in range(10)] # placeholder
+
+if os.path.isfile("categories.txt"):
+    # open categories.txt in read mode
+    categories = open("categories.txt", "r")
+    labels = categories.readline().split()
+
+## --------------------------------- ###
+###          page description        ###
+### -------------------------------- ###
+title = "Seefood: Hot dog or... not hot dog"
+description = "A Hugging Space demo created by datasith!"
+article = \
+'''
+#### Hot dog or not hot dog
+
+Jìan-Yang's masterpiece from the show Silicon Valley serves as a great exercise to get 
+familiar with Hugging Face spaces! All the necessary files are included for everything 
+to run smoothly on HF's Spaces:
+
+- app.py
+- reader.py
+- requirements.txt
+- model.h5 (TensorFlow/Keras)
+- categories.txt
+- info.txt
+
+The data used to train the model is available as a 
+[Kaggle dataset](https://www.kaggle.com/datasets/dansbecker/hot-dog-not-hot-dog).
+The step-by-step process for generating, training, and testing the Image Classification model is 
+available at my [GitHub respository](https://github.com/datasith/ds-experiments-image-classification/tree/main/hotdog-not-hotdog).
+
+If you enjoy my work feel free to follow me here on HF and/or on:
+
+- [GitHub](https://github.com/datasith)
+- [Kaggle](https://kaggle.com/datasith)
+- [Twitter](https://twitter.com/datasith)
+- [LinkedIn](https://linkedin.com/in/datasith)
+
+Either way, enjoy!
+'''
+
+### -------------------------------- ###
+###        interface creation        ###
+### -------------------------------- ###
+samples = ['yay.jpg', 'nay.jpg']
+
+def preprocess(image):
+    image = tf.image.resize(image, [512, 512])
+    img_array = tf.keras.utils.img_to_array(image)
+    img_array = tf.expand_dims(img_array, 0)
+    # image = np.array(image) / 255
+    # image = np.expand_dims(image, axis=0)
+    return img_array
+  
+def predict_image(image):
+    # pred = model.predict(preprocess(image))
+    # results = {}
+    # for row in pred:
+    #     for idx, item in enumerate(row):
+    #         results[labels[idx]] = float(item)
+    predictions = model.predict(preprocess(image))
+    scores = tf.nn.softmax(predictions[0])
+    results = {}
+    for idx, res in enumerate(scores):
+        results[labels[idx]] = float(res)
+    return results
+
+# generate img input and text label output
+image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
+label = gr.outputs.Label(num_top_classes=len(labels))
+
+# generate and launch interface
+interface = gr.Interface(fn=predict_image, inputs=image, 
+                         outputs=label, article=article, theme='default', 
+                         title=title, allow_flagging='never', description=description, 
+                         examples=samples)
+interface.launch()

categories.txt

@@ -0,0 +1 @@
+Hot-dog Not-hot-dog

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow>=2.6.1
+keras>=2.6.0
+yattag==1.14.0