async def _predict(payload: PredictPayload):
if payload.experiment_id == 'latest':
payload.experiment_id = max(os.listdir(config.EXPERIMENTS_DIR))
prediction = predict.predict(experiment_id=payload.experiment_id,
text=payload.text)
response = {
'message': HTTPStatus.OK.phrase,
'status-code': HTTPStatus.OK,
'data': {
"prediction": prediction
}
}
config.logger.info(json.dumps(response, indent=2))
return response
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)
# Results
raw_text = results[0]['raw_input']
st.write("**Raw text**:", raw_text)
preprocessed_text = results[0]['preprocessed_input']
st.write("**Preprocessed text**:", preprocessed_text)
st.write("**Probabilities**:")
st.json(results[0]['probabilities'])
# Interpretability
st.write("**Top n-grams**:")
words = preprocessed_text.split(' ')
top_n_grams = {}
token_index_to_freq = {i: 0 for i in range(len(words))}
def predict_fn(input_data, model):
prediction = predict.predict(
experiment_id='latest', inputs=input_data)
return prediction
