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import base64 |
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import io |
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import ast |
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import traceback |
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from threading import Thread |
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import dash |
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from dash import dcc, html, Input, Output, State |
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import dash_bootstrap_components as dbc |
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import pandas as pd |
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import plotly.graph_objs as go |
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import google.generativeai as genai |
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP]) |
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app.layout = dbc.Container([ |
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html.H1("Data Analysis Dashboard", className="my-4"), |
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dbc.Card([ |
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dbc.CardBody([ |
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dbc.Input(id="api-key", placeholder="Enter your Gemini API key", type="password", className="mb-3"), |
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dcc.Upload( |
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id='upload-data', |
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children=html.Div([ |
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'Drag and Drop or ', |
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html.A('Select Files') |
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]), |
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style={ |
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'width': '100%', |
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'height': '60px', |
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'lineHeight': '60px', |
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'borderWidth': '1px', |
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'borderStyle': 'dashed', |
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'borderRadius': '5px', |
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'textAlign': 'center', |
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'margin': '10px' |
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}, |
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multiple=False |
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), |
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dbc.Input(id="instructions", placeholder="Describe the analysis you want...", type="text"), |
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dbc.Button("Generate Insights", id="submit-button", color="primary", className="mt-3"), |
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]) |
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], className="mb-4"), |
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html.Div(id="error-message", className="text-danger mb-3"), |
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dcc.Loading( |
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id="loading-visualizations", |
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type="default", |
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children=[ |
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dbc.Card([ |
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dbc.CardBody([ |
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dcc.Graph(id='visualization-1'), |
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dcc.Graph(id='visualization-2'), |
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dcc.Graph(id='visualization-3'), |
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]) |
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]) |
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] |
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) |
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], fluid=True) |
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def parse_contents(contents, filename): |
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content_type, content_string = contents.split(',') |
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decoded = base64.b64decode(content_string) |
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try: |
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if 'csv' in filename: |
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df = pd.read_csv(io.StringIO(decoded.decode('utf-8'))) |
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elif 'xls' in filename: |
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df = pd.read_excel(io.BytesIO(decoded)) |
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else: |
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return None |
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return df |
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except Exception as e: |
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print(e) |
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return None |
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def process_data(df, instructions, api_key): |
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try: |
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genai.configure(api_key=api_key) |
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model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25') |
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response = model.generate_content(f""" |
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Analyze the following dataset and instructions: |
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Data columns: {list(df.columns)} |
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Data shape: {df.shape} |
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Instructions: {instructions} |
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Based on this, create 3 appropriate visualizations that provide meaningful insights. For each visualization: |
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1. Choose the most suitable plot type (bar, line, scatter, hist, pie, heatmap) |
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2. Determine appropriate data aggregation (e.g., top 5 categories, yearly averages) |
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3. Select relevant columns for x-axis, y-axis, and any additional dimensions (color, size) |
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4. Provide a clear, concise title that explains the insight |
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Consider data density and choose visualizations that simplify and clarify the information. |
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Limit the number of data points displayed to ensure readability (e.g., top 5, top 10, yearly). |
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Return your response as a Python list of dictionaries: |
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[ |
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{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}}, |
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{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}}, |
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{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}} |
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] |
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""") |
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code_block = response.text |
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if '```python' in code_block: |
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code_block = code_block.split('```python')[1].split('```')[0].strip() |
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elif '```' in code_block: |
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code_block = code_block.split('```')[1].strip() |
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plots = ast.literal_eval(code_block) |
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return plots |
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except Exception as e: |
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print(f"Error in process_data: {str(e)}") |
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return None |
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def generate_plot(df, plot_info): |
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plot_df = df.copy() |
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if plot_info['agg_func'] == 'sum': |
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plot_df = plot_df.groupby(plot_info['x'])[plot_info['y']].sum().reset_index() |
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elif plot_info['agg_func'] == 'mean': |
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plot_df = plot_df.groupby(plot_info['x'])[plot_info['y']].mean().reset_index() |
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elif plot_info['agg_func'] == 'count': |
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plot_df = plot_df.groupby(plot_info['x']).size().reset_index(name=plot_info['y']) |
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if 'top_n' in plot_info and plot_info['top_n']: |
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plot_df = plot_df.nlargest(plot_info['top_n'], plot_info['y']) |
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if plot_info['plot_type'] == 'bar': |
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fig = go.Figure(go.Bar(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']])) |
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elif plot_info['plot_type'] == 'line': |
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fig = go.Figure(go.Scatter(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']], mode='lines')) |
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elif plot_info['plot_type'] == 'scatter': |
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fig = go.Figure(go.Scatter(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']], mode='markers')) |
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elif plot_info['plot_type'] == 'hist': |
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fig = go.Figure(go.Histogram(x=plot_df[plot_info['x']])) |
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elif plot_info['plot_type'] == 'pie': |
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fig = go.Figure(go.Pie(labels=plot_df[plot_info['x']], values=plot_df[plot_info['y']])) |
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elif plot_info['plot_type'] == 'heatmap': |
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pivot_df = plot_df.pivot(index=plot_info['x'], columns=plot_info['additional']['color'], values=plot_info['y']) |
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fig = go.Figure(go.Heatmap(z=pivot_df.values, x=pivot_df.columns, y=pivot_df.index)) |
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fig.update_layout(title=plot_info['title'], xaxis_title=plot_info['x'], yaxis_title=plot_info['y']) |
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return fig |
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@app.callback( |
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[Output('visualization-1', 'figure'), |
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Output('visualization-2', 'figure'), |
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Output('visualization-3', 'figure'), |
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Output('error-message', 'children')], |
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[Input('submit-button', 'n_clicks')], |
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[State('upload-data', 'contents'), |
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State('upload-data', 'filename'), |
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State('instructions', 'value'), |
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State('api-key', 'value')] |
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) |
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def update_output(n_clicks, contents, filename, instructions, api_key): |
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if n_clicks is None or contents is None: |
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return dash.no_update, dash.no_update, dash.no_update, "" |
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if not api_key: |
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return dash.no_update, dash.no_update, dash.no_update, "Please enter a valid API key." |
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try: |
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df = parse_contents(contents, filename) |
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if df is None: |
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return dash.no_update, dash.no_update, dash.no_update, "Unable to parse the uploaded file." |
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plots = process_data(df, instructions, api_key) |
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if plots is None or len(plots) < 3: |
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return dash.no_update, dash.no_update, dash.no_update, "Unable to generate visualizations. Please check your instructions and try again." |
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figures = [generate_plot(df, plot_info) for plot_info in plots[:3]] |
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return figures[0], figures[1], figures[2], "" |
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except Exception as e: |
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error_message = f"An error occurred: {str(e)}" |
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return dash.no_update, dash.no_update, dash.no_update, error_message |
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if __name__ == '__main__': |
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app.run(debug=True, host='0.0.0.0', port=7860, threaded=True) |
