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|
| | import os |
| | import streamlit as st |
| | import streamlit.components.v1 as components |
| | import time |
| | import matplotlib.pyplot as plt |
| | import pandas as pd |
| | from pipeline import Pipeline |
| | import html |
| | from IPython.core.display import display, HTML |
| | import json |
| | from PIL import Image |
| | from tqdm import tqdm |
| | import logging |
| | from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts |
| | from htbuilder.units import percent, px |
| | from htbuilder.funcs import rgba, rgb |
| | import copy |
| | from download_models import check_if_exist |
| | import re |
| | import numpy as np |
| | from sklearn.manifold import TSNE |
| | from sklearn.decomposition import PCA |
| | import plotly.express as plotpx |
| | import umap |
| |
|
| | def image(src_as_string, **style): |
| | return img(src=src_as_string, style=styles(**style)) |
| |
|
| |
|
| | def link(link, text, **style): |
| | return a(_href=link, _target="_blank", style=styles(**style))(text) |
| |
|
| | def update_highlight(current,old): |
| | out = current |
| | matches_background_new = [(m.start(0), m.end(0)) for m in re.finditer("background-color:rgba\\(234, 131, 4,", out)] |
| | matches_background_old = [(m.start(0), m.end(0)) for m in re.finditer("background-color:rgba\\(234, 131, 4,", old)] |
| | for x,y in zip(matches_background_old,matches_background_new): |
| | try: |
| | old_importance = re.search("\\d+\\.\\d+",old[x[1]:x[1]+20]) |
| | new_importance = re.search("\\d+\\.\\d+",current[y[1]:y[1]+20]) |
| |
|
| | if int(out[y[1]]) ==0 and float(old[x[1]]) != 0: |
| | out = out[0:y[1]] + str(old_importance.group(0)) + out[y[1]:] |
| | return False,out |
| | if float(out[y[1]]) !=0 and float(old[x[1]]) != 0: |
| | if float(old[x[1]]) > float(out[y[1]]): |
| | out = out[0:y[1]] + str(old_importance.group(0))[0] + out[y[1]:] |
| | return False,out |
| | except Exception as e: |
| | return True, out |
| |
|
| | return True,out |
| |
|
| | def hidde_menu(): |
| |
|
| | footer_style = """<style> |
| | footer { |
| | visibility: hidden; |
| | } |
| | footer:after { |
| | content:"An end-to-end Breast Pathology Classification System to infer Breast Cancer Diagnosis and Severity"; |
| | visibility: visible; |
| | display: block; |
| | position: center; |
| | #background-color: red; |
| | padding: 5px; |
| | top: 2px; |
| | } |
| | </style> |
| | """ |
| |
|
| | st.markdown(footer_style, unsafe_allow_html=True) |
| |
|
| | def main(myargs): |
| | project_dir = os.path.dirname(os.path.abspath(__file__)) |
| |
|
| |
|
| | def add_content(columns): |
| | if 'hg_df' in st.session_state: |
| | columns[1].dataframe(st.session_state.hg_df) |
| | if 'all_l' in st.session_state: |
| | columns[2].dataframe(st.session_state.all_l) |
| |
|
| | if "highlight_samples" in st.session_state: |
| |
|
| | if "selected_indices" in st.session_state: |
| | if len(st.session_state.selected_indices) >0: |
| | out = "" |
| | l = st.session_state.selected_indices |
| | l.sort() |
| | for ind in l: |
| | out += st.session_state.highlight_samples[ind] + "<br><br>" |
| | components.html(out,scrolling=True) |
| | else: |
| | components.html(st.session_state.highlight_samples[0]) |
| | else: |
| | components.html(st.session_state.highlight_samples[0]) |
| |
|
| |
|
| | |
| | if st.session_state['input_type'] == 'File' and "embeddings_all" in st.session_state and st.session_state.embeddings_plot in ["2D", "3D"]: |
