/**
 * Chart Suggestion Tool for Notes
 * AI analyzes note content and suggests appropriate chart types with data
 */

import { tool } from 'ai'
import { z } from 'zod'
import { toolRegistry } from './registry'

// Chart suggestion data structures
export interface ChartSuggestion {
  type: 'bar' | 'horizontal-bar' | 'line' | 'area' | 'pie' | 'radar' | 'funnel' | 'gauge'
  title: string
  data: { label: string; value: number }[]
  description: string
  rationale?: string
}

export interface SuggestChartsResponse {
  suggestions: ChartSuggestion[]
  analyzedText: string
  detectedData: string
  hasData: boolean
}

toolRegistry.register({
  name: 'suggest_charts',
  description: 'Analyze content and suggest appropriate chart types with extracted data',
  isInternal: true,
  buildTool: (ctx) =>
    tool({
      description: `Analyze the provided text content and suggest 3 appropriate chart types with extracted data.

Available chart types:
- "bar": Vertical bar chart (best for comparing values across categories)
- "horizontal-bar": Horizontal bar chart (best for long category labels)
- "line": Line chart (best for trends over time or sequences)
- "area": Area chart (filled line chart, best for showing magnitude over time)
- "pie": Pie chart (best for showing proportions/percentages of a whole)
- "radar": Radar chart (best for comparing multiple dimensions)
- "funnel": Funnel chart (best for showing stages in a process)
- "gauge": Gauge chart (best for single values vs a target)

CRITICAL RULES:
1. Extract ONLY numerical data present in the text - do NOT invent or fabricate values
2. If fewer than 2 data points exist, return empty suggestions array with hasData=false
3. Each suggestion MUST use the SAME extracted data - only the chart type differs
4. Provide a clear rationale explaining WHY each chart type suits the data
5. Generate meaningful labels - if the text provides context (months, categories, names), use those; otherwise use generic labels like "Item 1", "Item 2", etc.

Data extraction examples:
- "Sales: Jan $5000, Feb $7500, Mar $6200" → [{label:"Jan",value:5000}, {label:"Feb",value:7500}, {label:"Mar",value:6200}]
- "Product A: 45%, Product B: 30%, Product C: 25%" → [{label:"Product A",value:45}, {label:"Product B",value:30}, {label:"Product C",value:25}]
- "Progress: Q1=10, Q2=25, Q3=40, Q4=60" → [{label:"Q1",value:10}, {label:"Q2",value:25}, {label:"Q3",value:40}, {label:"Q4",value:60}]

Output format:
Return exactly 3 chart suggestions with different types. Order by relevance (most suitable first).

Example response for sales data:
{
  "suggestions": [
    {
      "type": "bar",
      "title": "Sales by Month",
      "data": [{"label":"Jan","value":5000},{"label":"Feb","value":7500},{"label":"Mar","value":6200}],
      "description": "Bar chart comparing sales across months",
      "rationale": "Best for direct comparison of values between categories"
    },
    {
      "type": "line",
      "title": "Sales Trend",
      "data": [{"label":"Jan","value":5000},{"label":"Feb","value":7500},{"label":"Mar","value":6200}],
      "description": "Line chart showing sales progression over time",
      "rationale": "Ideal for visualizing trends and changes over time periods"
    },
    {
      "type": "area",
      "title": "Sales Volume",
      "data": [{"label":"Jan","value":5000},{"label":"Feb","value":7500},{"label":"Mar","value":6200}],
      "description": "Area chart emphasizing sales magnitude",
      "rationale": "Similar to line but emphasizes volume/proportion visually"
    }
  ],
  "analyzedText": "Sales: Jan $5000, Feb $7500, Mar $6200",
  "detectedData": "3 data points: sales figures for Jan, Feb, Mar",
  "hasData": true
}`,
      inputSchema: z.object({
        content: z.string().describe('The full note content to analyze for chart data'),
        selection: z.string().optional().describe('Optional selected text - if provided, analyze only this instead of full content'),
      }),
      execute: async ({ content, selection }) => {
        const textToAnalyze = selection && selection.trim() ? selection.trim() : content.trim()

        // This will be processed by the AI model
        // The AI will extract data and generate suggestions
        return {
          textToAnalyze,
          // The actual suggestion generation happens in the AI response
          // This tool provides the context for the AI to work with
        }
      },
    }),
})