routes/mcp_routes.py

"""Routes pour exposer MCP via FastAPI pour Swagger UI"""

from fastapi import APIRouter, HTTPException, UploadFile, File
from fastapi.responses import FileResponse
from typing import Dict, Any, Optional
from pydantic import BaseModel, Field
import logging
import json
import tempfile
import os
from pathlib import Path

from services.mcp_service import mcp_server
from services.stance_model_manager import stance_model_manager
from services.label_model_manager import kpa_model_manager
from services.generate_model_manager import generate_model_manager
from models.mcp_models import (
    ToolListResponse, 
    ToolInfo, 
    ToolCallRequest, 
    ToolCallResponse,
    DetectStanceResponse,
    MatchKeypointResponse,
    TranscribeAudioResponse,
    GenerateSpeechResponse
)
from models.generate import GenerateRequest, GenerateResponse
from datetime import datetime

router = APIRouter(prefix="/api/v1/mcp", tags=["MCP"])
logger = logging.getLogger(__name__)


# ===== Models pour chaque outil MCP =====

class DetectStanceRequest(BaseModel):
    """Request pour détecter la stance d'un argument"""
    topic: str = Field(..., description="Le sujet du débat")
    argument: str = Field(..., description="L'argument à analyser")
    
    class Config:
        json_schema_extra = {
            "example": {
                "topic": "Climate change is real",
                "argument": "Rising global temperatures prove it"
            }
        }

class MatchKeypointRequest(BaseModel):
    """Request pour matcher un argument avec un keypoint"""
    argument: str = Field(..., description="L'argument à évaluer")
    key_point: str = Field(..., description="Le keypoint de référence")
    
    class Config:
        json_schema_extra = {
            "example": {
                "argument": "Renewable energy reduces emissions",
                "key_point": "Environmental benefits"
            }
        }


class GenerateSpeechRequest(BaseModel):
    """Request pour générer de la parole"""
    text: str = Field(..., description="Texte à convertir en parole")
    voice: str = Field(default="Aaliyah-PlayAI", description="Voix à utiliser")
    format: str = Field(default="wav", description="Format audio (wav, mp3, etc.)")
    
    class Config:
        json_schema_extra = {
            "example": {
                "text": "Hello, this is a test",
                "voice": "Aaliyah-PlayAI",
                "format": "wav"
            }
        }



# ===== Routes MCP =====

@router.get("/health", summary="Health Check MCP")
async def mcp_health():
    """Health check pour le serveur MCP"""
    try:
        # Liste hardcodée des outils disponibles (plus fiable)
        tool_names = [
            "detect_stance",
            "match_keypoint_argument",
            "transcribe_audio",
            "generate_speech",
            "generate_argument",
            "health_check"
        ]
        return {
            "status": "healthy",
            "tools": tool_names,
            "tool_count": len(tool_names)
        }
    except Exception as e:
        logger.error(f"MCP health check error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@router.get("/tools", response_model=ToolListResponse, summary="Liste des outils MCP")
async def list_mcp_tools():
    """Liste tous les outils MCP disponibles"""
    try:
        # Définir manuellement les outils avec leurs schémas
        tool_list = [
            ToolInfo(
                name="detect_stance",
                description="Détecte si un argument est PRO ou CON pour un topic donné",
                input_schema={
                    "type": "object",
                    "properties": {
                        "topic": {"type": "string", "description": "Le sujet du débat"},
                        "argument": {"type": "string", "description": "L'argument à analyser"}
                    },
                    "required": ["topic", "argument"]
                }
            ),
            ToolInfo(
                name="match_keypoint_argument",
                description="Détermine si un argument correspond à un keypoint",
                input_schema={
                    "type": "object",
                    "properties": {
                        "argument": {"type": "string", "description": "L'argument à évaluer"},
                        "key_point": {"type": "string", "description": "Le keypoint de référence"}
                    },
                    "required": ["argument", "key_point"]
                }
            ),
            ToolInfo(
                name="transcribe_audio",
                description="Convertit un fichier audio en texte",
                input_schema={
                    "type": "object",
                    "properties": {
                        "audio_path": {"type": "string", "description": "Chemin vers le fichier audio"}
                    },
                    "required": ["audio_path"]
                }
            ),
            ToolInfo(
                name="generate_speech",
                description="Convertit du texte en fichier audio",
                input_schema={
                    "type": "object",
                    "properties": {
                        "text": {"type": "string", "description": "Texte à convertir en parole"},
                        "voice": {"type": "string", "description": "Voix à utiliser", "default": "Aaliyah-PlayAI"},
                        "format": {"type": "string", "description": "Format audio", "default": "wav"}
                    },
                    "required": ["text"]
                }
            ),
            ToolInfo(
                name="generate_argument",
                description="Génère un argument de débat pour un topic et une position donnés",
                input_schema={
                    "type": "object",
                    "properties": {
                        "topic": {"type": "string", "description": "Le sujet du débat"},
                        "position": {"type": "string", "description": "La position à prendre (positive ou negative)"}
                    },
                    "required": ["topic", "position"]
                }
            ),
            ToolInfo(
                name="health_check",
                description="Health check pour le serveur MCP",
                input_schema={
                    "type": "object",
                    "properties": {},
                    "required": []
                }
            )
        ]
        
