Create app.py

app.py

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+import dash
+from dash import dcc, html, Input, Output, State, callback
+import dash_bootstrap_components as dbc
+import base64
+import io
+import pandas as pd
+import openai
+import os
+import time
+from dash.exceptions import PreventUpdate
+
+# Initialize the Dash app
+app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+
+# Get OpenAI API key from Hugging Face Spaces environment variable
+openai.api_key = os.environ.get('OPENAI_API_KEY')
+
+# Global variables
+uploaded_files = []
+current_matrix = None
+matrix_type = None
+
+# Matrix types and their descriptions
+matrix_types = {
+    "Communications Plan Matrix": "Create a matrix showing stakeholders, communication methods, frequency, and responsibilities.",
+    "Project Kick-off Matrix": "Generate a matrix outlining key project details, goals, team roles, and initial timelines.",
+    "Decision Matrix": "Develop a matrix for evaluating options against criteria, with weighted scores.",
+    "Lessons Learned Matrix": "Create a matrix capturing project experiences, challenges, solutions, and recommendations.",
+    "Key Performance Indicator Matrix": "Generate a matrix of KPIs, their targets, actual performance, and status.",
+    "Prioritization Matrix": "Develop a matrix for ranking tasks or features based on importance and urgency.",
+    "Risk Matrix": "Create a matrix assessing potential risks, their likelihood, impact, and mitigation strategies.",
+    "RACI Matrix": "Generate a matrix showing team members and their roles (Responsible, Accountable, Consulted, Informed) for each task.",
+    "Project Schedule Matrix": "Develop a matrix showing project phases, tasks, durations, and dependencies.",
+    "Quality Control Matrix": "Create a matrix outlining quality standards, testing methods, and acceptance criteria.",
+    "Requirements Traceability Matrix": "Generate a matrix linking requirements to their sources, test cases, and status.",
+    "Sprint Planning Matrix": "Develop a matrix for sprint tasks, story points, assignees, and status.",
+    "Test Traceability Matrix": "Create a matrix linking test cases to requirements, execution status, and results.",
+    "Sprint Backlog": "Generate a matrix of user stories, tasks, estimates, and priorities for the sprint.",
+    "Sprint Retrospective": "Develop a matrix capturing what went well, what didn't, and action items from the sprint.",
+    "SWOT Matrix": "Create a matrix analyzing Strengths, Weaknesses, Opportunities, and Threats."
+}
+
+# Layout
+app.layout = dbc.Container([
+    dbc.Row([
+        dbc.Col([
+            html.H4("Project Artifacts", className="mt-3 mb-4"),
+            dcc.Upload(
+                id='upload-files',
+                children=html.Div([
+                    'Drag and Drop or ',
+                    html.A('Select Files')
+                ]),
+                style={
+                    'width': '100%',
+                    'height': '60px',
+                    'lineHeight': '60px',
+                    'borderWidth': '1px',
+                    'borderStyle': 'dashed',
+                    'borderRadius': '5px',
+                    'textAlign': 'center',
+                    'margin': '10px 0'
+                },
+                multiple=True
+            ),
+            html.Div(id='file-list'),
+            html.Hr(),
+            html.Div([
+                dbc.Button(
+                    matrix_type,
+                    id=f'btn-{matrix_type.lower().replace(" ", "-")}',
+                    color="primary",
+                    className="mb-2 w-100",
+                    style={'overflow': 'hidden', 'text-overflow': 'ellipsis', 'white-space': 'nowrap'}
+                ) for matrix_type in matrix_types.keys()
+            ])
+        ], width=3),
+        dbc.Col([
+            html.Div(id='matrix-preview', className="border p-3 mb-3"),
