import pickle
from minisom import MiniSom
import numpy as np
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
from scipy.ndimage import median_filter
from scipy.signal import convolve2d
import cv2
import math

class InputData(BaseModel):
    data: List[float]  # Lista de características numéricas (floats)

app = FastAPI()

def build_model():
    with open('somlucuma.pkl', 'rb') as fid:
        somhuella = pickle.load(fid)
    MM = np.loadtxt('matrizMM.txt', delimiter=" ")
    return somhuella,MM

def sobel(patron):
    gx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=np.float32)
    gy = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=np.float32)

    Gx = convolve2d(patron, gx, mode='valid')
    Gy = convolve2d(patron, gy, mode='valid')

    return Gx, Gy

def medfilt2(G, d=3):
    return median_filter(G, size=d)

def orientacion(patron, w):
    Gx, Gy = sobel(patron)
    Gx = medfilt2(Gx)
    Gy = medfilt2(Gy)

    m, n = Gx.shape
    mOrientaciones = np.zeros((m // w, n // w), dtype=np.float32)

    for i in range(m // w):
        for j in range(n // w):
            Gx_patch = Gx[i*w:(i+1)*w, j*w:(j+1)*w]
            Gy_patch = Gy[i*w:(i+1)*w, j*w:(j+1)*w]

            YY = np.sum(2 * Gx_patch * Gy_patch)
            XX = np.sum(Gx_patch**2 - Gy_patch**2)

            mOrientaciones[i, j] = (0.5 * np.arctan2(YY, XX) + np.pi / 2.0) * (18.0 / np.pi)

    return mOrientaciones

def redimensionar(img, h, v):
    return cv2.resize(img, (h, v), interpolation=cv2.INTER_AREA)
    
def testeo(som,archivo):
    Xtest = redimensionar(archivo,256,256)
    Xtest = np.array(Xtest)
    Xtest = Xtest.astype('float32') / 255.0
    Xtest = cv2.cvtColor(Xtest, cv2.COLOR_BGR2GRAY)
    orientaciones = orientacion(Xtest, w=14)
    orientaciones = orientaciones.reshape(-1)
    Xtest = np.concatenate([orientaciones.ravel()])
    return som.winner(Xtest)
    
som, MM = build_model()  # Construir modelo

@app.post("/predict/")
async def predict(data: InputData):
    print(f"Data: {data}")
    global som
    global MM
    try:
        
        #input_data = np.array(data.data).reshape(256, 256, 3)
        
        #representative_data = representativo(input_data)
        #representative_data = representative_data.reshape(1, -1)
        w = testeo(som,data.data)
        prediction = MM[w]
        
        return {"prediction": prediction}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))