"""
Cora Master API - Etymology App Integration
A unified endpoint for generating historical illustrations for etymological entries.
"""

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from typing import Optional, List
import os
import base64
from io import BytesIO

from cora_engine import CoraEngine
from cora_curator import CoraCurator
from cora_vision import CoraVision
from cora_memory import CoraMemory

app = FastAPI(title="Cora Visual Etymology API", version="1.0.0")

# Enable CORS for etymology app integration
app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",           # Next.js development
        "https://le-clef-mot.vercel.app",  # Production (update with your domain)
        "https://*.vercel.app"             # Vercel preview deployments
    ],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize components
engine = CoraEngine()
curator = CoraCurator()
vision = CoraVision()
memory = CoraMemory()

# Mount static files
if not os.path.exists("archive_images"):
    os.makedirs("archive_images")
app.mount("/archive_images", StaticFiles(directory="archive_images"), name="archive_images")


class IllustrationRequest(BaseModel):
    word: str
    etymology_context: Optional[str] = None
    visual_prompt: Optional[str] = None  # NEW: Accept visual prompt from Cledor
    style: Optional[str] = "historical_illustration"  # or "daily_life", "epic_dimension"


class IllustrationResponse(BaseModel):
    success: bool
    image_url: Optional[str] = None
    image_base64: Optional[str] = None
    prompt_used: str
    tags: List[str] = []
    source: str  # "generated" or "archive"
    error: Optional[str] = None


@app.post("/api/v1/generate_illustration", response_model=IllustrationResponse)
async def generate_illustration(request: IllustrationRequest):
    """
    Generate a historical illustration for an etymological entry.
    
    Pipeline:
    1. Use visual_prompt if provided (from Cledor), otherwise use Curator to refine
    2. Engine attempts generation (may fail with 402)
    3. On failure, RAG fallback searches archive for relevant artifacts
    4. Returns either generated image or museum artifact
    """
    try:
        # Step 1: Determine prompt to use
        if request.visual_prompt:
            # Cledor already generated the prompt, use it directly
            refined_prompt = request.visual_prompt
            print(f"[Cora API] Using Cledor's visual prompt: {refined_prompt[:100]}...")
        else:
            # Fallback: Build prompt from etymology data
            base_prompt = request.word
            if request.etymology_context:
                base_prompt = f"{request.word}: {request.etymology_context}"
            
            # Curator refinement
            refined_prompt = curator.refine_prompt(base_prompt)
            print(f"[Cora API] Curator refined prompt: {refined_prompt[:100]}...")
        
        # Step 2: Attempt generation (with built-in RAG fallback)
        try:
            result_image = engine.generate_from_text(refined_prompt)
            source = "generated"
        except Exception as gen_error:
            # RAG fallback is already built into engine.generate_from_text
            # If we reach here, both generation and fallback failed
            return IllustrationResponse(
                success=False,
                prompt_used=refined_prompt,
                source="none",
                error=str(gen_error)
            )
        
        # Step 4: Save to archive and generate response
        # Save image
        filename = f"etym_{request.word.replace(' ', '_')}_{os.urandom(4).hex()}.png"
        filepath = os.path.join("archive_images", filename)
        result_image.save(filepath)
        
        # Archive in memory
        emb = vision.embed_image(result_image)
        tags = vision.detect_tags(result_image)
        tags.append(f"etymology:{request.word}")
        memory.save(filepath, emb, request.word, tags)
        
        # Generate URL
        image_url = f"http://localhost:8000/archive_images/{filename}"
        
        # Optionally encode as base64 for direct embedding
        buffered = BytesIO()
        result_image.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode()
        
        return IllustrationResponse(
            success=True,
            image_url=image_url,
            image_base64=img_str,
            prompt_used=refined_prompt,
            tags=tags,
            source=source
        )
        
    except Exception as e:
        return IllustrationResponse(
            success=False,
            prompt_used=request.word,
            source="error",
            error=str(e)
        )


@app.get("/api/v1/search_archive")
async def search_archive(query: str, limit: int = 5):
    """
    Search the visual archive for relevant artifacts.
    Useful for the etymology app to show related historical images.
    """
    try:
        emb = vision.embed_text(query)
        results = memory.search_by_vector(emb, k=limit)
        
        images = []
        if results['ids']:
            ids = results['ids'][0]
            metadatas = results['metadatas'][0]
            distances = results['distances'][0]
            
            for i, uid in enumerate(ids):
                path = metadatas[i].get('path')
                if path and os.path.exists(path):
                    filename = os.path.basename(path)
                    image_url = f"http://localhost:8000/archive_images/{filename}"
                    images.append({
                        "url": image_url,
                        "tags": metadatas[i].get('tags'),
                        "prompt": metadatas[i].get('prompt'),
                        "score": float(distances[i])
                    })
        
        return {"results": images}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/health")
async def health_check():
    """Health check endpoint for the etymology app to verify API status."""
    return {
        "status": "healthy",
        "components": {
            "engine": {
                "status": engine.client is not None,
                "backend": "cloud (huggingface)" if engine.client else "none"
            },
            "curator": {
                "status": curator.backend != "none",
                "backend": curator.backend,
                "model": curator.MODEL_ID
            },
            "vision": vision is not None,
            "memory": memory is not None
        }
    }


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)