Add timeline tool

app.py

@@ -2,63 +2,69 @@ import re
 import argparse
 import torch
 import gradio as gr
+import pandas as pd
+import plotly.express as px
+import numpy as np
 from data import load_tokenizer
 from model import load_model
 from datetime import datetime
 from dateutil import parser
 from demo_assets import *
+from typing import List, Dict, Any
 
-parser = argparse.ArgumentParser()
-parser.add_argument('--data_dir', default='/data/mohamed/data')
-parser.add_argument('--aim_repo', default='/data/mohamed/')
-parser.add_argument('--ckpt', default='electra-base.pt')
-parser.add_argument('--aim_exp', default='mimic-decisions-1215')
-parser.add_argument('--label_encoding', default='multiclass')
-parser.add_argument('--multiclass', action='store_true')
-parser.add_argument('--debug', action='store_true')
-parser.add_argument('--save_losses', action='store_true')
-parser.add_argument('--task', default='token', choices=['seq', 'token'])
-parser.add_argument('--max_len', type=int, default=512)
-parser.add_argument('--num_layers', type=int, default=3)
-parser.add_argument('--kernels', nargs=3, type=int, default=[1,2,3])
-parser.add_argument('--model', default='roberta-base',)
-parser.add_argument('--model_name', default='google/electra-base-discriminator',)
-parser.add_argument('--gpu', default='0')
-parser.add_argument('--grad_accumulation', default=2, type=int)
-parser.add_argument('--pheno_id', type=int)
-parser.add_argument('--unseen_pheno', type=int)
-parser.add_argument('--text_subset')
-parser.add_argument('--pheno_n', type=int, default=500)
-parser.add_argument('--hidden_size', type=int, default=100)
-parser.add_argument('--emb_size', type=int, default=400)
-parser.add_argument('--total_steps', type=int, default=5000)
-parser.add_argument('--train_log', type=int, default=500)
-parser.add_argument('--val_log', type=int, default=1000)
-parser.add_argument('--seed', default = '0')
-parser.add_argument('--num_phenos', type=int, default=10)
-parser.add_argument('--num_decs', type=int, default=9)
-parser.add_argument('--num_umls_tags', type=int, default=33)
-parser.add_argument('--batch_size', type=int, default=8)
-parser.add_argument('--pos_weight', type=float, default=1.25)
-parser.add_argument('--alpha_distil', type=float, default=1)
-parser.add_argument('--distil', action='store_true')
-parser.add_argument('--distil_att', action='store_true')
-parser.add_argument('--distil_ckpt')
-parser.add_argument('--use_umls', action='store_true')
-parser.add_argument('--include_nolabel', action='store_true')
-parser.add_argument('--truncate_train', action='store_true')
-parser.add_argument('--truncate_eval', action='store_true')
-parser.add_argument('--load_ckpt', action='store_true')
-parser.add_argument('--gradio', action='store_true')
-parser.add_argument('--optuna', action='store_true')
-parser.add_argument('--mimic_data', action='store_true')
-parser.add_argument('--eval_only', action='store_true')
-parser.add_argument('--lr', type=float, default=4e-5)
-parser.add_argument('--resample', default='')
-parser.add_argument('--verbose', type=bool, default=True)
-parser.add_argument('--use_crf', type=bool)
-parser.add_argument('--print_spans', action='store_true')
-args = parser.parse_args()
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_dir', default='/data/mohamed/data')
+    parser.add_argument('--aim_repo', default='/data/mohamed/')
+    parser.add_argument('--ckpt', default='electra-base.pt')
+    parser.add_argument('--aim_exp', default='mimic-decisions-1215')
+    parser.add_argument('--label_encoding', default='multiclass')
+    parser.add_argument('--multiclass', action='store_true')
+    parser.add_argument('--debug', action='store_true')
+    parser.add_argument('--save_losses', action='store_true')
+    parser.add_argument('--task', default='token', choices=['seq', 'token'])
+    parser.add_argument('--max_len', type=int, default=512)
+    parser.add_argument('--num_layers', type=int, default=3)
+    parser.add_argument('--kernels', nargs=3, type=int, default=[1,2,3])
+    parser.add_argument('--model', default='roberta-base',)
