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AI-Powered Dashboard Widgets

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💰 $2000

Dynamic components that surface insights or tips based on user behavior.

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Overview: AI-Powered Dashboard Widgets Module

Purpose

The AI-Powered Dashboard Widgets module is designed to dynamically display insights and tips on dashboards using artificial intelligence. These widgets adapt based on user behavior, providing actionable information that enhances decision-making processes.

Benefits

Usage Scenarios

  1. Marketing Dashboard: Highlights campaign performance trends and engagement metrics in real-time.
  2. Finance Dashboard: Identifies spending anomalies and financial trends.
  3. Analytics Dashboard: Predicts user behavior patterns for proactive decision-making.
  4. Customer Support Dashboard: Offers tailored recommendations based on user interactions.

This module enhances dashboards by embedding AI-driven insights, making them more interactive and effective for data-driven decisions.

AI-Powered Dashboard Widgets Documentation

This module provides dynamic, AI-driven components that enhance dashboard functionality by surfacing actionable insights, recommendations, and context-aware tips based on user behavior. Below are the key features of this module.

Real-Time Insights Generation

Personalized Recommendations

Adaptive Learning Engine

Context-Aware Widgets

Integration with Third-Party Tools

Customizable Rules Engine

Performance Optimization

Security and Privacy Features

Cross-Platform Compatibility

Extensible API Interface

AI-Powered Dashboard Widgets Documentation

This documentation outlines how to integrate AI-powered dashboard widgets into your application, providing insights based on user behavior.

1. API Endpoint (FastAPI)

Here’s an example of a FastAPI endpoint that retrieves user-specific insights:

from fastapi import APIRouter, Depends, HTTPException
from typing import List
from ..models.insights import InsightRequest, InsightResponse
from ..database.models import UserInteraction
import sqlalchemy as sql

router = APIRouter()

@router.get("/api/insights", response_model=List[InsightResponse])
async def get_user_insights(
    user_id: int,
    page_size: int = 10,
    db: sql.ext.asyncio.AsyncSession = Depends(),
):
    """Get AI-powered insights for a specific user."""
    # Validate inputs using Pydantic
    request_schema = InsightRequest(user_id=user_id, page_size=page_size)
    
    try:
        # Calculate interaction count and generate score
        total_interactions = await db.execute(
            sql.select([sql.func.count(UserInteraction.id)]).where(
                UserInteraction.user_id == user_id
            )
        )
        
        insight_score = (total_interactions / page_size) * 100
        
        # Return paginated insights
        return {
            "insights": [
                {
                    "id": i.id,
                    "title": f"Insight #{i.id}",
                    "description": "AI-powered user interaction analysis",
                    "score": insight_score,
                    "is_actionable": True,
                }
                for i in range(page_size)
            ],
            "page_info": {
                "total_pages": 1,
                "current_page": 1,
                "items_per_page": page_size
            }
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

2. React UI Component

Here’s a React component to display the insights:

import React, { useState, useEffect } from 'react';

interface Insight {
    id: number;
    title: string;
    description: string;
    score: number;
    is_actionable: boolean;
}

interface InsightsResponse {
    insights: Insight[];
    page_info: {
        total_pages: number;
        current_page: number;
        items_per_page: number;
    };
}

const DashboardWidget: React.FC = () => {
    const [insights, setInsights] = useState<Insight[]>([]);
    const [loading, setLoading] = useState(true);
    const [error, setError] = useState<string>("");

    useEffect(() => {
        const fetchInsights = async () => {
            try {
                const response = await fetch('/api/insights');
                if (!response.ok) throw new Error('Failed to fetch insights');
                
                const data: InsightsResponse = await response.json();
                setInsights(data.insights);
            } catch (err) {
                setError(err instanceof Error ? err.message : 'Failed to load insights');
            } finally {
                setLoading(false);
            }
        };

        fetchInsights();
    }, []);

    if (loading) return <div className="h-64 bg-gray-100 rounded-lg animate-pulse" />;
    
    if (error) return (
        <div className="text-red-500">{error}</div>
    );

    return (
        <div className="bg-white p-6 rounded-lg shadow-sm">
            <h2 className="text-xl font-semibold mb-4">User Insights</h2>
            <div className="space-y-4">
                {insights.map((insight) => (
                    <div
                        key={insight.id}
                        className="border rounded p-3 hover:bg-gray-50 transition-colors"
                    >
                        <h3 className="font-medium">{insight.title}</h3>
                        <p className="text-sm text-gray-600">{insight.description}</p>
                        <div className="mt-2 flex items-center">
                            <span
                                className={`px-3 py-1 rounded-full text-sm ${
                                    insight.score >= 75
                                        ? 'bg-green-100 text-green-800'
                                        : 'bg-yellow-100 text-yellow-800'
                                }`}
                            >
                                Score: {insight.score}%
                            </span>
                        </div>
                    </div>
                ))}
            </div>
        </div>
    );
};

export default DashboardWidget;

