app = FastAPI(
title="text-classification",
description="",
version="1.0.0",
)
# Get best run
best_run = utils.get_best_run(project="mahjouri-saamahn/mwml-httynn-app_v2",
metric="test_loss", objective="minimize")
# Load best run (if needed)
best_run_dir = utils.load_run(run=best_run)
# Get run components for prediction
args, model, X_tokenizer, y_tokenizer = predict.get_run_components(
run_dir=best_run_dir)
@utils.construct_response
@app.get("/")
async def _index():
response = {
'message': HTTPStatus.OK.phrase,
'status-code': HTTPStatus.OK,
'data': {}
}
config.logger.info(json.dumps(response, indent=2))
return response
# Title
st.title("Creating an End-to-End ML Application")
st.write("""[<img src="https://github.com/madewithml/images/blob/master/images/yt.png?raw=true" style="width:1.2rem;"> Watch Lesson](https://www.youtube.com/madewithml?sub_confirmation=1) ┬╖ [<img src="https://github.com/madewithml/images/blob/master/images/github_logo.png?raw=true" style="width:1.1rem;"> GitHub](https://github.com/madewithml/e2e-ml-app-pytorch) ┬╖ [<img src="https://avatars0.githubusercontent.com/u/60439358?s=200&v=4" style="width:1.2rem;"> Made With ML](https://madewithml.com)""", unsafe_allow_html=True)
st.write("Video lesson coming soon...")
# Get best run
project = 'mahjouri-saamahn/mwml-tutorial-app'
best_run = utils.get_best_run(project=project,
metric="test_loss", objective="minimize")
# Load best run (if needed)
best_run_dir = utils.load_run(run=best_run)
# Get run components for prediction
model, word_map = predict.get_run_components(run_dir=best_run_dir)
# Pages
page = st.sidebar.selectbox(
"Choose a page", ['Prediction', 'Model details'])
if page == 'Prediction':
st.header("ЁЯЪА Try it out!")
# Input text
text = st.text_input(
"Enter text to classify", value="The Canadian government officials proposed the new federal law.")
# Predict
results = predict.predict(inputs=[{'text': text}], model=model, word_map=word_map)