        return ToolListResponse(tools=tool_list, count=len(tool_list))
    except Exception as e:
        logger.error(f"Error listing MCP tools: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/tools/call", response_model=ToolCallResponse, summary="Appeler un outil MCP")
async def call_mcp_tool(request: ToolCallRequest):
    """
    Appelle un outil MCP par son nom avec des arguments
    
    **Exemples d'utilisation:**
    
    1. **detect_stance** - Détecter la stance d'un argument:
    ```json
    {
      "tool_name": "detect_stance",
      "arguments": {
        "topic": "Climate change is real",
        "argument": "Rising global temperatures prove it"
      }
    }
    ```
    
    2. **match_keypoint_argument** - Matcher un argument avec un keypoint:
    ```json
    {
      "tool_name": "match_keypoint_argument",
      "arguments": {
        "argument": "Renewable energy reduces emissions",
        "key_point": "Environmental benefits"
      }
    }
    ```
    
    3. **generate_argument** - Générer un argument:
    ```json
    {
      "tool_name": "generate_argument",
      "arguments": {
        "topic": "Assisted suicide should be legal",
        "position": "positive"
      }
    }
    ```
    
    4. **transcribe_audio** - Transcrire un audio:
    ```json
    {
      "tool_name": "transcribe_audio",
      "arguments": {
        "audio_path": "/path/to/audio.wav"
      }
    }
    ```
    
    5. **generate_speech** - Générer de la parole:
    ```json
    {
      "tool_name": "generate_speech",
      "arguments": {
        "text": "Hello, this is a test",
        "voice": "Aaliyah-PlayAI",
        "format": "wav"
      }
    }
    ```
    """
    try:
        result = await mcp_server.call_tool(request.tool_name, request.arguments)
        # Gérer différents types de retours MCP
        if isinstance(result, dict):
            # Si le résultat contient une clé "result" avec une liste de ContentBlock
            if "result" in result and isinstance(result["result"], list) and len(result["result"]) > 0:
                content_block = result["result"][0]
                if hasattr(content_block, 'text') and content_block.text:
                    try:
                        final_result = json.loads(content_block.text)
                    except json.JSONDecodeError:
                        final_result = {"text": content_block.text}
                else:
                    final_result = result
            else:
                final_result = result
        elif isinstance(result, (list, tuple)) and len(result) > 0:
            # Si c'est une liste de ContentBlock, extraire le contenu
            if hasattr(result[0], 'text') and result[0].text:
                try:
                    final_result = json.loads(result[0].text)
                except json.JSONDecodeError:
                    final_result = {"text": result[0].text}
            else:
                final_result = {"result": result[0] if result else {}}
        else:
            final_result = {"result": result}
        
        return ToolCallResponse(
            success=True,
            result=final_result,
            tool_name=request.tool_name
        )
    except Exception as e:
        logger.error(f"Error calling MCP tool {request.tool_name}: {e}")
        return ToolCallResponse(
            success=False,
            error=str(e),
            tool_name=request.tool_name
        )


# ===== Routes individuelles pour chaque outil (pour Swagger) =====

@router.post("/tools/detect-stance", response_model=DetectStanceResponse, summary="Détecter la stance d'un argument")
async def mcp_detect_stance(request: DetectStanceRequest):
    """Détecte si un argument est PRO ou CON pour un topic donné"""
    try:
        # Vérifier que le modèle est chargé
        if not stance_model_manager.model_loaded:
            raise HTTPException(status_code=503, detail="Stance model not loaded")
        