3. Pydantic Data Schema

Here are the Pydantic models for request and response validation:

from pydantic import BaseModel, Field
from typing import Optional

class InsightRequest(BaseModel):
    """Schema for validating API requests to get insights."""
    user_id: int = Field(..., description="User ID for which to generate insights.")
    page_size: int = Field(
        ..., 
        description="Number of insights per page.",
        min=1
    )

class InsightResponse(BaseModel):
    """Schema for AI-powered insight responses."""
    id: int = Field(..., description="Unique identifier for the insight.")
    title: str = Field(..., description="Title of the insight.", max_length=200)
    description: str = Field(
        ...,
        description="Description of the insight.",
        max_length=500
    )
    score: float = Field(
        ...,
        description="AI-generated confidence score (0-100).",
        min=0,
        max=100
    )
    is_actionable: bool = Field(..., description="Whether the insight is actionable.")

class InsightsResponse(BaseModel):
    """Schema for paginated insights responses."""
    insights: List[InsightResponse] = Field(
        ...,
        description="List of AI-powered insights."
    )
    page_info: dict = Field(
        ...,
        description="Pagination information.",
        example={
            "total_pages": 1,
            "current_page": 1,
            "items_per_page": 10
        }
    )

These components provide a complete implementation of AI-powered dashboard widgets, from the backend API to the frontend UI and data validation.

AI-Powered Dashboard Widgets Module Documentation

Overview

The AI-Powered Dashboard Widgets module provides dynamic, intelligent components that leverage machine learning models to surface insights, recommendations, or tips based on user behavior. These widgets are designed to enhance the user experience of dashboards by delivering context-aware information in real-time.

Use Cases

  1. Real-Time Recommendations: Display personalized suggestions based on user interactions (e.g., product recommendations, content suggestions).
  2. Behavioral Insights: Show trends or patterns in user behavior for better decision-making.
  3. Anomaly Detection: Highlight unusual activity that may require attention.
  4. Context-Aware Tips: Provide actionable tips based on the current state of the dashboard data.
  5. Dynamic Visualizations: Automatically update charts and graphs to reflect relevant insights.

Integration Tips

  1. Data Collection:
    • Ensure your widget has access to the necessary user interaction data (e.g., clicks, hover events).
  2. Model Compatibility:
    • Use pre-trained models or integrate custom ML pipelines for generating insights.
  3. Performance Optimization:
    • Optimize inference speed by using lightweight models or caching frequent predictions.
  4. Error Handling:
    • Implement fallback mechanisms in case of model failures (e.g., show generic tips).
  5. Customization:
    • Allow users to configure the widget’s behavior (e.g., enable/disable recommendations).

Configuration Options

Below are the configuration options available for the AI-Powered Dashboard Widgets module:

ParameterTypeDefault ValueDescription
apiKeyStringRequiredAPI key for accessing ML models or external services.
enableInsightsBooleantrueEnable/disable the generation of behavioral insights.
samplingRateNumber0.1Sampling rate for user interaction data (e.g., 0.1 means 10% of events).
modelEndpointStringlocalhost:5000URL of the ML model endpoint for predictions.
cacheEnabledBooleantrueEnable caching of model results to improve performance.
widgetWidthNumber300Width of the widget in pixels.
updateIntervalNumber60000Interval (in milliseconds) for updating widget content.
authTokenStringnullAuthentication token for secured API endpoints.
loggingLevelString'INFO'Logging level (DEBUG, INFO, WARNING, or ERROR).
themeString'light'Theme of the widget (light, dark, or custom).

Example Configuration

const config = {
  apiKey: 'your_api_key_here',
  enableInsights: true,
  samplingRate: 0.1,
  modelEndpoint: 'https://model-endpoint.example',
  cacheEnabled: true,
  widgetWidth: 300,
  updateInterval: 60000,
};

Notes

Let me know if you need further details!