app.py

import base64
import io
import ast
import traceback
from threading import Thread

import dash
from dash import dcc, html, Input, Output, State
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.graph_objs as go
import google.generativeai as genai

# Initialize Dash app
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

# Layout
app.layout = dbc.Container([
    html.H1("Data Analysis Dashboard", className="my-4"),
    dbc.Card([
        dbc.CardBody([
            dbc.Input(id="api-key", placeholder="Enter your Gemini API key", type="password", className="mb-3"),
            dcc.Upload(
                id='upload-data',
                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'
                },
                multiple=False
            ),
            dbc.Input(id="instructions", placeholder="Describe the analysis you want...", type="text"),
            dbc.Button("Generate Insights", id="submit-button", color="primary", className="mt-3"),
        ])
    ], className="mb-4"),
    dbc.Card([
        dbc.CardBody([
            dcc.Graph(id='visualization-1'),
            dcc.Graph(id='visualization-2'),
            dcc.Graph(id='visualization-3'),
        ])
    ])
], fluid=True)

def parse_contents(contents, filename):
    content_type, content_string = contents.split(',')
    decoded = base64.b64decode(content_string)
    try:
        if 'csv' in filename:
            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
        elif 'xls' in filename:
            df = pd.read_excel(io.BytesIO(decoded))
        else:
            return None
        return df
    except Exception as e:
        print(e)
        return None

def process_data(df, instructions, api_key):
    try:
        # Initialize Gemini with provided API key
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
        
        # Generate visualization code
        response = model.generate_content(f"""
            Analyze the following dataset and instructions:
            
            Data columns: {list(df.columns)}
            Data shape: {df.shape}
            Instructions: {instructions}
            
            Based on this, create 3 appropriate visualizations that provide meaningful insights. For each visualization:
            1. Choose the most suitable plot type (bar, line, scatter, hist, pie, heatmap)
            2. Determine appropriate data aggregation (e.g., top 5 categories, yearly averages)
            3. Select relevant columns for x-axis, y-axis, and any additional dimensions (color, size)
            4. Provide a clear, concise title that explains the insight
            Consider data density and choose visualizations that simplify and clarify the information.
            Limit the number of data points displayed to ensure readability (e.g., top 5, top 10, yearly).
            
            Return your response as a Python list of dictionaries:
            [
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}},
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}},
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "agg_func": "...", "top_n": ..., "additional": {{"color": "...", "size": "..."}}}}
            ]
        """)

        # Extract code block safely
        code_block = response.text
        if '```python' in code_block:
            code_block = code_block.split('```python')[1].split('```')[0].strip()
        elif '```' in code_block:
            code_block = code_block.split('```')[1].strip()
        
        plots = ast.literal_eval(code_block)
        return plots
    except Exception as e:
        print(f"Error in process_data: {str(e)}")
        return None

def generate_plot(df, plot_info):
    plot_df = df.copy()
    if plot_info['agg_func'] == 'sum':
        plot_df = plot_df.groupby(plot_info['x'])[plot_info['y']].sum().reset_index()
    elif plot_info['agg_func'] == 'mean':
        plot_df = plot_df.groupby(plot_info['x'])[plot_info['y']].mean().reset_index()
    elif plot_info['agg_func'] == 'count':
        plot_df = plot_df.groupby(plot_info['x']).size().reset_index(name=plot_info['y'])
    
    if 'top_n' in plot_info and plot_info['top_n']:
        plot_df = plot_df.nlargest(plot_info['top_n'], plot_info['y'])
    
    if plot_info['plot_type'] == 'bar':
        fig = go.Figure(go.Bar(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']]))
    elif plot_info['plot_type'] == 'line':
        fig = go.Figure(go.Scatter(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']], mode='lines'))
    elif plot_info['plot_type'] == 'scatter':
        fig = go.Figure(go.Scatter(x=plot_df[plot_info['x']], y=plot_df[plot_info['y']], mode='markers'))
    elif plot_info['plot_type'] == 'hist':
        fig = go.Figure(go.Histogram(x=plot_df[plot_info['x']]))
    elif plot_info['plot_type'] == 'pie':
        fig = go.Figure(go.Pie(labels=plot_df[plot_info['x']], values=plot_df[plot_info['y']]))
    elif plot_info['plot_type'] == 'heatmap':
        pivot_df = plot_df.pivot(index=plot_info['x'], columns=plot_info['additional']['color'], values=plot_info['y'])
        fig = go.Figure(go.Heatmap(z=pivot_df.values, x=pivot_df.columns, y=pivot_df.index))
    
    fig.update_layout(title=plot_info['title'], xaxis_title=plot_info['x'], yaxis_title=plot_info['y'])
    return fig

@app.callback(
    [Output('visualization-1', 'figure'),
     Output('visualization-2', 'figure'),
     Output('visualization-3', 'figure')],
    [Input('submit-button', 'n_clicks')],
    [State('upload-data', 'contents'),
     State('upload-data', 'filename'),
     State('instructions', 'value'),
     State('api-key', 'value')]
)
def update_output(n_clicks, contents, filename, instructions, api_key):
    if n_clicks is None or contents is None or not api_key:
        return dash.no_update, dash.no_update, dash.no_update

    df = parse_contents(contents, filename)
    if df is None:
        return dash.no_update, dash.no_update, dash.no_update

    plots = process_data(df, instructions, api_key)
    if plots is None or len(plots) < 3:
        return dash.no_update, dash.no_update, dash.no_update

    figures = [generate_plot(df, plot_info) for plot_info in plots[:3]]
    return figures

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=7860, threaded=True)