+    parser.add_argument('--model_name', default='google/electra-base-discriminator',)
+    parser.add_argument('--gpu', default='0')
+    parser.add_argument('--grad_accumulation', default=2, type=int)
+    parser.add_argument('--pheno_id', type=int)
+    parser.add_argument('--unseen_pheno', type=int)
+    parser.add_argument('--text_subset')
+    parser.add_argument('--pheno_n', type=int, default=500)
+    parser.add_argument('--hidden_size', type=int, default=100)
+    parser.add_argument('--emb_size', type=int, default=400)
+    parser.add_argument('--total_steps', type=int, default=5000)
+    parser.add_argument('--train_log', type=int, default=500)
+    parser.add_argument('--val_log', type=int, default=1000)
+    parser.add_argument('--seed', default = '0')
+    parser.add_argument('--num_phenos', type=int, default=10)
+    parser.add_argument('--num_decs', type=int, default=9)
+    parser.add_argument('--num_umls_tags', type=int, default=33)
+    parser.add_argument('--batch_size', type=int, default=8)
+    parser.add_argument('--pos_weight', type=float, default=1.25)
+    parser.add_argument('--alpha_distil', type=float, default=1)
+    parser.add_argument('--distil', action='store_true')
+    parser.add_argument('--distil_att', action='store_true')
+    parser.add_argument('--distil_ckpt')
+    parser.add_argument('--use_umls', action='store_true')
+    parser.add_argument('--include_nolabel', action='store_true')
+    parser.add_argument('--truncate_train', action='store_true')
+    parser.add_argument('--truncate_eval', action='store_true')
+    parser.add_argument('--load_ckpt', action='store_true')
+    parser.add_argument('--gradio', action='store_true')
+    parser.add_argument('--optuna', action='store_true')
+    parser.add_argument('--mimic_data', action='store_true')
+    parser.add_argument('--eval_only', action='store_true')
+    parser.add_argument('--lr', type=float, default=4e-5)
+    parser.add_argument('--resample', default='')
+    parser.add_argument('--verbose', type=bool, default=True)
+    parser.add_argument('--use_crf', type=bool)
+    parser.add_argument('--print_spans', action='store_true')
+    return parser.parse_args()
+args = get_args()
 
 if args.task == 'seq' and args.pheno_id is not None:
     args.num_labels = 1
@@ -150,170 +156,262 @@ def extract_date(text):
 
 
 
-def run_gradio(model, tokenizer):
-    def predict(text):
-        encoding = tokenizer.encode_plus(text)
-        x = torch.tensor(encoding['input_ids']).unsqueeze(0).to(device)
-        mask = torch.ones_like(x)
-        output = model.generate(x, mask)[0]
-        return output, encoding.token_to_chars
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = load_tokenizer(args.model_name)
+model = load_model(args, device)[0]
+model.eval()
+torch.set_grad_enabled(False)
 
-    def process(text):
-        if text is not None:
-            output, t2c = predict(text)
-            tags = postprocess_labels(text, output, t2c)
-            with open('log.csv', 'a') as f:
-                f.write(f'{datetime.now()},{text}\n')
-            return list(zip(text, tags))
-        else:
-            return text
-
-    def process_sum(*inputs):
-        global sum_c
-        dates = {}
-        for i in range(sum_c):
-            text = inputs[i]
-            output, t2c = predict(text)
-            spans = indicators_to_spans(output.argmax(-1), t2c)
-            date = extract_date(text)
-            present_decs = set(cat for cat, _, _ in spans)
-            decs = {k: [] for k in sorted(present_decs)}
-            for c, s, e in spans:
-                decs[c].append(text[s:e])
-            dates[date] = decs
-
-        out = ""
-        for date in sorted(dates.keys(), key = lambda x: parser.parse(x)):
-            out += f'## **[{date}]**\n\n'
-            decs = dates[date]
-            for c in decs:
-                out += f'### {unicode_symbols[c]} ***{categories[c]}***\n\n'
-                for dec in decs[c]:
-                    out += f'{dec}\n\n'
-
-        return out
+def predict(text):
+    encoding = tokenizer.encode_plus(text)
