from fastapi import FastAPI, File, UploadFile, HTTPException
from PIL import Image
import numpy as np
import pickle
from io import BytesIO
import math

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

def sobel(I):
    m, n = I.shape
    Gx = np.zeros([m-2, n-2], np.float32)
    Gy = np.zeros([m-2, n-2], np.float32)
    gx = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
    gy = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]
    for j in range(1, m-2):
        for i in range(1, n-2):
            Gx[j-1, i-1] = sum(sum(I[j-1:j+2, i-1:i+2] * gx))
            Gy[j-1, i-1] = sum(sum(I[j-1:j+2, i-1:i+2] * gy))
    return Gx, Gy

def medfilt2(G, d=3):
    m, n = G.shape
    temp = np.zeros([m+2*(d//2), n+2*(d//2)], np.float32)
    salida = np.zeros([m, n], np.float32)
    temp[1:m+1, 1:n+1] = G
    for i in range(1, m):
        for j in range(1, n):
            A = np.asarray(temp[i-1:i+2, j-1:j+2]).reshape(-1)
            salida[i-1, j-1] = np.sort(A)[d+1]
    return salida

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], np.float32)
    for i in range(m//w):
        for j in range(n//w):
            YY = sum(sum(2*Gx[i*w:(i+1)*w, j:j+1] * Gy[i*w:(i+1)*w, j:j+1]))
            XX = sum(sum(Gx[i*w:(i+1)*w, j:j+1]**2 - Gy[i*w:(i+1)*w, j:j+1]**2))
            mOrientaciones[i, j] = (0.5 * math.atan2(YY, XX) + math.pi / 2.0) * (180.0 / math.pi)
    return mOrientaciones

def representativo(imarray):
    imarray = np.squeeze(imarray)
    m, n = imarray.shape
    patron = imarray[1:m-1, 1:n-1]
    EE = orientacion(patron, 14)
    return np.asarray(EE).reshape(-1)

app = FastAPI()

som, MM = load_model()

@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
    try:
        contents = await file.read()
        image = Image.open(BytesIO(contents)).convert('L')
        image = np.asarray(image)
        if image.shape != (256, 256):
            raise ValueError("La imagen debe ser de tamaño 256x256.")
        image = image.reshape(256, 256, 1)
        print(f"Imagen convertida a matriz: {image.shape}")
        
        representative_data = representativo(image)
        print(f"Datos representativos de la imagen: {representative_data.shape}")
        representative_data = representative_data.reshape(1, -1)
        
        w = som.winner(representative_data)
        print(f"Índice ganador del SOM: {w}")
        prediction = MM[w]
        print(f"Predicción: {prediction}")
        
        return {"prediction": prediction}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))