main.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import GUI

data = pd.read_csv('Database/Training.csv')
df = pd.DataFrame(data)

cols = df.columns[:-1]

ll = list(cols)

pp = []

for i in ll:
    pp.append(str(i))

x = df[cols]  # x is the feature
y = df['prognosis']  # y is the target

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

features = cols
feature_dict = {}

latest_features = list(features).copy()

for i, f in enumerate(features):
    feature_dict[f] = i

trix = dict()

for item in latest_features:
    trix[str(item).title().replace('_', ' ')] = item


def prediction():
    # symptoms = ['joint_pain', 'muscle_wasting']
    symptoms = [GUI.p.get(), GUI.en.get(), GUI.bb.get(), GUI.ee.get(), GUI.hh.get()]

    symptoms = [trix[j] for j in symptoms if j != '']

    hack_set = set()

    pos = []

    for i in range(len(symptoms)):
        pos.append(feature_dict[symptoms[i]])

    sample_x = [1.0 if i in pos else 0.0 for i in range(len(features))]
    sample_x = [sample_x]

    # Decision Tree

    dt = DecisionTreeClassifier()

    dt.fit(x_train, y_train)

    # print(dt.predict(sample_x))
    print(f"Decision Tree: {dt.predict(sample_x)}")

    """y_pred = dt.predict(x_test)

    accuracy = accuracy_score(y_test, y_pred)

    print(f"Accuracy Decision Tree: {accuracy * 100}%")"""

    # Naive Bayes

    naive = GaussianNB()

    naive.fit(x_train, y_train)

    # print(f"Naive Bayes: {naive.predict(sample_x)}")

    hack_set.add(*map(str, naive.predict(sample_x)))

    """y_pred = naive.predict(x_test)

    accuracy_naive = accuracy_score(y_test, y_pred) * 100

    print(f"Accuracy for Naive Bayes: {accuracy_naive}%")"""

    # Random Forest

    random = RandomForestClassifier()

    random.fit(x_train, y_train)

    hack_set.add(*map(str, random.predict(sample_x)))

    #print(f"Random Forest: {random.predict(sample_x)}")

    """y_pred = random.predict(x_test)

    accuracy_random = accuracy_score(y_test, y_pred) * 100

    print(f"Accuracy for Random Forest: {accuracy_random}%")"""

    # LogisticRegression
    """Logic = LogisticRegression()

    Logic.fit(x_train, y_train)

    # hack_set.add(dis.get(*map(str, Logic.predict(sample_x))))

    print(f'LogisticRegression: {Logic.predict(sample_x)}')

    y_pred = Logic.predict(x_test)
    accuracy = accuracy_score(y_test, y_pred)

    print(f"Accuracy for Logistic Regression: {accuracy * 100}%")"""

    # SVM

    """Svm = svm.SVC()

    Svm.fit(x_train, y_train)

    y_pred = Svm.predict(x_test)

    print(f"SVM: {Svm.predict(sample_x)}")

    accuracy = accuracy_score(y_test, y_pred) * 100

    print(f"Accuracy for SVM: {accuracy}%")"""

    magic = list(hack_set)

    s = ""
    if len(hack_set) == 1:
        s = s + "".join(magic[0])
    else:
        s = s + "".join(magic[0]) + ' or ' + "".join(magic[1])
    # Exceptions for Wrong Try
    if not symptoms:
        GUI.final_result.delete(0, GUI.END)
        GUI.final_result.insert(0, "Invalid ! No Disease Found")

    elif len(set(symptoms)) != len(symptoms):
        GUI.final_result.delete(0, GUI.END)
        GUI.final_result.insert(0, "Invalid ! Try with unique Symptoms")
    else:
        GUI.final_result.delete(0, GUI.END)
        GUI.final_result.insert(0, s)