Streamlit structure for work

app.py

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+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")