+            dcc.Loading(
+                id="loading-indicator",
+                type="dot",
+                children=[html.Div(id="loading-output")]
+            ),
+            dbc.Button("Download Matrix", id="btn-download", color="success", className="mt-3"),
+            dcc.Download(id="download-matrix"),
+            html.Hr(),
+            dbc.Input(id="chat-input", type="text", placeholder="Chat with GPT to update matrix...", className="mb-2"),
+            dbc.Button("Send", id="btn-send-chat", color="primary", className="mb-3"),
+            html.Div(id="chat-output")
+        ], width=9)
+    ])
+], fluid=True)
+
+@app.callback(
+    Output('file-list', 'children'),
+    Input('upload-files', 'contents'),
+    State('upload-files', 'filename'),
+    State('file-list', 'children')
+)
+def update_output(list_of_contents, list_of_names, existing_files):
+    if list_of_contents is not None:
+        new_files = [
+            html.Div([
+                html.Button('×', id={'type': 'remove-file', 'index': i}, style={'marginRight': '5px', 'fontSize': '10px'}),
+                html.Span(filename)
+            ]) for i, filename in enumerate(list_of_names)
+        ]
+        if existing_files is None:
+            existing_files = []
+        return existing_files + new_files
+    return existing_files
+
+@app.callback(
+    Output('matrix-preview', 'children'),
+    Output('loading-output', 'children'),
+    [Input(f'btn-{matrix_type.lower().replace(" ", "-")}', 'n_clicks') for matrix_type in matrix_types.keys()],
+    prevent_initial_call=True
+)
+def generate_matrix(*args):
+    global current_matrix, matrix_type
+    ctx = dash.callback_context
+    if not ctx.triggered:
+        raise PreventUpdate
+    button_id = ctx.triggered[0]['prop_id'].split('.')[0]
+    matrix_type = button_id.replace('btn-', '').replace('-', ' ').title()
+    
+    # Simulate matrix generation (replace with actual OpenAI call)
+    time.sleep(2)  # Simulating API call delay
+    current_matrix = pd.DataFrame({
+        'Column 1': ['Row 1', 'Row 2', 'Row 3'],
+        'Column 2': ['Data 1', 'Data 2', 'Data 3'],
+        'Column 3': ['Info 1', 'Info 2', 'Info 3']
+    })
+    
+    return dbc.Table.from_dataframe(current_matrix, striped=True, bordered=True, hover=True), f"{matrix_type} generated"
+
+@app.callback(
+    Output('chat-output', 'children'),
+    Output('matrix-preview', 'children', allow_duplicate=True),
+    Input('btn-send-chat', 'n_clicks'),
+    State('chat-input', 'value'),
+    prevent_initial_call=True
+)
+def update_matrix_via_chat(n_clicks, chat_input):
+    global current_matrix
+    if not chat_input:
+        raise PreventUpdate
+    
+    # Simulate chat interaction and matrix update (replace with actual OpenAI call)
+    time.sleep(1)  # Simulating API call delay
+    chat_response = f"Updated the matrix based on: {chat_input}"
+    current_matrix = current_matrix.append({'Column 1': 'New Row', 'Column 2': 'New Data', 'Column 3': 'New Info'}, ignore_index=True)
+    
+    return chat_response, dbc.Table.from_dataframe(current_matrix, striped=True, bordered=True, hover=True)
+
+@app.callback(
+    Output("download-matrix", "data"),
+    Input("btn-download", "n_clicks"),
+    prevent_initial_call=True
+)
+def download_matrix(n_clicks):
+    global current_matrix, matrix_type
+    if current_matrix is None:
+        raise PreventUpdate
+    
+    # Create an in-memory Excel file
+    output = io.BytesIO()
+    with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+        current_matrix.to_excel(writer, sheet_name='Sheet1', index=False)
+    
+    return dcc.send_bytes(output.getvalue(), f"{matrix_type}.xlsx")
+
+if __name__ == '__main__':
+    print("Starting the Dash application...")
+    app.run(debug=True, host='0.0.0.0', port=7860)
+    print("Dash application has finished running.")