frontend/src/services/ai.service.ts

/**
 * AI Service
 * 
 * Handles communication with the AI proxy endpoint for chat completions.
 */

import {
  NO_BACKEND_MODE,
  API_ENDPOINTS,
  AI_CONFIG,
  buildApiUrl,
  getApiHeaders,
  logApiDebug,
} from '../config';

// =============================================================================
// Type Definitions
// =============================================================================

export interface AIMessage {
  role: 'system' | 'user' | 'assistant';
  content: string;
}

export interface AIRequest {
  messages: AIMessage[];
  model?: string;
}

export interface AIResponse {
  id: string;
  object: string;
  created: number;
  model: string;
  choices: Array<{
    index: number;
    message: AIMessage;
    finish_reason: string;
  }>;
  usage: {
    prompt_tokens: number;
    completion_tokens: number;
    total_tokens: number;
  };
}

// =============================================================================
// Mock Response Generator
// =============================================================================

const createMockResponse = (userMessage: string): AIResponse => {
  return {
    id: `mock-${Date.now()}`,
    object: 'chat.completion',
    created: Math.floor(Date.now() / 1000),
    model: 'mock-model',
    choices: [
      {
        index: 0,
        message: {
          role: 'assistant',
          content: `I received your message: "${userMessage}". This is a mock response since the backend is not connected.`,
        },
        finish_reason: 'stop',
      },
    ],
    usage: {
      prompt_tokens: 10,
      completion_tokens: 20,
      total_tokens: 30,
    },
  };
};

// =============================================================================
// AI Service
// =============================================================================

export const aiService = {
  /**
   * Send chat messages to the AI proxy
   * @param messages - Array of chat messages
   * @param token - JWT token for authentication
   * @returns AI response
   */
  async chat(messages: AIMessage[], token?: string): Promise<AIResponse> {
    if (NO_BACKEND_MODE) {
      logApiDebug('AI Chat', 'Using mock mode');
      return new Promise((resolve) => {
        setTimeout(() => {
          const lastUserMessage = messages.filter((m) => m.role === 'user').pop();
          resolve(createMockResponse(lastUserMessage?.content || 'Hello'));
        }, AI_CONFIG.MOCK_RESPONSE_DELAY);
      });
    }

    const url = buildApiUrl(API_ENDPOINTS.AI.PROXY);

    logApiDebug('AI Request', {
      url,
      hasToken: !!token,
      messageCount: messages.length,
    });

    try {
      const response = await fetch(url, {
        method: 'POST',
        headers: getApiHeaders(token),
        body: JSON.stringify({ messages } as AIRequest),
      });

      logApiDebug('AI Response Status', response.status);

      if (!response.ok) {
        const errorText = await response.text();
        logApiDebug('AI Error Response', errorText);

        let errorDetail = 'AI request failed';
        try {
          const errorData = JSON.parse(errorText);
          errorDetail = errorData.detail || errorDetail;
        } catch {
          errorDetail = errorText || errorDetail;
        }
        throw new Error(`${response.status}: ${errorDetail}`);
      }

      const data = await response.json();
      logApiDebug('AI Success', {
        id: data.id,
        model: data.model,
        choicesCount: data.choices?.length,
      });

      return data;
    } catch (error) {
      console.error('AI proxy error:', error);
      throw error;
    }
  },

  /**
   * Simple helper for single message chat
   * @param content - User message content
   * @param token - JWT token for authentication
   * @param systemPrompt - Optional custom system prompt
   * @returns AI response text
   */
  async sendMessage(content: string, token?: string, systemPrompt?: string): Promise<string> {
    const messages: AIMessage[] = [
      {
        role: 'system',
        content: systemPrompt || AI_CONFIG.DEFAULT_SYSTEM_PROMPT,
      },
      {
        role: 'user',
        content,
      },
    ];

    const response = await this.chat(messages, token);
    return response.choices[0]?.message?.content || 'No response';
  },

  /**
   * Send a message with an image to AI for analysis
   * @param content - User message content
   * @param imageBase64 - Base64 encoded image data
   * @param token - JWT token for authentication
   * @returns AI response text
   */
  async sendMessageWithImage(content: string, imageBase64: string, token?: string): Promise<string> {
    if (NO_BACKEND_MODE) {
      logApiDebug('AI Image Analysis', 'Using mock mode');
      return new Promise((resolve) => {
        setTimeout(() => {
          resolve('This is a mock image analysis response. The image appears to show an interesting scene. In production, this would be analyzed by Gemini AI.');
        }, AI_CONFIG.MOCK_RESPONSE_DELAY);
      });
    }

    const url = buildApiUrl(API_ENDPOINTS.AI.PROXY);

    logApiDebug('AI Image Request', {
      url,
      hasToken: !!token,
      hasImage: !!imageBase64,
    });

    // Gemini vision format - using multimodal content
    const messages = [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: content,
          },
          {
            type: 'image_url',
            image_url: {
              url: `data:image/jpeg;base64,${imageBase64}`,
            },
          },
        ],
      },
    ];

    try {
      const response = await fetch(url, {
        method: 'POST',
        headers: getApiHeaders(token),
        body: JSON.stringify({ messages }),
      });

      logApiDebug('AI Image Response Status', response.status);

      if (!response.ok) {
        const errorText = await response.text();
        logApiDebug('AI Image Error Response', errorText);

        let errorDetail = 'AI image request failed';
        try {
          const errorData = JSON.parse(errorText);
          errorDetail = errorData.detail || errorDetail;
        } catch {
          errorDetail = errorText || errorDetail;
        }
        throw new Error(`${response.status}: ${errorDetail}`);
      }

      const data = await response.json();
      logApiDebug('AI Image Success', {
        id: data.id,
        model: data.model,
      });

      return data.choices[0]?.message?.content || 'No response';
    } catch (error) {
      console.error('AI image proxy error:', error);
      throw error;
    }
  },
};