| | indices = [x for x in range(st.session_state.data_df[st.session_state.input_column].values.shape[0])] |
| | if "selected_indices" in st.session_state: |
| | if len(st.session_state.selected_indices) >=4: |
| | l = st.session_state.selected_indices |
| | l.sort() |
| | indices = l |
| |
|
| | if st.session_state.data_df[st.session_state.input_column].values.shape[0] >=2: |
| | sub_embeddings = st.session_state.embeddings_all[indices] |
| | sentences = st.session_state.data_df[st.session_state.input_column].values[indices] |
| | sentences_parses = [] |
| | break_size = 20 |
| | for data in sentences: |
| | d = data.split() |
| | size_sentence = len(d) |
| | if len(d) >break_size: |
| | out = "" |
| | for lower_bound in range(0,size_sentence, break_size): |
| | upper_bound = lower_bound + break_size if lower_bound + break_size <= size_sentence else size_sentence |
| | out += " ".join(x for x in d[lower_bound:upper_bound]) + "<br>" |
| | sentences_parses.append(out) |
| | else: |
| | sentences_parses.append(data) |
| |
|
| | |
| |
|
| | prediction_label = st.session_state.hg_df["Prediction"].values[indices] |
| | prediction_worst_label = [] |
| | for pred in prediction_label: |
| | preds = pred.split(" | ") |
| | if len(preds) ==1: |
| | prediction_worst_label.extend(preds) |
| | else: |
| | worst_index = min([st.session_state.predictor.bert_model.config['worst_rank'].index(x) for x in preds]) |
| | prediction_worst_label.append(st.session_state.predictor.bert_model.config['worst_rank'][worst_index]) |
| | |
| |
|
| | if st.session_state.embeddings_type == "PCA": |
| |
|
| | low_dim_embeddings = PCA(n_components=3).fit_transform(sub_embeddings) |
| | elif st.session_state.embeddings_type == "TSNE": |
| | low_dim_embeddings = TSNE(n_components=3,init="pca",perplexity=st.session_state.perplexity,learning_rate=st.session_state.learning_rate).fit_transform(sub_embeddings) |
| |
|
| | else: |
| | n_neighbors = min(st.session_state.n_neighbors, len(sub_embeddings)-1 ) |
| | low_dim_embeddings = umap.UMAP(n_neighbors=n_neighbors, min_dist=st.session_state.min_dist,n_components=3).fit(sub_embeddings).embedding_ |
| |
|
| | df_embeddings = pd.DataFrame(low_dim_embeddings) |
| | df_embeddings = df_embeddings.rename(columns={0:'x',1:'y',2:'z'}) |
| | df_embeddings = df_embeddings.assign(severity=prediction_worst_label) |
| | df_embeddings = df_embeddings.assign(text=sentences_parses) |
| | df_embeddings = df_embeddings.assign(data_index=indices) |
| | df_embeddings = df_embeddings.assign(all_predictions=prediction_label) |
| | |
| |
|
| | if st.session_state.embeddings_plot == "2D": |
| | |
| | plot = plotpx.scatter( |
| | df_embeddings, x='x', y='y', |
| | color='severity', labels={'color': 'severity'}, |
| | hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 4) to display specific examples' |
| | ) |
| |
|
| | else: |
| | |
| | plot = plotpx.scatter_3d( |
| | df_embeddings, x='x', y='y', z='z', |
| | color='severity', labels={'color': 'severity'}, |
| | hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 4) to display specific examples' |
| | ) |
| |
|
| | st.plotly_chart(plot,use_container_width=True,) |
| |
|
| |
|
| | |
| |
|
| | if 'bert_lime_output' in st.session_state and st.session_state.bert_lime: |
| | if len(st.session_state.bert_lime_output) >0: |
| | st.markdown("BERT Interpretability") |
| | components.html(st.session_state.bert_lime_output[0]) |
| |
|
| | if 'json_output' in st.session_state and st.session_state.json_out: |
| |
|
