#%%
import os
from datetime import time

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_validate, StratifiedKFold, train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
import pandas as pd
import numpy as np
import socket
import json
import pandas as pd
random_state = 19
splits = 5
estimators = 100
#%% md
# # Socket que funciona*
#%% md
# # Data Preparation
#%%
dataset = pd.read_csv(os.path.join("csv", 'openbci.csv'))

columnas_utiles = [f"EXG Channel {i}" for i in range(16)] + ["Movement"]
dataset = dataset[columnas_utiles]

# Split the dataset
X = dataset.iloc[:, :-1].values
# Guardar nombres de columnas usadas en el modelo
columnas_modelo = dataset.columns[:-1].tolist()
print("✔️ Columnas usadas en el modelo:", columnas_modelo)

y = dataset.iloc[:,-1].values
# Show the characteristics of the dataset
print(f"X shape: {X.shape}")
print(f"y shape: {y.shape}")
print("Labels distribution")
print("===================")
print(dataset.Movement.value_counts(normalize=True)*100)

#%% md
# # Stratified Cross Validation
#%%
import joblib
clf = RandomForestClassifier(n_estimators=estimators, n_jobs=-1, random_state=random_state)
cv = StratifiedKFold(n_splits=splits, shuffle=True, random_state=random_state)
scores = cross_validate(clf, X, y, scoring="accuracy", cv=cv, n_jobs=-1, return_train_score=True)
print(f"Accuracy: {np.mean(scores['test_score'])} (+/- {np.std(scores['test_score'])})")
clf.fit(X, y)
joblib.dump(clf, 'modelo_entrenado.pkl')
#%% md
# # Live Classification
#%%
import socket
import json
import numpy as np
import joblib
import csv
import os
from datetime import datetime

clf = joblib.load('modelo_entrenado.pkl')

# Variables globales
IP = "127.0.0.1"
PORT = 12345

# Crear socket UDP
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind((IP, PORT))
print(f"Esperando datos en {IP}:{PORT}...")

# Crear nombre único para el archivo de salida
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_file = f"samples_con_predicciones_{timestamp}.csv"

while True:
    #data, _ = sock.recvfrom(65535)
    try:
        mensaje = json.loads(data.decode("utf-8"))
        datos_crudos = []
        for linea in mensaje:
            datos_crudos.append(linea["data"])

        datos_crudos = mensaje["data"]

        # Convertir a numpy y verificar dimensiones
        datos_np = np.array(datos_crudos)
        if datos_np.shape[0] != 16:
            print(f"Se esperaban {16} canales, se recibieron {datos_np.shape[0]}")
            continue

        # [
        # [ 1, 2, 3, 4 .... 16],
        # [ 182,3,34,4,.... 21],
        # ...
        # [sdafl asdfklalsdkfjd]
        # Vamos a hacer un dataset con las 5 muestras que envía el sensor
        samples = []
        for i in range(5):
            samples.append([datos_np[j][i] for j in range(16)])

        # Clasificamos por mayoría
        pred = clf.predict(samples)
        # [ i, d, i, i, none]
        keys, values = np.unique(pred, return_counts=True)
        res = keys[np.argmax(values)]
        rep = max(values)
        print(f"Predicción: {res} with a probability of {rep/5*100}%")

    except Exception as e:
        print("Error procesando mensaje:", e)

#%%
# import numpy as np
#
# a= [[11284.547,12447.82,12083.107,11501.806,12807.84],[-12317.487,-11649.863,-12289.168,-11944.437,-11870.162],[-44142.168,-43465.47,-44276.66,-43668.535,-43838.836],[-48939.258,-49422.3,-49312.082,-49024.46,-49621.812],[-46515.21,-42789.35,-45057.406,-45182.152,-42718.074],[-50600.24,-49273.01,-48712.496,-50988.35,-47901.44],[-55553.695,-54737.543,-55705.754,-54981.83,-55189.562],[-47437.688,-47808.816,-47803.074,-47444.28,-48032.02],[-32000.01,-31640.191,-32217.111,-31654.541,-31996.723],[-34022.508,-33585.953,-34134.49,-33706.766,-33866.96],[-52641.195,-52273.914,-52842.473,-52302.656,-52619.94],[-37791.434,-37473.55,-38074.094,-37444.223,-37881.824],[-50217.285,-49953.332,-50541.453,-49866.676,-50376.137],[-54649.746,-54273.344,-54846.934,-54319.88,-54617.36],[-36739.273,-36184.703,-36606.793,-36466.848,-36266.13],[-63696.46,-67149.516,-62522.164,-66528.56,-64694.105]]
# samples = []
# for i in range(5):
#     samples.append([a[j][i] for j in range(16)])
# print(np.array(samples))
# a = np.array(["i", "i", "d", "d", "d", "none"]).reshape(-1, 1)
# print(a)
# c, k = np.unique(a, return_counts=True)
# print(c, k)
# #%% md
# # # Classification Report
# #%%
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=random_state, stratify=y)
# clf.fit(X_train, y_train)
#
# y_pred = clf.predict(X_test)
# print(classification_report(y_test, y_pred, digits=7))
# #%% md
# # # Confusion Matrix
# #%%
# cm = confusion_matrix(y_test, y_pred)
# disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)
# disp.plot()
# #%%
# import socket
# import datetime
#
# # 📡 Configuración del Servidor UDP para recibir datos de OpenBCI
# UDP_IP = "127.0.0.1"  # Dirección donde OpenBCI está enviando los datos
# UDP_PORT = 12345  # Puerto configurado en OpenBCI GUI
# BUFFER_SIZE = 4096  # Asegurar espacio suficiente para recibir datos
#
# # 🟢 Crear socket UDP
# sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
# sock.bind((UDP_IP, UDP_PORT))
#
# print(f"🔄 Servidor UDP en {UDP_IP}:{UDP_PORT}, esperando datos de OpenBCI...")
#
# while True:
#     try:
#         # 🟡 Recibir datos UDP desde OpenBCI
#         data, addr = sock.recvfrom(BUFFER_SIZE)
#         decoded_data = data.decode("utf-8").strip()
#
#         # 🟠 Mostrar en consola los datos recibidos
#         print(f"{decoded_data} Time: {datetime.datetime.now()}")
#
#     except socket.timeout:
#         print("⏳ No hay datos recibidos aún... (Verifica la configuración de OpenBCI)")
#
#     except Exception as e:
#         print(f"❌ Error al recibir datos: {e}")