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| import streamlit as st | |
| import base64 | |
| from io import BytesIO | |
| import pandas as pd | |
| import numpy as np | |
| import pathlib | |
| import os | |
| from joblib import load | |
| import common | |
| from common import log_verbose | |
| file = st.file_uploader(label="Upload file for prediction", accept_multiple_files=False, type=["csv"], help="Select file to perform prediction on") | |
| #if selection made | |
| if file is not None: | |
| #prepare for possible multiple files | |
| input_df = common.preprocess_input_file(file) | |
| shapes_df = input_df.drop(columns=['id']) | |
| print('Found file with following dimensions: ' + str(shapes_df.shape)) | |
| # normalize input | |
| X_predict = common.apply_scaler(shapes_df) | |
| # apply PCA | |
| X_predict = common.check_apply_pca(X_predict) | |
| # perform all defined predictions | |
| for model_full_file in pathlib.Path(common.model_directory()).glob('*' + common.model_suffix): | |
| model_file = os.path.basename(model_full_file) | |
| model_name = model_file[:-len(common.model_suffix)] | |
| log_verbose(' Retrieving prediction model based on ' + model_name + ' from file: ' + str(model_full_file)) | |
| if model_name in common.one_hot_encoded: | |
| print(model_name + ' currently not supported') | |
| else: | |
| clf = load(model_full_file) | |
| input_df = common.append_predictions(clf, X_predict, model_name, input_df) | |
| # Get all entries for the "RandomForestClassifier" column | |
| random_forest_classifier_entries = input_df['RandomForestClassifier'] | |
| # Find the most frequent entry | |
| most_frequent_entry = random_forest_classifier_entries.value_counts().idxmax() | |
| # Print the most frequent entry | |
| st.write(" ") | |
| #st.markdown(f"Dataset classified as: **{most_frequent_entry}** <hr/>", unsafe_allow_html=True) | |
| st.markdown( | |
| f'<span style="font-size: 24px; color: green;">Dataset classified as: <b>{most_frequent_entry}</b></span> <hr/>', | |
| unsafe_allow_html=True, | |
| ) | |
| #st.dataframe(input_df) | |
| # provide excel download | |
| def to_excel(df): | |
| output = BytesIO() | |
| writer = pd.ExcelWriter(output, engine='xlsxwriter') | |
| df.to_excel(writer, index=False, sheet_name='Results', float_format="%.2f") | |
| writer.save() | |
| processed_data = output.getvalue() | |
| return processed_data | |
| def get_table_download_link(df): | |
| val = to_excel(df) | |
| b64 = base64.b64encode(val) | |
| return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="result.xlsx">Download detailed results as Excel file</a>' | |
| st.markdown(get_table_download_link(input_df), unsafe_allow_html=True) | |
| st.subheader('Cite') | |
| st.write('If you use this app, please consider citing the following two papers. Thanks in advance!') | |
| st.markdown( | |
| """ | |
| For developing SCattering Ai aNalysis: <br> | |
| Machine Learning-Assisted Analysis of Small Angle X-ray Scattering **2021** (https://doi.org/10.1109/SweDS53855.2021.9638297) | |
| """, unsafe_allow_html=True) | |
| st.markdown( | |
| """ | |
| For developing this HuggingFace app: <br> | |
| Machine Learning for Analysis of Experimental Scattering and Spectroscopy Data in Materials Chemistry **2023** (https://chemrxiv.org/engage/chemrxiv/article-details/64ed9d6079853bbd78a73fb0) | |
| """, unsafe_allow_html=True) | |