import os
import uuid
import shutil
import sqlite3
import json
import logging
import asyncio
import numpy as np
import chromadb
import cv2
from datetime import datetime
from typing import List, Optional
from contextlib import asynccontextmanager

# FastAPI & Utilities
from fastapi import FastAPI, UploadFile, File, BackgroundTasks, HTTPException, Request, Form
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel

# AI Libraries
import torch
from PIL import Image
from sentence_transformers import SentenceTransformer
from transformers import BlipProcessor, BlipForConditionalGeneration
from insightface.app import FaceAnalysis

# --- CONFIGURATION ---
UPLOAD_DIR = "static/uploads"
DB_PATH = "photos.db"
CHROMA_PATH = "chroma_db"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Ensure directories exist
os.makedirs(UPLOAD_DIR, exist_ok=True)

# --- LOGGING ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("CloudzyAI")

# --- GLOBAL MODELS (Loaded on Startup) ---
ai_models = {}

@asynccontextmanager
async def lifespan(app: FastAPI):
    # 1. Load CLIP for Semantic Search (Text <-> Image)
    logger.info("Loading CLIP model...")
    ai_models["clip"] = SentenceTransformer('clip-ViT-B-32', device=device)
    
    # 2. Load BLIP for Captioning
    logger.info("Loading BLIP model...")
    ai_models["blip_processor"] = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
    ai_models["blip_model"] = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
    
    # 3. Load InsightFace for Smart Analysis
    logger.info("Loading InsightFace model...")
    # 'buffalo_l' is a good default model pack. It downloads automatically on first run.
    app_face = FaceAnalysis(name='buffalo_l', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
    app_face.prepare(ctx_id=0, det_size=(640, 640))
    ai_models["face"] = app_face

    # 4. Initialize Database
    init_db()
    
    yield
    logger.info("Shutting down...")

app = FastAPI(lifespan=lifespan)
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates") # Create this folder

# --- DATABASE SETUP (SQLite + ChromaDB) ---
chroma_client = chromadb.PersistentClient(path=CHROMA_PATH)
collection = chroma_client.get_or_create_collection(name="photo_embeddings")

def init_db():
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS photos (
            id TEXT PRIMARY KEY,
            filename TEXT,
            filepath TEXT,
            upload_date TEXT,
            caption TEXT,
            tags TEXT,
            smart_analysis TEXT,
            status TEXT
        )
    """)
    conn.commit()
    conn.close()

# --- Pydantic Models ---
class PhotoResponse(BaseModel):
    id: str
    filename: str
    url: str
    caption: Optional[str] = None
    tags: List[str] = []
    smart_features: Optional[dict] = None
    upload_date: str

# --- AI PROCESSING TASKS ---
def process_image_task(photo_id: str, file_path: str):
    """
    Background task that runs the AI pipeline:
    1. Generate Caption (BLIP)
    2. Analyze Faces (InsightFace)
    3. Create Embeddings (CLIP)
    4. Update DBs
    """
    logger.info(f"Starting AI analysis for {photo_id}")
    
    try:
        # Load Images
        pil_image = Image.open(file_path).convert("RGB")
        cv_image = cv2.imread(file_path) # InsightFace needs OpenCV format
        
        # A. Captioning (BLIP)
        inputs = ai_models["blip_processor"](pil_image, return_tensors="pt").to(device)
        out = ai_models["blip_model"].generate(**inputs)
        caption = ai_models["blip_processor"].decode(out[0], skip_special_tokens=True)
        
        # B. Smart Feature: Face Analysis (InsightFace)
        faces = ai_models["face"].get(cv_image)
        face_data = []
        tags = ["ai-generated"]
        
        if len(faces) > 0:
            avg_age = np.mean([face.age for face in faces])
            gender_counts = {"M": 0, "F": 0}
            for face in faces:
                gender = "M" if face.sex == 1 else "F"
                gender_counts[gender] += 1
                face_data.append({
                    "age": int(face.age),
                    "gender": gender,
                    "confidence": float(face.det_score)
                })
            
            # Smart Tagging based on Analysis
            tags.append("person")
            tags.append(f"{len(faces)} people")
            if gender_counts["M"] > gender_counts["F"]: tags.append("mostly_male")
            if gender_counts["F"] > gender_counts["M"]: tags.append("mostly_female")
            if avg_age < 18: tags.append("youth")
            elif avg_age > 60: tags.append("senior")
            else: tags.append("adult")
        else:
            tags.append("scenery") # Fallback tag
            face_data = {"message": "No faces detected"}

