app.py

import streamlit as st
import pandas as pd
import numpy as np
import pickle

# Load the saved model
with open('rf_model.pkl', 'rb') as file:
    rf_model = pickle.load(file)

# Create the Streamlit app
st.title("Parkinson's Disease Prediction")

# Collect user input
col = ['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE']

input_data = {}
for feature in col:
    input_data[feature] = st.number_input(f"Enter {feature}", value=0.0)

# Make the prediction
if st.button("Predict"):
    input_array = np.array(list(input_data.values())).reshape(1, -1)
    prediction = rf_model.predict(input_array)

    # Display the results
    if prediction[0] == 1:
        st.error("Parkinson's Disease detected")
    else:
        st.success("No Parkinson's Disease")