| | st.markdown("Here are your analysis results in JSON format:") |
| | out = {} |
| | if "selected_indices" in st.session_state: |
| | |
| | if len(st.session_state.selected_indices) >0: |
| | l = st.session_state.selected_indices |
| | l.sort() |
| | for ind in l: |
| | out['sample_'+str(ind)] = st.session_state.json_output['sample_'+str(ind)] |
| | st.json(out) |
| | else: |
| | out['sample_'+str(0)] = st.session_state.json_output['sample_'+str(0)] |
| | st.json(out) |
| | else: |
| | |
| | out['sample_'+str(0)] = st.session_state.json_output['sample_'+str(0)] |
| | st.json(out) |
| |
|
| |
|
| | def delete_var_session(keys:list): |
| | for key in keys: |
| | if key in st.session_state: |
| | del st.session_state[key] |
| |
|
| | im = Image.open(os.path.join(project_dir, "imgs/icon.png")) |
| |
|
| |
|
| | |
| | st.set_page_config(page_title='HCSBC', layout = 'wide',page_icon=im,menu_items={ |
| | 'Get Help': 'https://github.com/thiagosantos1/BreastPathologyClassificationSystem', |
| | 'Report a bug': "https://github.com/thiagosantos1/BreastPathologyClassificationSystem", |
| | 'About': "An end-to-end breast pathology classification system https://github.com/thiagosantos1/BreastPathologyClassificationSystem" |
| | }) |
| | st.sidebar.image(os.path.join(project_dir,"imgs/doctor.png"),use_column_width=False) |
| |
|
| | |
| | st.markdown("<h1 style='text-align: center; color: black;'>HCSBC: Hierarchical Classification System for Breast Cancer Specimen Report</h1>", unsafe_allow_html=True) |
| | st.markdown("System Pipeline: Pathology Emory Pubmed BERT + 6 independent Machine Learning discriminators") |
| | |
| | st.write('\n') |
| | |
| | st.markdown("*Hint: click on the top-right corner to enlarge it!*") |
| | |
| |
|
| | cols = st.columns((1, 1, 1)) |
| | |
| | cols[0].subheader("Input Data") |
| | cols[1].subheader("Severity Predictions") |
| | cols[2].subheader("Diagnose Predictions") |
| |
|
| | |
| | |
| | st.sidebar.title("Data Selection") |
| | |
| | st.session_state['input_type'] = st.sidebar.radio("Input Selection", ('File', 'Text'), key="data_format",index=1) |
| | if "prev_input_type" not in st.session_state: |
| | st.session_state['prev_input_type'] = st.session_state.input_type |
| |
|
| | st.write('<style>div.row-widget.stRadio > div{flex-direction:row;}</style>', unsafe_allow_html=True) |
| |
|
| |
|
| | |
| | st.set_option('deprecation.showfileUploaderEncoding', False) |
| |
|
| |
|
| | if st.session_state['input_type'] == 'File': |
| | if st.session_state['prev_input_type'] == 'Text': |
| | delete_var_session(keys=["data_df","data_columns","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| | st.session_state['prev_input_type'] = "File" |
| |
|
| | |
| | new_file = st.sidebar.file_uploader("Upload Document", type=['xlsx','csv']) |
| | if 'uploaded_file' in st.session_state and st.session_state.uploaded_file != None and new_file != None: |
| | if st.session_state.uploaded_file.name != new_file.name and st.session_state.uploaded_file.id != new_file.id: |
| | delete_var_session(keys=["data_df","data_columns","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| | |
| | st.session_state['uploaded_file'] = new_file |
| |
|
| | data_columns = ['Input'] |
| | if 'data_columns' not in st.session_state: |
| | st.session_state['data_columns'] = data_columns |
| |
|
| | if st.session_state.uploaded_file is not None: |
| | if 'data_df' not in st.session_state: |
| | if st.session_state.uploaded_file.name.endswith('.xlsx'): |
| | df = pd.read_excel(st.session_state.uploaded_file) |