        # Appeler directement le modèle (plus fiable que via MCP)
        result = stance_model_manager.predict(request.topic, request.argument)
        
        # Construire la réponse structurée directement depuis le résultat du modèle
        response = DetectStanceResponse(
            predicted_stance=result["predicted_stance"],
            confidence=result["confidence"],
            probability_con=result["probability_con"],
            probability_pro=result["probability_pro"]
        )
        
        logger.info(f"Stance prediction: {response.predicted_stance} (conf={response.confidence:.4f})")
        return response
        
    except HTTPException:
        raise
    except KeyError as e:
        logger.error(f"Missing key in detect_stance response: {e}")
        raise HTTPException(status_code=500, detail=f"Invalid response format: missing {e}")
    except Exception as e:
        logger.error(f"Error in detect_stance: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error executing tool detect_stance: {e}")

@router.post("/tools/match-keypoint", response_model=MatchKeypointResponse, summary="Matcher un argument avec un keypoint")
async def mcp_match_keypoint(request: MatchKeypointRequest):
    """Détermine si un argument correspond à un keypoint"""
    try:
        # Vérifier que le modèle est chargé
        if not kpa_model_manager.model_loaded:
            raise HTTPException(status_code=503, detail="KPA model not loaded")
        
        # Appeler directement le modèle (plus fiable que via MCP)
        result = kpa_model_manager.predict(request.argument, request.key_point)
        
        # Construire la réponse structurée directement depuis le résultat du modèle
        response = MatchKeypointResponse(
            prediction=result["prediction"],
            label=result["label"],
            confidence=result["confidence"],
            probabilities=result["probabilities"]
        )
        
        logger.info(f"Keypoint matching: {response.label} (conf={response.confidence:.4f})")
        return response
        
    except HTTPException:
        raise
    except KeyError as e:
        logger.error(f"Missing key in match_keypoint response: {e}")
        raise HTTPException(status_code=500, detail=f"Invalid response format: missing {e}")
    except Exception as e:
        logger.error(f"Error in match_keypoint_argument: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error executing tool match_keypoint_argument: {e}")

@router.post("/tools/transcribe-audio", response_model=TranscribeAudioResponse, summary="Transcrire un audio en texte")
async def mcp_transcribe_audio(file: UploadFile = File(...)):
    """Convertit un fichier audio en texte (upload de fichier)"""
    # Vérifier le type de fichier
    if not file.content_type or not file.content_type.startswith('audio/'):
        raise HTTPException(status_code=400, detail="File must be an audio file")
    
    # Créer un fichier temporaire
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
        temp_path = temp_file.name
        content = await file.read()
        
        if len(content) == 0:
            os.unlink(temp_path)
            raise HTTPException(status_code=400, detail="Audio file is empty")
        
        temp_file.write(content)
    
    try:
        # Appeler le service MCP avec le chemin temporaire
        result = await mcp_server.call_tool("transcribe_audio", {
            "audio_path": temp_path
        })
        
        # Extraire le texte du résultat MCP
        transcribed_text = None
        if isinstance(result, dict):
            if "result" in result and isinstance(result["result"], list) and len(result["result"]) > 0:
                content_block = result["result"][0]
                if hasattr(content_block, 'text'):
                    transcribed_text = content_block.text
            elif "text" in result:
                transcribed_text = result["text"]
        elif isinstance(result, str):
            transcribed_text = result
        elif isinstance(result, (list, tuple)) and len(result) > 0:
            if hasattr(result[0], 'text'):
                transcribed_text = result[0].text
            else:
                transcribed_text = str(result[0])
        else:
            transcribed_text = str(result)
        
        if not transcribed_text:
            raise HTTPException(status_code=500, detail="Empty transcription result from MCP tool")
        
        response = TranscribeAudioResponse(text=transcribed_text)
        logger.info(f"Audio transcribed: {len(transcribed_text)} characters")
        return response
        
    except FileNotFoundError as e:
        logger.error(f"File not found in transcribe_audio: {e}")
        raise HTTPException(status_code=500, detail=f"Error executing tool transcribe_audio: {e}")
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error in transcribe_audio: {e}")
        raise HTTPException(status_code=500, detail=f"Error executing tool transcribe_audio: {e}")
    finally:
        # Nettoyer le fichier temporaire
        if os.path.exists(temp_path):
            os.unlink(temp_path)

