import wandb import json from text_classification import config, data, predict, utils 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,
def normalize(x): return (x - min(x)) / (max(x) - min(x)) # 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-tensorflow) ┬╖ [<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 = 'GokuMohandas/e2e-ml-app-tensorflow' 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 args, model, conv_outputs_model, X_tokenizer, y_tokenizer = 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!")
if __name__ == '__main__': # Arguments parser = ArgumentParser() parser.add_argument('--text', type=str, required=True, help="text to predict") args = parser.parse_args() inputs = [{'text': args.text}] # Get best run best_run = utils.get_best_run( project="mahjouri-saamahn/mwml-app-tensorflow", 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, conv_outputs_model, X_tokenizer, y_tokenizer = get_run_components( run_dir=best_run_dir) # Predict results = predict(inputs=inputs, args=args, model=model, conv_outputs_model=conv_outputs_model, X_tokenizer=X_tokenizer,
conv_outputs=conv_outputs, filter_sizes=args.filter_sizes)}) return results if __name__ == '__main__': # Arguments parser = ArgumentParser() parser.add_argument('--text', type=str, required=True, help="text to predict") args = parser.parse_args() inputs = [{'text': args.text}] # Get best run best_run = utils.get_best_run(project="GokuMohandas/e2e-ml-app-tensorflow", 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, conv_outputs_model, X_tokenizer, y_tokenizer = get_run_components( run_dir=best_run_dir) # Predict results = predict(inputs=inputs, args=args, model=model, conv_outputs_model=conv_outputs_model, X_tokenizer=X_tokenizer, y_tokenizer=y_tokenizer) config.logger.info(json.dumps(results, indent=4, sort_keys=False))
results.append(performance) return results if __name__ == '__main__': # Arguments parser = ArgumentParser() parser.add_argument('--text', type=str, required=True, help="text to predict") args = parser.parse_args() inputs = [{'text': args.text}] # Get best run best_run = utils.get_best_run(project="mahjouri-saamahn/mwml-tutorial-app", 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 = get_run_components(run_dir=best_run_dir) # Predict results = predict(inputs=inputs, args=args, model=model, word_map=word_map) config.logger.info(json.dumps(results, indent=4, sort_keys=False))