import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import StratifiedKFold
from sklearn import svm
import numpy as np
from sklearn.inspection import permutation_importance
import gradio as gr

df = pd.read_csv('flies.csv')

replacement = {
    'a': 0,
    'x': 1
}

df['Type'] = df['Type'].map(replacement)

cols_to_use = ['Wing Length (cm)', 'Abdomen Length (cm)', 'Antenna Length (cm)', 'Max Antenna Width (cm)']
# cols_to_use = ['Abdomen Length (cm)']

df_use = df[[*cols_to_use, 'Type']]

# Shuffle the dataframe
df_use = shuffle(df_use)
x = df_use.iloc[:,0:len(df_use.columns)-1]
y = df_use.iloc[:, -1]
features = x.columns.values
scaler = MinMaxScaler(feature_range = (0,1))
scaler.fit(x)
x = pd.DataFrame(scaler.transform(x))
x.columns = features
# x_train ,x_test , y_train ,y_test = train_test_split(x, y, train_size= 0.8)
# print(x)

skf = StratifiedKFold(n_splits=4, shuffle=True)
kfold = skf.split(x, y)

confusions = []
accs = []
precisions = []
recalls = []
f1s = []
importances = []
for i, x in enumerate(kfold):
    print(f"\n------------------Fold: {i+1}---------------")

    train, test = df_use.iloc[x[0].tolist()], df_use.iloc[x[1].tolist()]
    ytrain = train[["Type"]]
    print(f"Training: {len(train)}")
    print(f"Testing: {len(test)}")
    ytest = test[["Type"]]

    xtrain = train.drop("Type", axis=1)
    xtest = test.drop("Type", axis=1)

    model = svm.SVC(kernel='poly')
    model.fit(xtrain , np.squeeze(ytrain))
    ypred = model.predict(xtest)


    confusions.append(confusion_matrix(ytest, ypred))
    accs.append(accuracy_score(ytest, ypred))
    precisions.append(precision_score(ytest, ypred))
    recalls.append(recall_score(ytest, ypred))
    f1s.append(f1_score(ytest, ypred))

    

    perm_importance = permutation_importance(model, xtest, ytest)
    features = np.array(cols_to_use)

    sorted_idx = perm_importance.importances_mean.argsort()

    # print(perm_importance.importances_mean[sorted_idx])
    importances.append(perm_importance.importances_mean[sorted_idx])


avg_acc = sum(accs) / len(accs)
avg_precision = sum(precisions) / len(precisions)
avg_recall = sum(recalls) / len(recalls)
avg_f1 = sum(f1s) / len(f1s)

print()
print
print("Accuracy:", avg_acc)
print("Precision:", avg_precision)
print("Recall:", avg_recall)
print("F1:", avg_f1)

matrix = np.asmatrix(np.array(importances))
# print(matrix)
means = matrix.mean(0).A1 # convert back to array


test1 = [2.81, 1.80, 1.24, 0.46]
test2 = [2.65, 1.84, 1.28, 0.39]
test3 = [3.61, 2.04, 1.40, 0.50]

matrix = [
    test1, test2, test3
]



wingL = []
abdL = []
antL = []
antM = []

for row in matrix:
    wingL.append(row[0])
    abdL.append(row[1])
    antL.append(row[2])
    antM.append(row[3])


user_df = pd.DataFrame({
    'Wing Length (cm)': wingL,
    'Abdomen Length (cm)': abdL,
    'Antenna Length (cm)': antL,
     'Max Antenna Width (cm)': antM
})

user_df = scaler.transform(user_df)

preds = model.predict(user_df)

for pred in preds:
  if pred == 0:
    print("A", end = " ")
  else:
    print("X", end = " ")


def main(wingL, abdL, antL, maxAW):
    test1 = [2.81, 1.80, 1.24, 0.46]

    matrix = [
        test1
    ]

    wingL = []
    abdL = []
    antL = []
    antM = []

    for row in matrix:
        wingL.append(row[0])
        abdL.append(row[1])
        antL.append(row[2])
        antM.append(row[3])


    user_df = pd.DataFrame({
        'Wing Length (cm)': wingL,
        'Abdomen Length (cm)': abdL,
        'Antenna Length (cm)': antL,
        'Max Antenna Width (cm)': antM
    })

    user_df = scaler.transform(user_df)

    preds = model.predict(user_df)

    if pred == 0:
        return "A"
    else:
        return "X"

gr.Interface(
  fn=main, 
  title="FlyCatcher",
  inputs=[gr.inputs.Number(label="Wing Length (cm)"), 
          gr.inputs.Number(label="Abdomen Length (cm)"),
          gr.inputs.Number(label="Antenna Length (cm)"),
          gr.inputs.Number(label="Max Antenna Width (cm)"),
  ],
  outputs=["text"],
  theme="huggingface").launch(share=True)