| | else: |
| | df = pd.read_csv(st.session_state.uploaded_file) |
| | |
| | df = df.loc[:, ~df.columns.str.contains('^Unnamed')] |
| | df = df.fillna("NA") |
| | data_columns = df.columns.values |
| | st.session_state['data_df'] = df |
| | st.session_state['data_columns'] = data_columns |
| | else: |
| | if st.session_state['prev_input_type'] == 'File': |
| | delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| | st.session_state['prev_input_type'] = "Text" |
| |
|
| | input_column = "Input" |
| | data = st.sidebar.text_area("Please enter a breast cancer pathology diagnose",value="BRWIRE Left wire directed segmntal mastectomy; short suture, superior; long suture, lateral breast, left, wire-directed segmental mastectomy: - infiltrating ductal carcinoma, nottingham grade i, 0.8 cm in maximum gross dimension. - ductal carcinoma in situ, low nuclear grade, solid and cribriform types, associated with microcalcifications and partially involving a small intraductal papilloma (0.2 cm). - invasive and in situ carcinoma extend to within 0.2 cm of the anterior specimen edge separately submitted margin specimen below). - no angiolymphatic invasion identifie - adjacent breast with biopsy site changes, a small intraductal papilloma (0.2 cm), and fibrocystic changes. - see synoptic report.") |
| | if "user_input" in st.session_state: |
| | if data != st.session_state.user_input: |
| | delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| |
|
| | st.session_state['user_input'] = data |
| | if len(st.session_state.user_input.split()) >0: |
| | st.session_state['data_df'] = pd.DataFrame([st.session_state['user_input']], columns =[input_column]) |
| | st.session_state['input_column'] = input_column |
| | st.session_state['uploaded_file'] = True |
| | else: |
| | delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| |
|
| |
|
| | if 'data_df' in st.session_state: |
| | cols[0].dataframe(st.session_state.data_df) |
| |
|
| |
|
| | if st.session_state['input_type'] == 'File': |
| | |
| | st.sidebar.write('\n') |
| | st.sidebar.title("Column For Prediction") |
| | input_column = st.sidebar.selectbox("Columns", st.session_state.data_columns) |
| |
|
| | st.session_state['input_column'] = input_column |
| |
|
| |
|
| | st.sidebar.write('\n') |
| | st.sidebar.title("Severity Model") |
| | input_higher = st.sidebar.selectbox("Model", ["PathologyEmoryPubMedBERT"]) |
| | st.session_state['input_higher'] = input_higher |
| |
|
| | if "prev_input_higher" not in st.session_state: |
| | st.session_state['prev_input_higher'] = st.session_state.input_higher |
| | st.session_state['input_higher_exist'] = check_if_exist(st.session_state.input_higher) |
| | st.session_state['load_new_higher_model'] = True |
| | elif st.session_state.prev_input_higher != st.session_state.input_higher: |
| | st.session_state['input_higher_exist'] = check_if_exist(st.session_state.input_higher) |
| | st.session_state['prev_input_higher'] = st.session_state.input_higher |
| | st.session_state['load_new_higher_model'] = True |
| | delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| | |
| |
|
| | st.sidebar.write('\n') |
| | st.sidebar.title("Diagnosis Model") |
| | input_all_labels = st.sidebar.selectbox("Model", ['single_vectorizer', 'branch_vectorizer']) |
| | st.session_state['input_all_labels'] = input_all_labels |
| |
|
| | if "prev_input_all_labels" not in st.session_state: |
| | st.session_state['prev_input_all_labels'] = st.session_state.input_all_labels |