+    x = torch.tensor(encoding['input_ids']).unsqueeze(0).to(device)
+    mask = torch.ones_like(x)
+    output = model.generate(x, mask)[0]
+    return output, encoding.token_to_chars
+
+def process(text):
+    if text is not None:
+        output, t2c = predict(text)
+        tags = postprocess_labels(text, output, t2c)
+        with open('log.csv', 'a') as f:
+            f.write(f'{datetime.now()},{text}\n')
+        return list(zip(text, tags))
+    else:
+        return text
 
+def process_sum(*inputs):
     global sum_c
-    sum_c = 1
-    SUM_INPUTS = 20
-    def update_inputs(inputs):
-        outputs = []
-        if inputs is None:
-            c = 0
-        else:
-            inputs = [open(f.name).read() for f in inputs]
-            for i, text in enumerate(inputs):
-                outputs.append(gr.update(value=text, visible=True))
-            c = len(inputs)
-
-        n = SUM_INPUTS
-        for i in range(n - c):
-            outputs.append(gr.update(value='', visible=False))
-        global sum_c; sum_c = c
-        return outputs
-
-    def add_ex(*inputs):
-        global sum_c
-        new_idx = sum_c
-        if new_idx < SUM_INPUTS:
-            out = inputs[:new_idx] + (gr.update(visible=True),) + inputs[new_idx+1:]
-            sum_c += 1
-        else:
-            out = inputs
-        return out
-
-    def sub_ex(*inputs):
-        global sum_c
-        new_idx = sum_c - 1
-        if new_idx > 0:
-            out = inputs[:new_idx] + (gr.update(visible=False),) + inputs[new_idx+1:]
-            sum_c -= 1
-        else:
-            out = inputs
-        return out
-
-
-    device = model.backbone.device
-    # colors = ['aqua', 'blue', 'fuchsia', 'teal', 'green', 'olive', 'lime', 'silver', 'purple', 'red',
-    #         'yellow', 'navy', 'gray', 'white', 'maroon', 'black']
-    colors = ['#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3', '#fdb462', '#b3de69', '#fccde5', '#d9d9d9', '#bc80bd']
-
-    color_map = {cat: colors[i] for i,cat in enumerate(categories)}
-
-    det_desc = ['Admit, discharge, follow-up, referral', 
-            'Ordering test, consulting colleague, seeking external information',
-            'Diagnostic conclusion, evaluation of health state, etiological inference, prognostic judgment',
-            'Quantitative or qualitative',
-            'Start, stop, alter, maintain, refrain',
-            'Start, stop, alter, maintain, refrain',
-            'Positive, negative, ambiguous test results',
-            'Transfer responsibility, wait and see, change subject',
-            'Advice or precaution',
-            'Sick leave, drug refund, insurance, disability']
-
-    desc = '### Zones (categories)\n'
-    desc += '| | |\n| --- | --- |\n'
-    for i,cat in enumerate(categories):
-        desc += f'| {unicode_symbols[i]} **{cat}** | {det_desc[i]}|\n'
-
-    #colors
-    #markdown labels
-    #legend and desc
-    #css font-size
-    css = '.category-legend {border:1px dashed black;}'\
-            '.text-sm {font-size: 1.5rem; line-height: 200%;}'\
-            '.gr-sample-textbox {width: 1000px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;}'\
-            '.text-limit label textarea {height: 150px !important; overflow: scroll; }'\
-            '.text-gray-500 {color: #111827; font-weight: 600; font-size: 1.25em; margin-top: 1.6em; margin-bottom: 0.6em;'\
-                    'line-height: 1.6;}'\
-            '#sum-out {border: 2px solid #007bff; padding: 20px; border-radius: 10px; box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);'
-    title='Clinical Decision Zoning'
-    with gr.Blocks(title=title, css=css) as demo:
-        gr.Markdown(f'# {title}')
-        with gr.Tab("Label a Clinical Note"):
+    dates = {}
+    for i in range(sum_c):
+        text = inputs[i]
+        output, t2c = predict(text)
+        spans = indicators_to_spans(output.argmax(-1), t2c)
+        date = extract_date(text)
+        present_decs = set(cat for cat, _, _ in spans)
+        decs = {k: [] for k in sorted(present_decs)}
+        for c, s, e in spans:
