all files

Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+test:
+	python -m pytest -vvv --cov=hello --cov=greeting \
+		--cov=smath --cov=web tests
+	python -m pytest --nbval notebook.ipynb	#tests our jupyter notebook
+	#python -m pytest -v tests/test_web.py #if you just want to test web
+
+debug:
+	python -m pytest -vv --pdb	#Debugger is invoked
+
+one-test:
+	python -m pytest -vv tests/test_greeting.py::test_my_name4
+
+debugthree:
+	#not working the way I expect
+	python -m pytest -vv --pdb --maxfail=4  # drop to PDB for first three failures
+
+format:
+	black *.py
+
+lint:
+	pylint --disable=R,C *.py
+
+all: install lint test format

app.py

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+import gradio as gr
+
+transformer_model = "huggingface/google/vit-base-patch16-224"
+Instructuction = "Browse the internet to download any unique image"
+title="Simple Image classification playground"
+description = "Drop an image to classify, then observe how the machine learning model\
+               is able to show the percentages for different class predictions made."
+article = """
+            - Select an image from the examples provided as demo image
+            - Click submit button to make Image classification
+            - Click clear button to try new Image for classification
+          """
+
+# Gradio app design
+interface = gr.Interface.load(
+            transformer_model,
+            title = title,
+            description = description,
+            article = article,
+            allow_flagging = "never",
+            theme = "peach",
+            live = False,
+            examples=["examples-jpg/example_image1.png", 
+                      "examples-jpg/example_image2.jpg",
+                      "examples-jpg/example_image3.jpg"]
+            )
+interface.launch()

requirements.txt

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+gradio