| | st.session_state['input_all_labels_exist'] = check_if_exist(st.session_state.input_all_labels) |
| | st.session_state['load_new_all_label_model'] = True |
| | elif st.session_state.prev_input_all_labels != st.session_state.input_all_labels: |
| | st.session_state['input_all_labels_exist'] = check_if_exist(st.session_state.input_all_labels) |
| | st.session_state['prev_input_all_labels'] = st.session_state.input_all_labels |
| | st.session_state['load_new_all_label_model'] = True |
| | delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"]) |
| | |
| |
|
| | |
| | st.sidebar.write('\n') |
| | st.sidebar.title("Analysis Options") |
| |
|
| | predictions, json_output, higher_order_pred,all_labels_pred,higher_order_prob,all_labels_prob = {},[],[],[],[],[] |
| | hg_df, all_l,highlight_samples, bert_lime_output, embeddings_all= [],[],[],[],[] |
| |
|
| |
|
| | if st.session_state['input_type'] == 'File': |
| | embeddings_plot = st.sidebar.radio('Display embeddings plot', |
| | ['2D', |
| | '3D', |
| | 'Dont Display'],index=1) |
| |
|
| | st.session_state['embeddings_plot'] = embeddings_plot |
| |
|
| | else: |
| | st.session_state['embeddings_plot'] = 'Dont Display' |
| |
|
| | if st.session_state['input_type'] == 'File': |
| | embeddings_type = st.sidebar.radio('Dimensionality Reduction', |
| | ['PCA', |
| | 'TSNE','UMAP'],index=0) |
| |
|
| | st.session_state['embeddings_type'] = embeddings_type |
| |
|
| | if st.session_state.embeddings_type == "TSNE": |
| | perplexity = st.sidebar.slider("Perplexity", min_value=5, max_value=100, step=5, value=30) |
| | st.session_state['perplexity'] = perplexity |
| |
|
| | learning_rate = st.sidebar.slider("Learning Rate", min_value=10, max_value=1000, step=10, value=100) |
| | st.session_state['learning_rate'] = learning_rate |
| |
|
| | if st.session_state.embeddings_type == "UMAP": |
| | n_neighbors = st.sidebar.slider("Neighbors", min_value=2, max_value=100, step=1, value=2) |
| | st.session_state['n_neighbors'] = n_neighbors |
| |
|
| | min_dist = st.sidebar.slider("Minimal Distance", min_value=0.1, max_value=0.99, step=0.05, value=0.1) |
| | st.session_state['min_dist'] = min_dist |
| |
|
| | json_out = st.sidebar.checkbox('Display Json',value = True,key='check1') |
| | st.session_state['json_out'] = json_out |
| |
|
| | if st.session_state['input_type'] == 'Text': |
| | bert_lime = st.sidebar.checkbox('Display BERT Interpretability',value = False,key='check2') |
| | st.session_state['bert_lime'] = bert_lime |
| | else: |
| | st.session_state['bert_lime'] = False |
| |
|
| |
|
| | |
| | st.sidebar.write('\n') |
| | st.sidebar.title("Prediction") |
| |
|
| |
|
| | if st.sidebar.button("Run Prediction"): |
| |
|
| | if st.session_state.uploaded_file is None: |
| | st.sidebar.write("Please upload a your data") |
| |
|
| | else: |
| | st.session_state['input_all_labels_exist'] = check_if_exist(st.session_state.input_all_labels) |
| | if not st.session_state.input_all_labels_exist: |
| | st.sidebar.write("Please Download Model: " + str(st.session_state.input_all_labels)) |
| |
|
| | st.session_state['input_higher_exist'] = check_if_exist(st.session_state.input_higher) |
| | if not st.session_state.input_higher_exist: |
| | st.sidebar.write("Please Download Model: " + str(st.session_state.input_higher)) |
| |
|
| | if st.session_state.input_all_labels_exist and st.session_state.input_higher_exist: |
| | if "predictor" not in st.session_state or st.session_state.load_new_higher_model or st.session_state.load_new_all_label_model: |
