#4:
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import numpy as np
import cv2
import base64
import logging
import os
from pathlib import Path

# face recognition system:
from face_recognition_system import FaceRecognitionSystem

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Initialize FastAPI app
app = FastAPI(
    title="Face Recognition API",
    description="API for face detection and recognition using InsightFace",
    version="1.0.0"
)

# Add CORS middleware for Hugging Face Spaces
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Create necessary directories
MODELS_DIR = Path("models")
KNOWN_FACES_DIR = Path("known_faces")
for directory in [MODELS_DIR, KNOWN_FACES_DIR]:
    directory.mkdir(parents=True, exist_ok=True)

# Initialize face recognition system
try:
    face_recog_system = FaceRecognitionSystem(
        model_name="buffalo_l",
        model_root=str(MODELS_DIR)
    )
    face_recog_system.process_known_faces(str(KNOWN_FACES_DIR))
    logger.info("Face recognition system initialized successfully")
except Exception as e:
    logger.error(f"Failed to initialize face recognition system: {e}")
    raise

@app.get("/")
async def root():
    """Health check endpoint"""
    model_files = list(MODELS_DIR.glob("*"))
    known_faces = list(KNOWN_FACES_DIR.glob("*"))
    return {
        "status": "ok",
        "message": "Face Recognition API is running",
        "model_directory": str(MODELS_DIR),
        "known_faces_directory": str(KNOWN_FACES_DIR),
        "model_files": [str(f.name) for f in model_files],
        "known_faces": [str(f.name) for f in known_faces]
    }

@app.post("/detect_faces")
async def detect_faces(file: UploadFile = File(...)):
    """
    Endpoint to detect and identify faces in an uploaded image
    """
    try:
        # Validate file type
        if not file.content_type.startswith('image/'):
            raise HTTPException(status_code=400, detail="File must be an image")

        # Read and decode image
        image_data = await file.read()
        nparr = np.frombuffer(image_data, np.uint8)
        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        if img is None:
            raise HTTPException(status_code=400, detail="Failed to decode image")

        # Process image
        detected_img = face_recog_system.detect_and_identify(img)

        # Encode processed image to base64
        success, buffer = cv2.imencode('.jpg', detected_img)
        if not success:
            raise HTTPException(status_code=500, detail="Failed to encode processed image")

        processed_image_base64 = base64.b64encode(buffer).decode("utf-8")

        # Prepare response
        serializable_embeddings = {
            name: embedding.tolist() if isinstance(embedding, np.ndarray) else embedding 
            for name, embedding in face_recog_system.known_face_embeddings.items()
        }

        return JSONResponse(content={
            "status": "success",
            "processed_image": processed_image_base64,
            "faces": serializable_embeddings
        })

    except HTTPException as he:
        raise he
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        raise HTTPException(status_code=500, detail=str(e))



# Configuration for Hugging Face Spaces
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)



#3:

# from fastapi import FastAPI, File, UploadFile, HTTPException
# from fastapi.responses import JSONResponse
# from fastapi.middleware.cors import CORSMiddleware
# import numpy as np
# import cv2
# import base64
# import logging
# from face_recognition_system import FaceRecognitionSystem

# # Set up logging
# logging.basicConfig(
#     level=logging.INFO,
#     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# )
# logger = logging.getLogger(__name__)

# # Initialize FastAPI app
# app = FastAPI(
#     title="Face Recognition API",
#     description="API for face detection and recognition using InsightFace",
#     version="1.0.0"
# )

# # Add CORS middleware for Hugging Face Spaces
# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=["*"],
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# # Initialize face recognition system
# try:
#     face_recog_system = FaceRecognitionSystem()
#     # Update the path to match your Hugging Face Spaces directory structure
#     face_recog_system.process_known_faces("known_faces")
#     logger.info("Face recognition system initialized successfully")
# except Exception as e:
#     logger.error(f"Failed to initialize face recognition system: {e}")
#     raise

# @app.get("/")
# async def root():
#     """Health check endpoint"""
#     return {"status": "ok", "message": "Face Recognition API is running"}

# @app.post("/detect_faces")
# async def detect_faces(file: UploadFile = File(...)):
#     """
#     Endpoint to detect and identify faces in an uploaded image
#     """
#     try:
#         # Validate file type
#         if not file.content_type.startswith('image/'):
#             raise HTTPException(status_code=400, detail="File must be an image")

