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"""
MEXAR Chat API - Phase 2
Handles all chat interactions with agents.
"""
from typing import Optional
from pathlib import Path
import shutil
import uuid
import logging
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form
from fastapi.responses import FileResponse
from sqlalchemy.orm import Session
from pydantic import BaseModel
from core.database import get_db
from services.agent_service import agent_service
from services.tts_service import get_tts_service
from services.storage_service import storage_service
from services.conversation_service import conversation_service
from api.deps import get_current_user
from models.user import User
from modules.reasoning_engine import create_reasoning_engine
from modules.explainability import create_explainability_generator
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/chat", tags=["chat"])
# Pydantic models for JSON requests
class ChatRequest(BaseModel):
agent_name: str
message: str
include_explainability: bool = True
include_tts: bool = False
tts_provider: str = "elevenlabs" # "elevenlabs" or "web_speech"
class MultimodalChatRequest(BaseModel):
agent_name: str
message: str = ""
class TTSRequest(BaseModel):
text: str
provider: str = "elevenlabs" # "elevenlabs" or "web_speech"
voice_id: Optional[str] = None
# ===== MAIN CHAT ENDPOINT (JSON) =====
@router.post("")
@router.post("/")
async def chat_json(
request: ChatRequest,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""
Chat with an agent using JSON body.
This is the primary endpoint used by the frontend.
"""
# Get agent with ownership check
agent = agent_service.get_agent(db, current_user, request.agent_name)
if not agent:
raise HTTPException(status_code=404, detail=f"Agent '{request.agent_name}' not found")
if agent.status != "ready":
raise HTTPException(
status_code=400,
detail=f"Agent is not ready. Current status: {agent.status}"
)
# Get/Create conversation
conversation = conversation_service.get_or_create_conversation(
db, agent.id, current_user.id
)
# Log USER message
conversation_service.add_message(
db, conversation.id, "user", request.message
)
try:
# Use agent's storage path for reasoning engine
storage_path = Path(agent.storage_path).parent
engine = create_reasoning_engine(str(storage_path))
result = engine.reason(
agent_name=agent.name,
query=request.message
)
response = {
"success": True,
"answer": result["answer"],
"confidence": result["confidence"],
"in_domain": result["in_domain"]
}
if request.include_explainability:
try:
explainer = create_explainability_generator()
response["explainability"] = explainer.generate(result)
except Exception as e:
logger.warning(f"Explainability generation failed: {e}")
response["explainability"] = result.get("explainability")
# Log ASSISTANT message
conversation_service.add_message(
db,
conversation.id,
"assistant",
result["answer"],
explainability_data=response.get("explainability"),
confidence=result["confidence"]
)
# Generate TTS if requested
if request.include_tts:
try:
tts_service = get_tts_service()
tts_result = tts_service.generate_speech(
text=result["answer"],
provider=request.tts_provider
)
response["tts"] = tts_result
except Exception as e:
logger.warning(f"TTS generation failed: {e}")
response["tts"] = {"success": False, "error": str(e)}
return response
except Exception as e:
logger.error(f"Chat error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# ===== MULTIMODAL CHAT ENDPOINT =====
@router.post("/multimodal")
async def chat_multimodal(
agent_name: str = Form(...),
message: str = Form(""),
audio: UploadFile = File(None),
image: UploadFile = File(None),
include_explainability: bool = Form(True),
include_tts: bool = Form(False),
tts_provider: str = Form("elevenlabs"),
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""
Chat with an agent using multimodal inputs (audio/image).
Uses multipart form data.