| | with st.spinner('Loading model...'): |
| | print("\n\tLoading Model") |
| | st.session_state["predictor"] = Pipeline(bert_option=str(st.session_state.input_higher), branch_option=str(st.session_state.input_all_labels)) |
| | st.session_state['load_new_higher_model'] = False |
| | st.session_state['load_new_all_label_model'] = False |
| |
|
| | with st.spinner('Transforming Data...'): |
| | data = st.session_state.data_df[st.session_state.input_column].values |
| |
|
| | with st.spinner('Analyzing...'): |
| | time.sleep(0.1) |
| | prog_bar = st.progress(0) |
| | logging.info("Running Predictions for data size of: " + str(len(data))) |
| | logging.info("\n\tRunning Predictions with: " + str(st.session_state.input_higher) + str(st.session_state.input_all_labels)) |
| | for index in tqdm(range(len(data))): |
| | d = data[index] |
| | time.sleep(0.1) |
| | prog_bar.progress(int( (100/len(data)) * (index+1) )) |
| | |
| | preds,embeddings_output = st.session_state.predictor.run(d) |
| | embeddings = embeddings_output.tolist() |
| | embeddings_all.append(embeddings[0]) |
| | if st.session_state.bert_lime: |
| | logging.info("Running BERT LIME Interpretability Predictions") |
| | bert_lime_output.append(st.session_state.predictor.bert_interpretability(d)) |
| |
|
| | predictions["sample_" + str(index)] = {} |
| | for ind,pred in enumerate(preds): |
| | predictions["sample_" + str(index)]["prediction_" + str(ind)] = pred |
| | |
| |
|
| | prog_bar.progress(100) |
| | time.sleep(0.1) |
| | |
| | for key,sample in predictions.items(): |
| | higher,all_p, prob_higher, prob_all = [],[],[],[] |
| | for key,pred in sample.items(): |
| | for higher_order, sub_arr in pred.items(): |
| | higher.append(higher_order) |
| | prob_higher.append(round(sub_arr["probability"], 2)) |
| | for label,v in sub_arr['diagnose'].items(): |
| | all_p.append(label) |
| | prob_all.append(round(v["probability"], 2)) |
| |
|
| | higher_order_pred.append(" | ".join(x for x in higher)) |
| | all_labels_pred.append(" | ".join(x for x in all_p)) |
| |
|
| | higher_order_prob.append(" | ".join(str(x) for x in prob_higher)) |
| | all_labels_prob.append(" | ".join(str(x) for x in prob_all)) |
| |
|
| | predictions_refact = copy.deepcopy(predictions) |
| |
|
| | for index in tqdm(range(len(data))): |
| | highlights = "" |
| | key = "sample_" + str(index) |
| | for k,v in predictions[key].items(): |
| | for k_s, v_s in v.items(): |
| | predictions_refact["sample_" + str(index)]["data"] = v_s['data'] |
| | predictions_refact["sample_" + str(index)]["transformer_data"] = v_s['transformer_data'] |
| | predictions_refact["sample_" + str(index)]["discriminator_data"] = v_s['word_analysis']['discriminator_data'] |
| | highlight = v_s['word_analysis']['highlighted_html_text'] |
| | |
| | if len(highlights) >0: |
| | done = False |
| | merged = highlight |
| | while not done: |
| | done,merged = update_highlight(merged,highlights) |
| |
|
| | highlights = merged |
| | else: |
| | highlights = highlight |
| |
|
| | del predictions_refact[key][k][k_s]['data'] |
| | del predictions_refact[key][k][k_s]['transformer_data'] |
| | del predictions_refact[key][k][k_s]['word_analysis']['discriminator_data'] |
| |
|
| | highlight_samples.append(highlights) |
| |
|
| | json_output = predictions_refact |
| | |
| | hg_df = pd.DataFrame(list(zip(higher_order_pred, higher_order_prob)), columns =['Prediction', "Probability"]) |
| | all_l = pd.DataFrame(list(zip(all_labels_pred,all_labels_prob)), columns =['Prediction',"Probability"]) |
