PhDLLM

pip install dash dash-cytoscape plotly
python llm_workflow.py
import dash
from dash import dcc, html, Input, Output, State
import dash_cytoscape as cyto
import plotly.express as px
import pandas as pd

# Initialize app
app = dash.Dash(__name__)
app.title = "LLM-Enhanced PhD Research Workflow"

# Research domain data
domains = {
    'biology': {
        'metrics': {'time_saved': 65, 'cost_reduction': 40, 'accuracy_gain': 22},
        'example': "Gene-disease association mining from 50k+ papers"
    },
    'cs': {
        'metrics': {'time_saved': 72, 'cost_reduction': 55, 'accuracy_gain': 18},
        'example': "Code vulnerability detection in 1M+ LOC"
    },
    'social_science': {
        'metrics': {'time_saved': 58, 'cost_reduction': 35, 'accuracy_gain': 29},
        'example': "Sentiment analysis of 100k+ survey responses"
    }
}

# Enhanced workflow nodes
nodes = [
    {'data': {'id': 'proposal', 'label': 'Research Proposal\nDevelopment'}, 'classes': 'phase'},
    {'data': {'id': 'lit_review', 'label': 'Literature Review\n(LLM-Powered)'}, 'classes': 'llm'},
    {'data': {'id': 'hypothesis', 'label': 'Hypothesis\nGeneration'}, 'classes': 'phase'},
    {'data': {'id': 'experiment', 'label': 'Experiment Design'}, 'classes': 'phase'},
    {'data': {'id': 'data_collect', 'label': 'Data Collection &\nAnnotation'}, 'classes': 'llm'},
    {'data': {'id': 'analysis', 'label': 'Analysis &\nInterpretation'}, 'classes': 'llm'},
    {'data': {'id': 'writing', 'label': 'Paper Writing &\nPeer Review'}, 'classes': 'llm'}
]

edges = [
    {'data': {'source': 'proposal', 'target': 'lit_review'}},
    {'data': {'source': 'lit_review', 'target': 'hypothesis'}},
    {'data': {'source': 'hypothesis', 'target': 'experiment'}},
    {'data': {'source': 'experiment', 'target': 'data_collect'}},
    {'data': {'source': 'data_collect', 'target': 'analysis'}},
    {'data': {'source': 'analysis', 'target': 'writing'}}
]

app.layout = html.Div([
    html.Div([
        html.H1("LLM-Augmented PhD Research Workflow", className="header"),
        dcc.Dropdown(
            id='domain-selector',
            options=[
                {'label': 'Biology', 'value': 'biology'},
                {'label': 'Computer Science', 'value': 'cs'},
                {'label': 'Social Science', 'value': 'social_science'}
            ],
            value='biology',
            clearable=False
        )
    ], className='control-panel'),
    
    cyto.Cytoscape(
        id='research-workflow',
        layout={'name': 'dagre', 'nodeSep': 100},
        style={'width': '100%', 'height': '600px'},
        elements=nodes + edges,
        stylesheet=[
            {
                'selector': 'node',
                'style': {
                    'label': 'data(label)',
                    'background-color': '#2C3E50',
                    'text-valign': 'center',
                    'color': 'white',
                    'font-size': '14px',
                    'width': 120,
                    'height': 120,
                    'shape': 'ellipse'
                }
            },
            {
                'selector': '.llm',
                'style': {
                    'background-color': '#3498DB',
                    'border-width': 2,
                    'border-color': '#2980B9'
                }
            },
            {
                'selector': 'edge',
                'style': {
                    'width': 3,
                    'line-color': '#7F8C8D',
                    'target-arrow-color': '#7F8C8D',
                    'target-arrow-shape': 'triangle',
                    'curve-style': 'bezier'
                }
            }
        ]
    ),
    
    html.Div([
        html.Div(id='node-details', className='detail-card'),
        dcc.Graph(id='metrics-chart')
    ], className='dashboard-row'),
    
    dcc.Store(id='current-domain', data='biology')
])

@app.callback(
    [Output('node-details', 'children'),
     Output('metrics-chart', 'figure'),
     Output('current-domain', 'data')],
    [Input('research-workflow', 'tapNodeData'),
     Input('domain-selector', 'value')],
    [State('current-domain', 'data')]
)
def update_display(node_data, selected_domain, current_domain):
    ctx = dash.callback_context
    domain_data = domains[selected_domain]
    
    # Update metrics chart
    df = pd.DataFrame({
        'Metric': ['Time Saved (%)', 'Cost Reduction (%)', 'Accuracy Gain (%)'],
        'Value': [domain_data['metrics']['time_saved'], 
                 domain_data['metrics']['cost_reduction'],
                 domain_data['metrics']['accuracy_gain']]
    })
    fig = px.bar(df, x='Value', y='Metric', orientation='h',
                 title=f"Performance Metrics: {selected_domain.replace('_', ' ').title()}",
                 color='Metric', 
                 color_discrete_map={
                     'Time Saved (%)': '#3498DB',
                     'Cost Reduction (%)': '#2ECC71',
                     'Accuracy Gain (%)': '#9B59B6'
                 })
    
    # Node details
    if ctx.triggered[0]['prop_id'] == 'domain-selector.value':
        return [html.Div([
                    html.H3("Domain Overview"),
                    html.P(domain_data['example']),
                    html.Ul([
                        html.Li(f"Time saved: {domain_data['metrics']['time_saved']}%"),
                        html.Li(f"Cost reduction: {domain_data['metrics']['cost_reduction']}%"),
                        html.Li(f"Accuracy improvement: {domain_data['metrics']['accuracy_gain']}%")
                    ])
                ]), fig, selected_domain]
    
    if node_data:
        node_id = node_data['id']
        content = []
        if node_id == 'lit_review':
            content = [
                html.H3("LLM-Powered Literature Review"),
                html.P("Key capabilities:"),
                html.Ul([
                    html.Li("Automated paper categorization (TF-IDF + BERT embeddings)"),
                    html.Li("Cross-lingual analysis support"),
                    html.Li("Citation graph generation"),
                    html.Li("Research gap identification")
                ]),
                html.Br(),
                html.P(f"Example in {selected_domain.replace('_', ' ').title()}:"),
                html.P(domain_data['example'])
            ]
        elif node_id == 'data_collect':
            content = [
                html.H3("Smart Data Collection"),
                html.Ul([
                    html.Li("Automated survey response coding"),
                    html.Li("Image/text annotation with active learning"),
                    html.Li("Bias detection in training data"),
                    html.Li("Data anonymization workflows")
                ])
            ]
        return [html.Div(content), fig, current_domain]
    
    return [html.Div("Select a research phase or domain"), fig, current_domain]

if __name__ == '__main__':
    app.run_server(debug=True, port=8051)
dcc.Checklist(
    id='ethics-checklist',
    options=[
        {'label': 'Bias Audit Completed', 'value': 'bias'},
        {'label': 'Data Anonymized', 'value': 'anon'}
    ],
    value=[]
)
# Add Gantt chart component
dcc.Graph(figure=px.timeline(
    df, x_start="Start", x_end="End", y="Task",
    title="LLM-Accelerated Publication Timeline"
))