"""
from modules.multimodal_processor import create_multimodal_processor
# Get agent with ownership check
agent = agent_service.get_agent(db, current_user, agent_name)
if not agent:
raise HTTPException(status_code=404, detail=f"Agent '{agent_name}' not found")
if agent.status != "ready":
raise HTTPException(
status_code=400,
detail=f"Agent is not ready. Current status: {agent.status}"
)
# Get/Create conversation
conversation = conversation_service.get_or_create_conversation(
db, agent.id, current_user.id
)
try:
multimodal_context = ""
audio_url = None
image_url = None
# Process audio if provided
if audio and audio.filename:
# Upload to Supabase Storage
upload_result = await storage_service.upload_file(
file=audio,
bucket="chat-media",
folder=f"audio/{agent.id}"
)
audio_url = upload_result["url"]
# Save temporarily for processing
temp_dir = Path("data/temp")
temp_dir.mkdir(parents=True, exist_ok=True)
temp_path = temp_dir / f"{uuid.uuid4()}{Path(audio.filename).suffix}"
with open(temp_path, "wb") as buffer:
await audio.seek(0) # Reset file pointer
shutil.copyfileobj(audio.file, buffer)
processor = create_multimodal_processor()
audio_text = processor.process_audio(str(temp_path))
if audio_text:
multimodal_context += f"\n[AUDIO TRANSCRIPTION]: {audio_text}"
# Clean up temp file
try:
temp_path.unlink()
except:
pass
# Process image if provided
if image and image.filename:
# Upload to Supabase Storage
upload_result = await storage_service.upload_file(
file=image,
bucket="chat-media",
folder=f"images/{agent.id}"
)
image_url = upload_result["url"]
logger.info(f"[MULTIMODAL] Image uploaded to Supabase: {image_url}")
# Save temporarily for processing
temp_dir = Path("data/temp")
temp_dir.mkdir(parents=True, exist_ok=True)
temp_path = temp_dir / f"{uuid.uuid4()}{Path(image.filename).suffix}"
logger.info(f"[MULTIMODAL] Saving temp file: {temp_path}")
with open(temp_path, "wb") as buffer:
await image.seek(0) # Reset file pointer
shutil.copyfileobj(image.file, buffer)
file_size = temp_path.stat().st_size
logger.info(f"[MULTIMODAL] Temp file saved, size: {file_size} bytes")
try:
logger.info(f"[MULTIMODAL] Starting image analysis with Groq Vision...")
processor = create_multimodal_processor()
image_result = processor.process_image(str(temp_path))
logger.info(f"[MULTIMODAL] Image processing result: {image_result.get('success')}")
if image_result.get("success"):
image_desc = image_result.get("description", "")
if image_desc:
logger.info(f"[MULTIMODAL] ✓ Image analyzed successfully, description length: {len(image_desc)} chars")
logger.info(f"[MULTIMODAL] Description preview: {image_desc[:150]}...")
multimodal_context += f"\n[IMAGE DESCRIPTION]: {image_desc}"
else:
logger.warning(f"[MULTIMODAL] Image analysis returned success but empty description")
multimodal_context += f"\n[IMAGE]: User uploaded an image named {image.filename}"
else:
# Log error but don't fail - provide basic context
error_msg = image_result.get('error', 'Unknown error')
error_type = image_result.get('error_type', 'Unknown')
logger.warning(f"[MULTIMODAL] Image analysis failed - {error_type}: {error_msg}")
multimodal_context += f"\n[IMAGE]: User uploaded an image named {image.filename}"
except Exception as e:
logger.error(f"[MULTIMODAL] Image processing exception: {type(e).__name__}: {str(e)}")
import traceback
logger.error(f"[MULTIMODAL] Traceback: {traceback.format_exc()}")
multimodal_context += f"\n[IMAGE]: User uploaded an image named {image.filename}"
# Clean up temp file
try:
temp_path.unlink()
logger.info(f"[MULTIMODAL] Temp file cleaned up")
except:
pass
# Run reasoning
storage_path = Path(agent.storage_path).parent
engine = create_reasoning_engine(str(storage_path))
result = engine.reason(
agent_name=agent.name,
query=message,
multimodal_context=multimodal_context
)
# Log USER message with attachments
conversation_service.add_message(
db,
conversation.id,
"user",
message,
multimodal_data={
"audio_url": audio_url,
"image_url": image_url
}
)
response = {
"success": True,
"answer": result["answer"],
"confidence": result["confidence"],
"in_domain": result["in_domain"],
"audio_url": audio_url,
"image_url": image_url
}
if include_explainability:
try:
explainer = create_explainability_generator()
response["explainability"] = explainer.generate(result)
except Exception:
response["explainability"] = result.get("explainability")
# Log ASSISTANT message
conversation_service.add_message(
db,
conversation.id,
"assistant",
result["answer"],
explainability_data=response.get("explainability"),
confidence=result["confidence"]
)
# Generate TTS if requested
if include_tts:
try:
tts_service = get_tts_service()
tts_result = tts_service.generate_speech(
text=result["answer"],
provider=tts_provider
)
response["tts"] = tts_result
except Exception as e:
logger.warning(f"TTS generation failed: {e}")
response["tts"] = {"success": False, "error": str(e)}
return response
except Exception as e:
logger.error(f"Multimodal chat error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# ===== HISTORY ENDPOINTS =====
@router.get("/{agent_name}/history")
def get_chat_history(
agent_name: str,
limit: int = 50,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""Get conversation history with an agent."""
