--- a
+++ b/Tabs/predict.py
@@ -0,0 +1,75 @@
+"""This modules contains data about prediction page"""
+
+# Import necessary modules
+import streamlit as st
+
+# Import necessary functions from web_functions
+from web_functions import predict
+
+
+def app(df, X, y):
+    """This function create the prediction page"""
+
+    # Add title to the page
+    st.title("Prediction Page")
+
+    # Add a brief description
+    st.markdown(
+        """
+            <p style="font-size:25px">
+                This app uses <b style="color:green">Random Forest Classifier</b> for the Prediction of Parkinson's disease.
+            </p>
+        """, unsafe_allow_html=True)
+    with st.expander("View attribute details"):
+        st.markdown("""MDVP:Fo(Hz) - Average vocal fundamental frequency\n
+MDVP:Fhi(Hz) - Maximum vocal fundamental frequency\n
+MDVP:Flo(Hz) - Minimum vocal fundamental frequency\n
+MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several
+measures of variation in fundamental frequency\n
+MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude\n
+NHR,HNR - Two measures of ratio of noise to tonal components in the voice\n
+status - Health status of the subject (one) - Parkinson's, (zero) - healthy\n
+RPDE,D2 - Two nonlinear dynamical complexity measures\n
+DFA - Signal fractal scaling exponent\n
+spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation""")
+    # Take feature input from the user
+    # Add a subheader
+    st.subheader("Select Values:")
+
+    # Take input of features from the user.
+    avff = st.slider("Average vocal fundamental frequency", int(df["AVFF"].min()), int(df["AVFF"].max()))
+    mavff = st.slider("Maximum vocal fundamental frequency", int(df["MAVFF"].min()), int(df["MAVFF"].max()))
+    mivff = st.slider("Minimum vocal fundamental frequency", int(df["MIVFF"].min()), int(df["MIVFF"].max()))
+    jitddp = st.slider("Jitter:DDP", float(df["Jitter:DDP"].min()), float(df["Jitter:DDP"].max()))
+    mdvpjit = st.slider("Multidimensional Voice Program:Jitter(%)", float(df["MDVP:Jitter(%)"].min()), float(df["MDVP:Jitter(%)"].max()))
+    mdvprap = st.slider("MDVP:RAP", float(df["MDVP:RAP"].min()), float(df["MDVP:RAP"].max()))
+    mdvpapq = st.slider("MDVP:APQ", float(df["MDVP:APQ"].min()), float(df["MDVP:APQ"].max()))
+    mdvpppq = st.slider("MDVP:PPQ", float(df["MDVP:PPQ"].min()), float(df["MDVP:PPQ"].max()))
+    mdvpshim = st.slider("MDVP:Shimmer", float(df["MDVP:Shimmer"].min()), float(df["MDVP:Shimmer"].max()))
+    shimdda = st.slider("Shimmer:DDA", float(df["Shimmer:DDA"].min()), float(df["Shimmer:DDA"].max()))
+    shimapq3 = st.slider("Shimmer:APQ3", float(df["Shimmer:APQ3"].min()), float(df["Shimmer:APQ3"].max()))
+    shimapq5 = st.slider("Shimmer:APQ5", float(df["Shimmer:APQ5"].min()), float(df["Shimmer:APQ5"].max()))
+    nhr = st.slider("NHR", float(df["NHR"].min()), float(df["NHR"].max()))
+    hnr = st.slider("HNR", float(df["HNR"].min()), float(df["HNR"].max()))
+    rpde = st.slider("RPDE", float(df["RPDE"].min()), float(df["RPDE"].max()))
+    dfa = st.slider("DFA", float(df["DFA"].min()), float(df["DFA"].max()))
+    d2 = st.slider("D2", float(df["D2"].min()), float(df["D2"].max()))
+    ppe = st.slider("PPE", float(df["PPE"].min()), float(df["PPE"].max()))
+
+    # Create a list to store all the features
+    features = [avff, mavff, mivff, jitddp, mdvpjit, mdvprap,mdvpapq,mdvpppq,mdvpshim,shimdda,shimapq3,shimapq5,nhr,hnr,rpde,dfa,d2,ppe]
+
+    # Create a button to predict
+    if st.button("Predict"):
+        # Get prediction and model score
+        prediction, score = predict(X, y, features)
+        st.success("Predicted Sucessfully")
+
+        # Print the output according to the prediction
+        if (prediction == 1):
+            st.warning("The person either has Parkison's disease or prone to get Parkinson's disease")
+        else:
+            st.info("The person is safe from Parkinson's disease")
+
+        # Print teh score of the model 
+        st.write("The model used is trusted by doctor and has an accuracy of ", (score*100),"%")