+            decs[c].append(text[s:e])
+        dates[date] = decs
+
+    out = ""
+    for date in sorted(dates.keys(), key = lambda x: parser.parse(x)):
+        out += f'## **[{date}]**\n\n'
+        decs = dates[date]
+        for c in decs:
+            out += f'### {unicode_symbols[c]} ***{categories[c]}***\n\n'
+            for dec in decs[c]:
+                out += f'{dec}\n\n'
+
+    return out
+
+
+def get_structured_data(*inputs):
+    global sum_c
+    data = []
+    for i in range(sum_c):
+        text = inputs[i]
+        output, t2c = predict(text)
+        spans = indicators_to_spans(output.argmax(-1), t2c)
+        date = extract_date(text)
+        for c, s, e in spans:
+            data.append({
+                'date': date,
+                'timestamp': parser.parse(date),
+                'decision_type': categories[c], 'details': text[s:e]})
+    return data
+
+def update_inputs(inputs):
+    outputs = []
+    if inputs is None:
+        c = 0
+    else:
+        inputs = [open(f.name).read() for f in inputs]
+        for i, text in enumerate(inputs):
+            outputs.append(gr.update(value=text, visible=True))
+        c = len(inputs)
+
+    n = SUM_INPUTS
+    for i in range(n - c):
+        outputs.append(gr.update(value='', visible=False))
+    global sum_c; sum_c = c
+    global structured_data
+    structured_data = get_structured_data(*inputs) if inputs is not None else []
+    return outputs
+
+def add_ex(*inputs):
+    global sum_c
+    new_idx = sum_c
+    if new_idx < SUM_INPUTS:
+        out = inputs[:new_idx] + (gr.update(visible=True),) + inputs[new_idx+1:]
+        sum_c += 1
+    else:
+        out = inputs
+    return out
+
+def sub_ex(*inputs):
+    global sum_c
+    new_idx = sum_c - 1
+    if new_idx > 0:
+        out = inputs[:new_idx] + (gr.update(visible=False),) + inputs[new_idx+1:]
+        sum_c -= 1
+    else:
+        out = inputs
+    return out
+
+
+def create_timeline_plot(data: List[Dict[str, Any]]):
+    df = pd.DataFrame(data)
+    # df['int_cat'] = pd.factorize(df['decision_type'])[0]
+    # df['int_cat_jittered'] = df['int_cat'] + np.random.uniform(-0.1, 0.1, size=len(df))
+    # fig = px.scatter(df, x='date', y='int_cat_jittered', color='decision_type', hover_data=['details'],
+    #                  title='Patient Timeline')
+    # fig.update_layout(
+    #         yaxis=dict(
+    #             tickmode='array',
+    #             tickvals=df['int_cat'].unique(),
+    #             ticktext=df['decision_type'].unique()),
+    #         xaxis_title='Date',
+    #         yaxis_title='Category')
+    fig = px.strip(df, x='date', y='decision_type', color='decision_type', hover_data=['details'],
+                   stripmode = "overlay",
+                     title='Patient Timeline')
+    fig.update_traces(jitter=1.0, marker=dict(size=10, opacity=0.6))
+    fig.update_layout(height=600)
+    return fig
+
+def filter_timeline(decision_type: str, start_date: str, end_date: str) -> px.scatter:
+    global structured_data
+    filtered_data = structured_data
+    if 'All' not in decision_types:
+        filtered_data = [event for event in filtered_data if event['decision_type'] in decision_types]
+    
+    start = parser.parse(start_date)
+    end = parser.parse(end_date)
+    filtered_data = [event for event in filtered_data if start <= event['timestamp'] <= end]
+    
+    return create_timeline_plot(filtered_data)
+
+def generate_summary(*inputs) -> str:
+    global structured_data
+    structured_data = get_structured_data(*inputs)
+    decision_types = {}
+    for event in structured_data:
+        decision_type = event['decision_type']
+        decision_types[decision_type] = decision_types.get(decision_type, 0) + 1
+    
+    summary = "Decision Type Summary:\n"
+    for decision_type, count in decision_types.items():
+        summary += f"{decision_type}: {count}\n"
+    return summary, create_timeline_plot(structured_data)
+
+global sum_c
+sum_c = 1
+SUM_INPUTS = 20
+structured_data = []
+
+device = model.backbone.device