| | all_preds = pd.DataFrame(list(zip(higher_order_pred, all_labels_pred)), columns =['Severity Prediction',"Diagnose Prediction"]) |
| |
|
| | st.session_state['hg_df'] = hg_df |
| | st.session_state['all_l'] = all_l |
| | st.session_state['all_preds'] = all_preds |
| | st.session_state['json_output'] = json_output |
| | st.session_state['highlight_samples'] = highlight_samples |
| | st.session_state['highlight_samples_df'] = pd.DataFrame(highlight_samples, columns =["HTML Word Importance"]) |
| | st.session_state['bert_lime_output'] = bert_lime_output |
| | st.session_state['embeddings_all'] = np.asarray(embeddings_all) |
| |
|
| | if 'data_df' in st.session_state and 'json_output' in st.session_state: |
| | st.markdown("<h1 style='text-align: center; color: purple;'>Model Analysis</h1>", unsafe_allow_html=True) |
| | selected_indices = st.multiselect('Select Rows to Display Word Importance, Embeddings Visualization, and Json Analysis:', [x for x in range(len(st.session_state.data_df))]) |
| | st.session_state['selected_indices'] = selected_indices |
| |
|
| | add_content(cols) |
| | |
| |
|
| | if 'json_output' in st.session_state: |
| | st.sidebar.write('\n') |
| | st.sidebar.title("Save Results") |
| |
|
| | st.sidebar.write('\n') |
| | st.sidebar.download_button( |
| | label="Download Output Json", |
| | data=str(st.session_state.json_output), |
| | file_name="output.json", |
| | ) |
| | st.sidebar.download_button( |
| | label="Download Predictions", |
| | data=st.session_state.all_preds.to_csv(), |
| | file_name="predictions.csv", |
| | ) |
| | st.sidebar.download_button( |
| | label="Download Data + Predictions", |
| | data = pd.concat([st.session_state.data_df, st.session_state.all_preds,st.session_state.highlight_samples_df], axis=1, join='inner').to_csv(), |
| | file_name="data_predictions.csv", |
| | ) |
| | |
| | st.sidebar.write('\n') |
| | st.sidebar.title("Contact Me") |
| | sub_colms = st.sidebar.columns([1, 1, 1]) |
| | sub_colms[0].markdown('''<a href="https://github.com/thiagosantos1/BreastPathologyClassificationSystem"> |
| | <img src="https://img.icons8.com/fluency/48/000000/github.png" /></a>''',unsafe_allow_html=True) |
| | sub_colms[1].markdown('''<a href="https://twitter.com/intent/follow?original_referer=https%3A%2F%2Fgithub.com%2Ftsantos_maia&screen_name=tsantos_maia"> |
| | <img src="https://img.icons8.com/color/48/000000/twitter--v1.png" /></a>''',unsafe_allow_html=True) |
| | sub_colms[2].markdown('''<a href="https://www.linkedin.com/in/thiagosantos-cs/"> |
| | <img src="https://img.icons8.com/color/48/000000/linkedin.png" /></a>''',unsafe_allow_html=True) |
| | |
| |
|
| | hidde_menu() |
| |
|
| | |
| |
|
| |
|
| | if __name__ == '__main__': |
| |
|
| | myargs = [ |
| | "Made in ", |
| | image('https://avatars3.githubusercontent.com/u/45109972?s=400&v=4', |
| | width=px(25), height=px(25)), |
| | " with ❤️ by ", |
| | link("https://www.linkedin.com/in/thiagosantos-cs/", "@thiagosantos-cs"), |
| | br(), |
| | link("https://www.linkedin.com/in/thiagosantos-cs/", image('https://img.icons8.com/color/48/000000/twitter--v1.png')), |
| | link("https://github.com/thiagosantos1/BreastPathologyClassificationSystem", image('https://img.icons8.com/fluency/48/000000/github.png')), |
| | ] |
| | logging.basicConfig( |
| | format="%(asctime)s - %(levelname)s - %(filename)s - %(message)s", |
| | datefmt="%d/%m/%Y %H:%M:%S", |
| | level=logging.INFO) |
| | main(myargs) |
| |
|
| |
|
| |
|