        # Combine caption words into tags (Simple approach)
        tags.extend([word for word in caption.split() if len(word) > 4])
        tags = list(set(tags)) # unique

        # C. Embedding (CLIP)
        # We embed the IMAGE itself for semantic search
        embedding = ai_models["clip"].encode(pil_image).tolist()
        
        # D. Save Results
        conn = sqlite3.connect(DB_PATH)
        cursor = conn.cursor()
        cursor.execute("""
            UPDATE photos 
            SET caption = ?, tags = ?, smart_analysis = ?, status = 'completed'
            WHERE id = ?
        """, (caption, json.dumps(tags), json.dumps(face_data), photo_id))
        conn.commit()
        conn.close()
        
        # Save to ChromaDB
        collection.add(
            ids=[photo_id],
            embeddings=[embedding],
            metadatas=[{"caption": caption}]
        )
        
        logger.info(f"AI processing completed for {photo_id}")

    except Exception as e:
        logger.error(f"Error processing {photo_id}: {e}")
        conn = sqlite3.connect(DB_PATH)
        conn.execute("UPDATE photos SET status = 'failed' WHERE id = ?", (photo_id,))
        conn.commit()
        conn.close()

# --- API ENDPOINTS ---

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    """Serve the UI"""
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/upload", response_model=PhotoResponse)
async def upload_photo(file: UploadFile = File(...), background_tasks: BackgroundTasks = None):
    """
    1. Validate file
    2. Save to disk
    3. create DB record
    4. Trigger Async AI Task
    """
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="File must be an image")
    
    file_id = str(uuid.uuid4())
    ext = file.filename.split(".")[-1]
    filename = f"{file_id}.{ext}"
    file_path = os.path.join(UPLOAD_DIR, filename)
    
    # Save file
    with open(file_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)
        
    # Initial DB Record
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
        INSERT INTO photos (id, filename, filepath, upload_date, status)
        VALUES (?, ?, ?, ?, 'processing')
    """, (file_id, file.filename, file_path, datetime.now().isoformat()))
    conn.commit()
    conn.close()
    
    # Trigger AI
    background_tasks.add_task(process_image_task, file_id, file_path)
    
    return {
        "id": file_id,
        "filename": file.filename,
        "url": f"/static/uploads/{filename}",
        "upload_date": datetime.now().isoformat()
    }

@app.get("/photo/{photo_id}", response_model=PhotoResponse)
async def get_photo(photo_id: str):
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    cursor = conn.cursor()
    row = cursor.execute("SELECT * FROM photos WHERE id = ?", (photo_id,)).fetchone()
    conn.close()
    
    if not row:
        raise HTTPException(status_code=404, detail="Photo not found")
        
    return {
        "id": row["id"],
        "filename": row["filename"],
        "url": f"/{row['filepath']}",
        "caption": row["caption"],
        "tags": json.loads(row["tags"]) if row["tags"] else [],
        "smart_features": json.loads(row["smart_analysis"]) if row["smart_analysis"] else None,
        "upload_date": row["upload_date"]
    }

@app.get("/search")
async def search_photos(q: str):
    """
    Semantic Search:
    1. Embed query text using CLIP.
    2. Search ChromaDB for nearest image vectors.
    3. Retrieve metadata from SQLite.
    """
    # Embed query text
    query_vec = ai_models["clip"].encode(q).tolist()
    
    # Query Vector DB
    results = collection.query(
        query_embeddings=[query_vec],
        n_results=5
    )
    
    ids = results["ids"][0]
    if not ids:
        return []

    # Fetch details from SQLite
    placeholders = ",".join("?" * len(ids))
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    cursor = conn.cursor()
    
    # We use a trick to preserve order or just fetch all and map
    rows = cursor.execute(f"SELECT * FROM photos WHERE id IN ({placeholders})", ids).fetchall()
    conn.close()
    
    # Format response
    response_data = []
    for row in rows:
        response_data.append({
            "id": row["id"],
            "url": f"/{row['filepath']}",
            "caption": row["caption"],
            "tags": json.loads(row["tags"]) if row["tags"] else [],
            "smart_features": json.loads(row["smart_analysis"]) if row["smart_analysis"] else None,
        })
        
    return response_data

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