123 lines
3.6 KiB
Go
123 lines
3.6 KiB
Go
package ai
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import (
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"caltrack/db"
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"caltrack/types"
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"context"
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"encoding/json"
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"fmt"
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"strings"
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"github.com/anthropics/anthropic-sdk-go"
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"github.com/anthropics/anthropic-sdk-go/option"
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)
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type Client struct {
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cfg *types.Config
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db *db.DB
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}
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func New(cfg *types.Config, database *db.DB) *Client {
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return &Client{
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cfg: cfg,
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db: database,
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}
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}
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const systemPrompt = `You are a nutrition and exercise expert. Your job is to estimate calories for food items and exercises.
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When given a food description, respond with a JSON object containing an "items" array. Each item should have:
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- "name": the food item name (e.g., "Banana")
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- "brand": brand name if applicable, empty string otherwise
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- "kcal": estimated calories (integer)
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- "serving_size": the amount (e.g., 1, 200)
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- "serving_unit": unit (e.g., "piece", "g", "ml")
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When given an exercise description, respond with a JSON object containing an "items" array with a single item:
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- "name": the exercise name
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- "brand": empty string
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- "kcal": estimated calories burned (integer, positive number)
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- "serving_size": duration or distance
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- "serving_unit": "min" or "km" etc.
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For branded products (like "Snickers bar", "Coca Cola 330ml", "Lidl protein pudding"), use real nutritional data.
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IMPORTANT: Respond ONLY with valid JSON. No markdown, no explanation, just the JSON object.
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Examples:
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Input: "ate a banana and a protein shake"
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Output: {"items":[{"name":"Banana","brand":"","kcal":105,"serving_size":1,"serving_unit":"piece"},{"name":"Protein shake","brand":"","kcal":250,"serving_size":1,"serving_unit":"serving"}]}
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Input: "ran 5k in 25 minutes"
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Output: {"items":[{"name":"Running 5K","brand":"","kcal":350,"serving_size":5,"serving_unit":"km"}]}
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Input: "Snickers bar"
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Output: {"items":[{"name":"Snickers Bar","brand":"Mars","kcal":250,"serving_size":52,"serving_unit":"g"}]}`
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func (c *Client) EstimateCalories(ctx context.Context, description string, entryType types.EntryType) (*types.AIEstimation, error) {
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if c.cfg.AnthropicAPIKey == "" {
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return nil, fmt.Errorf("Anthropic API key not configured - go to settings to add it")
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}
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client := anthropic.NewClient(
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option.WithAPIKey(c.cfg.AnthropicAPIKey),
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)
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prompt := fmt.Sprintf("Estimate calories for this %s: %s", entryType, description)
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msg, err := client.Messages.New(ctx, anthropic.MessageNewParams{
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Model: "claude-sonnet-4-6",
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MaxTokens: 1024,
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System: []anthropic.TextBlockParam{
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{Text: systemPrompt},
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},
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Messages: []anthropic.MessageParam{
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anthropic.NewUserMessage(anthropic.NewTextBlock(prompt)),
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},
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})
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if err != nil {
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return nil, fmt.Errorf("anthropic API error: %w", err)
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}
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if len(msg.Content) == 0 {
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return nil, fmt.Errorf("empty response from API")
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}
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text := msg.Content[0].Text
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// Strip markdown code fences if present
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text = strings.TrimSpace(text)
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if strings.HasPrefix(text, "```") {
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if i := strings.Index(text, "\n"); i != -1 {
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text = text[i+1:]
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}
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if i := strings.LastIndex(text, "```"); i != -1 {
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text = text[:i]
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}
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text = strings.TrimSpace(text)
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}
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var estimation types.AIEstimation
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if err := json.Unmarshal([]byte(text), &estimation); err != nil {
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return nil, fmt.Errorf("failed to parse AI response: %w (response: %s)", err, text)
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}
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// Cache products
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for _, item := range estimation.Items {
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if entryType == types.EntryFood {
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existing, _ := c.db.FindProduct(item.Name, item.Brand)
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if existing == nil {
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c.db.SaveProduct(types.Product{
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Name: item.Name,
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Brand: item.Brand,
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ServingSize: item.ServingSize,
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ServingUnit: item.ServingUnit,
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KcalPerServing: item.Kcal,
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Source: "ai",
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})
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}
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}
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}
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return &estimation, nil
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}
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