#         # Read and decode image
#         image_data = await file.read()
#         nparr = np.frombuffer(image_data, np.uint8)
#         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

#         if img is None:
#             raise HTTPException(status_code=400, detail="Failed to decode image")

#         # Process image
#         detected_img = face_recog_system.detect_and_identify(img)

#         # Encode processed image to base64
#         success, buffer = cv2.imencode('.jpg', detected_img)
#         if not success:
#             raise HTTPException(status_code=500, detail="Failed to encode processed image")

#         processed_image_base64 = base64.b64encode(buffer).decode("utf-8")

#         # Prepare response
#         serializable_embeddings = {
#             name: embedding.tolist() if isinstance(embedding, np.ndarray) else embedding 
#             for name, embedding in face_recog_system.known_face_embeddings.items()
#         }

#         return JSONResponse(content={
#             "status": "success",
#             "processed_image": processed_image_base64,
#             "faces": serializable_embeddings
#         })

#     except HTTPException as he:
#         raise he
#     except Exception as e:
#         logger.error(f"Error processing image: {e}")
#         raise HTTPException(status_code=500, detail=str(e))

# # Configuration for Hugging Face Spaces
# if __name__ == "__main__":
#     import uvicorn
#     uvicorn.run(app, host="0.0.0.0", port=7860)





# initial:
# from fastapi import FastAPI

# app = FastAPI()

# @app.get("/")
# def home():
#     '''Fuck Everyday Bitch'''
#     return {"Everything's": "OK bTICH✅"}


# final:
# #2
# from fastapi import FastAPI, File, UploadFile
# from fastapi.responses import JSONResponse
# import numpy as np
# import cv2
# import base64
# import logging
# from face_recognition_system import FaceRecognitionSystem  # import your class

# # Set up logging
# logging.basicConfig(level=logging.INFO)

# app = FastAPI()
# face_recog_system = FaceRecognitionSystem()

# # Load known faces
# try:
#     face_recog_system.process_known_faces("./data/known/custom/")
#     logging.info("Loaded known faces successfully.")
# except Exception as e:
#     logging.error(f"Error loading known faces: {e}")

# @app.post("/detect_faces")
# async def detect_faces(file: UploadFile = File(...)):
#     try:
#         # Read and decode image from the uploaded file
#         image_data = await file.read()
#         nparr = np.frombuffer(image_data, np.uint8)
#         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

#         # Check if image is loaded
#         if img is None:
#             logging.error("Failed to decode image. Ensure the uploaded file is a valid image.")
#             return JSONResponse(content={"error": "Invalid image file"}, status_code=400)

#         # Run detection and identification
#         detected_img = face_recog_system.detect_and_identify(img)

#         # Encode imNONOFage to base64
#         success, buffer = cv2.imencode('.jpg', detected_img)
#         if not success:
#             logging.error("Image encoding failed.")
#             return JSONResponse(content={"error": "Image encoding failed"}, status_code=500)

#         processed_image_base64 = base64.b64encode(buffer).decode("utf-8")

#         # Optional: Check if face embeddings were created
#         if not face_recog_system.known_face_embeddings:
#             logging.warning("No faces detected.")

#         # NOTE:
#         # Convert numpy arrays to lists for JSON serialization
#         serializable_embeddings = {
#             name: embedding.tolist() if isinstance(embedding, np.ndarray) else embedding 
#             for name, embedding in face_recog_system.known_face_embeddings.items()
#         }
#         return JSONResponse(content={
#             "processed_image": processed_image_base64, 
#             "faces": serializable_embeddings
#         })
        
#         # return JSONResponse(content={"processed_image": processed_image_base64, "faces": face_recog_system.known_face_embeddings})

#     except Exception as e:
#         logging.error(f"Error processing image: {e}")
#         return JSONResponse(content={"error": "An error occurred while processing the image"}, status_code=500)


# # main:
# # NOTE: ALWAYS FIRST CHECK IPv4-Address via: <ipconfig>
# # import uvicorn
# # if __name__ == "__main__":
# #     uvicorn.run(app='app:app',
# #                 host='192.168.1.17', port=7860, reload=True)