app.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
import streamlit as st

def process_and_evaluate(file):
    # Load the dataset
    df = pd.read_csv(file)

    # Encode categorical features
    categorical_columns = df.select_dtypes(include=['object']).columns
    label_encoders = {}
    for col in categorical_columns:
        le = LabelEncoder()
        df[col] = le.fit_transform(df[col])
        label_encoders[col] = le

    # Define the target and features
    target = 'target'  # Assuming the target column is named 'target'
    X = df.drop(columns=[target])
    y = df[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)

    # Train a RandomForestClassifier
    clf = RandomForestClassifier(random_state=42)
    clf.fit(X_train, y_train)

    # Predict on the test set
    y_pred = clf.predict(X_test)

    # Compute the confusion matrix
    conf_matrix = confusion_matrix(y_test, y_pred)

    # Generate the classification report
    classification_rep = classification_report(y_test, y_pred)

    return df, conf_matrix, classification_rep

# Streamlit interface
st.title("Heart Disease Prediction")
st.write("Upload a CSV file containing heart disease data to get the classification report.")

uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file is not None:
    df, conf_matrix, report = process_and_evaluate(uploaded_file)

    # Display the classification report
    st.subheader("Classification Report")
    st.text(report)

    # Plot the correlation matrix
    st.subheader("Correlation Matrix")
    corr_matrix = df.corr()
    fig, ax = plt.subplots(figsize=(10, 8))
    sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", ax=ax)
    st.pyplot(fig)

    # Plot the confusion matrix
    st.subheader("Confusion Matrix")
    fig, ax = plt.subplots()
    sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", ax=ax)
    ax.set_xlabel('Predicted')
    ax.set_ylabel('Actual')
    st.pyplot(fig)