import numpy as np
import pandas as pd

data= pd.read_csv("heart.csv")
data.info()



from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Define features and target
X = data.drop('target', axis=1)
y = data['target']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

from keras.models import Sequential
from keras.layers import Dense

# Define the FNN model
model = Sequential()
model.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=10, validation_split=0.2)

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve, auc, roc_auc_score


log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
y_pred = log_reg.predict(X_test)

from sklearn.tree import DecisionTreeClassifier  

# Decision Tree
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)

from sklearn.ensemble import RandomForestClassifier

# Random Forest
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)

from sklearn.svm import SVC

# Support Vector Machine
svm = SVC(probability=True)
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)

from xgboost import XGBClassifier
import xgboost as xgb

# XGBoost
xgboost = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
xgboost.fit(X_train, y_train)
y_pred = xgboost.predict(X_test)

import gradio as gr

def predict(input_age, input_sex, input_cp, input_trestbps, input_chol, input_fbs,
             input_restecg, input_thalach, input_exang, input_oldpeak, input_slope,
             input_ca, input_thal):

    # Input validation (add checks for other inputs as needed)
    if not (0 <= input_age <= 120):
        return {"error": "Age must be between 0 and 120."}

    # Prepare input data
    input_data = [[input_age, input_sex, input_cp, input_trestbps, input_chol,
                   input_fbs, input_restecg, input_thalach, input_exang,
                   input_oldpeak, input_slope, input_ca, input_thal]]

    input_scaled = scaler.transform(input_data)

    # Predictions
    try:
        predictions = {
            'Logistic Regression': log_reg.predict(input_scaled)[0],
            'Random Forest': rf.predict(input_scaled)[0],
            'Decision Tree': dt.predict(input_scaled)[0],
            'SVM': svm.predict(input_scaled)[0],
            'XGBoost': xgboost.predict(input_scaled)[0],
            'FNN': (model.predict(input_scaled) > 0.5).astype("int32")[0][0]
        }

        for key in predictions:
            predictions[key] = 'Heart Disease' if predictions[key] == 1 else 'No Heart Disease'

        return predictions
    except Exception as e:
        return {"error": str(e)}

# Create Gradio interface
input_components = [
    gr.Number(label='Age'),
    gr.Number(label='Sex'),
    gr.Number(label='Chest Pain Type (cp)'),
    gr.Number(label='Resting Blood Pressure (trestbps)'),
    gr.Number(label='Cholesterol Level (chol)'),
    gr.Number(label='Fasting Blood Sugar (fbs)'),
    gr.Number(label='Resting Electrocardiographic Results (restecg)'),
    gr.Number(label='Maximum Heart Rate Achieved (thalach)'),
    gr.Number(label='Exercise Induced Angina (exang)'),
    gr.Number(label='Old Peak'),
    gr.Number(label='Slope of the Peak Exercise ST Segment (slope)'),
    gr.Number(label='Number of Major Vessels Colored by Fluoroscopy (ca)'),
    gr.Number(label='Thalassemia (thal)'),
]

# Launch the Gradio interface
gr.Interface(fn=predict,
             inputs=input_components,
             outputs='json',
             title='Heart Disease Prediction',
             description='Enter the features to predict the presence of heart disease.').launch()