+# colors = ['aqua', 'blue', 'fuchsia', 'teal', 'green', 'olive', 'lime', 'silver', 'purple', 'red',
+#         'yellow', 'navy', 'gray', 'white', 'maroon', 'black']
+colors = ['#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3', '#fdb462', '#b3de69', '#fccde5', '#d9d9d9', '#bc80bd']
+
+color_map = {cat: colors[i] for i,cat in enumerate(categories)}
+
+det_desc = ['Admit, discharge, follow-up, referral', 
+        'Ordering test, consulting colleague, seeking external information',
+        'Diagnostic conclusion, evaluation of health state, etiological inference, prognostic judgment',
+        'Quantitative or qualitative',
+        'Start, stop, alter, maintain, refrain',
+        'Start, stop, alter, maintain, refrain',
+        'Positive, negative, ambiguous test results',
+        'Transfer responsibility, wait and see, change subject',
+        'Advice or precaution',
+        'Sick leave, drug refund, insurance, disability']
+
+desc = '### Zones (categories)\n'
+desc += '| | |\n| --- | --- |\n'
+for i,cat in enumerate(categories):
+    desc += f'| {unicode_symbols[i]} **{cat}** | {det_desc[i]}|\n'
+
+#colors
+#markdown labels
+#legend and desc
+#css font-size
+css = '.category-legend {border:1px dashed black;}'\
+        '.text-sm {font-size: 1.5rem; line-height: 200%;}'\
+        '.gr-sample-textbox {width: 1000px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;}'\
+        '.text-limit label textarea {height: 150px !important; overflow: scroll; }'\
+        '.text-gray-500 {color: #111827; font-weight: 600; font-size: 1.25em; margin-top: 1.6em; margin-bottom: 0.6em;'\
+                'line-height: 1.6;}'\
+        '#sum-out {border: 2px solid #007bff; padding: 20px; border-radius: 10px; box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);'
+title='Clinical Decision Zoning'
+with gr.Blocks(title=title, css=css) as demo:
+    gr.Markdown(f'# {title}')
+    with gr.Tab("Label a Clinical Note"):
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## Enter a Discharge Summary or Clinical Note"),
+                text_input = gr.Textbox(
+                        # value=examples[0],
+                        label="",
+                        placeholder="Enter text here...")
+                text_btn = gr.Button('Run')
+            with gr.Column():
+                gr.Markdown("## Labeled Summary or Note"),
+                text_out = gr.Highlight(label="", combine_adjacent=True, show_legend=False, color_map=color_map)
+        gr.Examples(text_examples, inputs=text_input)
+    with gr.Tab("Summarize Patient History"):
+        with gr.Row():
+            with gr.Column():
+                sum_inputs = [gr.Text(label='Clinical Note 1', elem_classes='text-limit')]
+                sum_inputs.extend([gr.Text(label='Clinical Note %d'%i, visible=False, elem_classes='text-limit')
+                    for i in range(2, SUM_INPUTS + 1)])
+                sum_btn = gr.Button('Run')
+                with gr.Row():
+                    ex_add = gr.Button("+")
+                    ex_sub = gr.Button("-")
+                upload = gr.File(label='Upload clinical notes', file_types=['text'], file_count='multiple')
+                gr.Examples(sum_examples, inputs=upload,
+                        fn = update_inputs, outputs=sum_inputs, run_on_click=True)
+            with gr.Column():
+                gr.Markdown("## Summarized Clinical Decision History")
+                sum_out = gr.Markdown(elem_id='sum-out')
+    with gr.Tab("Timeline Visualization Tool"):
+        with gr.Column():
+            sum_inputs2 = [gr.Text(label='Clinical Note 1', elem_classes='text-limit')]
+            sum_inputs2.extend([gr.Text(label='Clinical Note %d'%i, visible=False, elem_classes='text-limit')
+                for i in range(2, SUM_INPUTS + 1)])
             with gr.Row():
-                with gr.Column():
-                    gr.Markdown("## Enter a Discharge Summary or Clinical Note"),
-                    text_input = gr.Textbox(
-                            # value=examples[0],
-                            label="",
-                            placeholder="Enter text here...")
