First commit

Makefile

@@ -0,0 +1,25 @@
+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+# test:
+# 	python -m pytest -vv --cov=main --cov=mylib test_*.py
+
+format:	
+	black *.py 
+
+run:
+	python app.py
+
+# lint:
+# 	pylint --disable=R,C --ignore-patterns=test_.*?py *.py mylib/*.py
+
+# container-lint:
+# 	docker run --rm -i hadolint/hadolint < Dockerfile
+
+# refactor: format lint
+
+# deploy:
+# 	#deploy goes here
+		
+# all: install lint test format deploy

app.py

@@ -0,0 +1,14 @@
+from transformers import pipeline
+import gradio as gr
+
+model=pipeline("summarization")
+
+def predict(prompt):
+    summary=model(prompt)[0]["summary_text"]
+    return summary
+
+with gr.Blocks() as app:
+    textbox= gr.Textbox(placeholder="Entrez le text à resumer ici ", lines=4)
+    gr.Interface(fn=predict,inputs=textbox,outputs="text")
+
+app.launch()

flagged/log.csv

@@ -0,0 +1,2 @@
+prompt,output,flag,username,timestamp
+"MLOps, short for Machine Learning Operations, is a set of practices designed to create an assembly line for building and running machine learning models. It helps companies automate tasks and deploy models quickly, ensuring everyone involved (data scientists, engineers, IT) can cooperate smoothly and monitor and improve models for better accuracy and performance.","MLOps is a set of practices designed to create an assembly line for building and running machine learning models . it helps companies automate tasks and deploy models quickly, ensuring everyone involved can cooperate smoothly .",,,2024-09-02 12:22:23.108530

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+transformers
+tensorflow
+tf-keras