app.py add

Makefile

@@ -1,10 +1,27 @@
 install:
-	pip install -r requirements.txt
-
-run1:
-	python hello.py
-run2:
-	python sample_classification.py
-run3:
-	python hello.py
-	python sample_classification.py
+	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

@@ -0,0 +1,20 @@
+from transformers import pipeline
+import gradio as gr
+
+
+# Initialize the summarization model
+model = pipeline("summarization")
+
+
+def predict(prompt):
+    summary = model(prompt)[0]["summary_text"]
+    return summary
+
+
+# Create a Gradio interface
+interface = gr.Interface(fn=predict, inputs=gr.Textbox(placeholder="Enter text to summarize", lines=4), outputs="text")
+
+
+# Launch the interface
+interface.launch()
+

requirements.txt

@@ -1,3 +1,6 @@
 pandas
 numpy
-scikit-learn
+scikit-learn
+gradio
+transformers
+tensorflow