@router.post("/tools/generate-speech", summary="Générer de la parole à partir de texte")
async def mcp_generate_speech(request: GenerateSpeechRequest):
    """Convertit du texte en fichier audio (téléchargeable)"""
    try:
        result = await mcp_server.call_tool("generate_speech", {
            "text": request.text,
            "voice": request.voice,
            "format": request.format
        })
        
        # Extraire le chemin audio du résultat MCP
        audio_path = None
        if isinstance(result, dict):
            if "result" in result and isinstance(result["result"], list) and len(result["result"]) > 0:
                content_block = result["result"][0]
                if hasattr(content_block, 'text'):
                    audio_path = content_block.text
            elif "audio_path" in result:
                audio_path = result["audio_path"]
        elif isinstance(result, str):
            audio_path = result
        elif isinstance(result, (list, tuple)) and len(result) > 0:
            if hasattr(result[0], 'text'):
                audio_path = result[0].text
            else:
                audio_path = str(result[0])
        else:
            audio_path = str(result)
        
        # Nettoyer le chemin si c'est une représentation string d'objet
        if audio_path and isinstance(audio_path, str):
            # Si c'est une représentation d'objet TextContent, extraire le chemin
            if "text='" in audio_path and ".wav" in audio_path:
                import re
                match = re.search(r"text='([^']+)'", audio_path)
                if match:
                    audio_path = match.group(1)
        
        if not audio_path:
            raise HTTPException(status_code=500, detail="Empty audio path from MCP tool")
        
        # Vérifier que le fichier existe
        if not Path(audio_path).exists():
            raise HTTPException(status_code=500, detail=f"Audio file not found: {audio_path}")
        
        # Déterminer le type MIME
        media_type = "audio/wav" if request.format == "wav" else "audio/mpeg"
        
        # Retourner le fichier pour téléchargement
        logger.info(f"Speech generated: {audio_path}")
        return FileResponse(
            path=audio_path,
            filename=f"speech.{request.format}",
            media_type=media_type,
            headers={
                "Content-Disposition": f"attachment; filename=speech.{request.format}"
            }
        )
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error in generate_speech: {e}")
        raise HTTPException(status_code=500, detail=f"Error executing tool generate_speech: {e}")

@router.post("/tools/generate-argument", response_model=GenerateResponse, summary="Générer un argument de débat")
async def mcp_generate_argument(request: GenerateRequest):
    """Génère un argument de débat pour un topic et une position donnés"""
    try:
        # Vérifier que le modèle est chargé
        if not generate_model_manager.model_loaded:
            raise HTTPException(status_code=503, detail="Generation model not loaded")
        
        # Appeler directement le modèle (plus fiable que via MCP)
        argument_text = generate_model_manager.generate(
            topic=request.topic,
            position=request.position
        )
        
        # Construire la réponse structurée
        response = GenerateResponse(
            topic=request.topic,
            position=request.position,
            argument=argument_text,
            timestamp=datetime.now().isoformat()
        )
        
        logger.info(f"Argument generated for topic '{request.topic}' with position '{request.position}': {len(response.argument)} characters")
        return response
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error in generate_argument: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error executing tool generate_argument: {e}")

@router.get("/tools/health-check", summary="Health check MCP (outil)")
async def mcp_tool_health_check() -> Dict[str, Any]:
    """Health check via l'outil MCP"""
    try:
        result = await mcp_server.call_tool("health_check", {})
        # Gérer différents types de retours MCP
        import json
        if isinstance(result, dict):
            # Si le résultat contient une clé "result" avec une liste de ContentBlock
            if "result" in result and isinstance(result["result"], list) and len(result["result"]) > 0:
                content_block = result["result"][0]
                if hasattr(content_block, 'text') and content_block.text:
                    try:
                        return json.loads(content_block.text)
                    except json.JSONDecodeError:
                        return {"text": content_block.text}
            return result
        elif isinstance(result, (list, tuple)) and len(result) > 0:
            if hasattr(result[0], 'text') and result[0].text:
                try:
                    return json.loads(result[0].text)
                except json.JSONDecodeError:
                    return {"text": result[0].text}
            return {"result": result[0] if result else {}}
        return {"result": result}
    except Exception as e:
        logger.error(f"Error in health_check tool: {e}")
        raise HTTPException(status_code=500, detail=f"Error executing tool health_check: {e}")