from services.conversation_service import conversation_service
agent = agent_service.get_agent(db, current_user, agent_name)
if not agent:
raise HTTPException(status_code=404, detail="Agent not found")
history = conversation_service.get_conversation_history(
db, agent.id, current_user.id, limit
)
return {"messages": history}
@router.delete("/{agent_name}/history")
def clear_chat_history(
agent_name: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""Clear conversation history with an agent."""
from models.conversation import Conversation
agent = agent_service.get_agent(db, current_user, agent_name)
if not agent:
raise HTTPException(status_code=404, detail="Agent not found")
conversation = db.query(Conversation).filter(
Conversation.agent_id == agent.id,
Conversation.user_id == current_user.id
).first()
if conversation:
db.delete(conversation)
db.commit()
return {"message": "Chat history cleared"}
# ===== TEXT-TO-SPEECH ENDPOINTS =====
@router.post("/tts/generate")
async def generate_tts(
request: TTSRequest,
current_user: User = Depends(get_current_user)
):
"""Generate text-to-speech audio."""
try:
tts_service = get_tts_service()
result = tts_service.generate_speech(
text=request.text,
provider=request.provider,
voice_id=request.voice_id
)
return result
except Exception as e:
logger.error(f"TTS generation error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@router.get("/tts/audio/{filename}")
async def serve_tts_audio(filename: str):
"""Serve cached TTS audio files."""
audio_path = Path("data/tts_cache") / filename
if not audio_path.exists():
raise HTTPException(status_code=404, detail="Audio file not found")
return FileResponse(
path=audio_path,
media_type="audio/mpeg",
filename=filename
)
@router.get("/tts/voices")
async def get_tts_voices(
provider: str = "elevenlabs",
current_user: User = Depends(get_current_user)
):
"""Get available TTS voices for a provider."""
try:
tts_service = get_tts_service()
voices = tts_service.get_available_voices(provider)
return {"provider": provider, "voices": voices}
except Exception as e:
logger.error(f"Failed to fetch voices: {e}")
raise HTTPException(status_code=500, detail=str(e))
@router.get("/tts/quota")
async def get_tts_quota(current_user: User = Depends(get_current_user)):
"""Check TTS quota for ElevenLabs."""
try:
tts_service = get_tts_service()
quota = tts_service.check_quota()
return quota
except Exception as e:
logger.error(f"Failed to check quota: {e}")
raise HTTPException(status_code=500, detail=str(e))
# ===== LIVE AUDIO TRANSCRIPTION =====
@router.post("/transcribe")
async def transcribe_audio(
audio: UploadFile = File(...),
language: str = Form("en"),
current_user: User = Depends(get_current_user)
):
"""Transcribe uploaded audio (for live recording)."""
from modules.multimodal_processor import create_multimodal_processor
try:
# Save audio temporarily
temp_dir = Path("data/temp")
temp_dir.mkdir(parents=True, exist_ok=True)
temp_path = temp_dir / f"{uuid.uuid4()}{Path(audio.filename).suffix}"
with open(temp_path, "wb") as buffer:
shutil.copyfileobj(audio.file, buffer)
# Transcribe
processor = create_multimodal_processor()
result = processor.process_audio(str(temp_path), language)
# Clean up
try:
temp_path.unlink()
except:
pass
if result.get("success"):
return {
"success": True,
"transcript": result.get("transcript", ""),
"language": language,
"word_count": result.get("word_count", 0)
}
else:
raise HTTPException(status_code=500, detail=result.get("error", "Transcription failed"))
except Exception as e:
logger.error(f"Audio transcription error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# ===== UTILITY FUNCTIONS =====
async def save_upload(file: UploadFile, base_path: str, subfolder: str) -> str:
"""Save an uploaded file and return its path."""
upload_dir = Path(base_path) / subfolder
upload_dir.mkdir(parents=True, exist_ok=True)
ext = Path(file.filename).suffix
filename = f"{uuid.uuid4()}{ext}"
file_path = upload_dir / filename
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
return str(file_path)
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