-                    text_btn = gr.Button('Run')
-                with gr.Column():
-                    gr.Markdown("## Labeled Summary or Note"),
-                    text_out = gr.Highlight(label="", combine_adjacent=True, show_legend=False, color_map=color_map)
-            gr.Examples(text_examples, inputs=text_input)
-        with gr.Tab("Summarize Patient History"):
+                ex_add2 = gr.Button("+")
+                ex_sub2 = gr.Button("-")
+            upload2 = gr.File(label='Upload clinical notes', file_types=['text'], file_count='multiple')
+            gr.Examples(sum_examples, inputs=upload2,
+                    fn = update_inputs, outputs=sum_inputs2, run_on_click=True)
+        with gr.Column():
             with gr.Row():
-                with gr.Column():
-                    sum_inputs = [gr.Text(label='Clinical Note 1', elem_classes='text-limit')]
-                    sum_inputs.extend([gr.Text(label='Clinical Note %d'%i, visible=False, elem_classes='text-limit')
-                        for i in range(2, SUM_INPUTS + 1)])
-                    sum_btn = gr.Button('Run')
-                    with gr.Row():
-                        ex_add = gr.Button("+")
-                        ex_sub = gr.Button("-")
-                    upload = gr.File(label='Upload clinical notes', file_type='text', file_count='multiple')
-                    gr.Examples(sum_examples, inputs=upload,
-                            fn = update_inputs, outputs=sum_inputs, run_on_click=True)
-                with gr.Column():
-                    gr.Markdown("## Summarized Clinical Decision History")
-                    sum_out = gr.Markdown(elem_id='sum-out')
-        gr.Markdown(desc)
-
-        # Functions
-        text_input.submit(process, inputs=text_input, outputs=text_out)
-        text_btn.click(process, inputs=text_input, outputs=text_out)
-        upload.change(update_inputs, inputs=upload, outputs=sum_inputs)
-        ex_add.click(add_ex, inputs=sum_inputs, outputs=sum_inputs)
-        ex_sub.click(sub_ex, inputs=sum_inputs, outputs=sum_inputs)
-        sum_btn.click(process_sum, inputs=sum_inputs, outputs=sum_out)
-    # demo = gr.TabbedInterface([text_demo, sum_demo], ["Label a Clinical Note", "Summarize Patient History"])
-    demo.launch(share=False)
+                decision_type = gr.Dropdown(["All"] + categories,
+                                            multiselect=True,
+                                            label="Decision Type", value="All")
+                start_date = gr.Textbox(label="Start Date (MM/DD/YYYY)", value="01/01/2006")
+                end_date = gr.Textbox(label="End Date (MM/DD/YYYY)", value="12/31/2024")
+            
+            filter_button = gr.Button("Filter Timeline")
+            
+            timeline_plot = gr.Plot()
+            
+            summary_button = gr.Button("Generate Summary")
+            summary_output = gr.Textbox(label="Summary")
+    gr.Markdown(desc)
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-tokenizer = load_tokenizer(args.model_name)
-model = load_model(args, device)[0]
-model.eval()
-torch.set_grad_enabled(False)
-run_gradio(model, tokenizer)
+    # Functions
+    text_input.submit(process, inputs=text_input, outputs=text_out)
+    text_btn.click(process, inputs=text_input, outputs=text_out)
+    upload.change(update_inputs, inputs=upload, outputs=sum_inputs)
+    upload2.change(update_inputs, inputs=upload2, outputs=sum_inputs2)
+    ex_add.click(add_ex, inputs=sum_inputs, outputs=sum_inputs)
+    ex_sub.click(sub_ex, inputs=sum_inputs, outputs=sum_inputs)
+    ex_add2.click(add_ex, inputs=sum_inputs2, outputs=sum_inputs2)
+    ex_sub2.click(sub_ex, inputs=sum_inputs2, outputs=sum_inputs2)
+    sum_btn.click(process_sum, inputs=sum_inputs, outputs=sum_out)
+    filter_button.click(filter_timeline, inputs=[decision_type, start_date, end_date], outputs=timeline_plot)
+    summary_button.click(generate_summary, inputs=sum_inputs2, outputs=[summary_output, timeline_plot])
+demo.launch(share=True)