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app.py

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+import pickle
+import streamlit as st
+from streamlit_option_menu import option_menu
+
+
+# loading the saved models
+
+diabetes_model = pickle.load(open("./models/diabetes_model.sav", "rb"))
+
+heart_disease_model = pickle.load(open("./models/heart_disease_model.sav", "rb"))
+
+parkinsons_model = pickle.load(open("./models/parkinsons_model.sav", "rb"))
+
+
+# sidebar for navigation
+with st.sidebar:
+    selected = option_menu(
+        "Multiple Disease Prediction System",
+        ["Diabetes Prediction", "Heart Disease Prediction", "Parkinsons Prediction"],
+        icons=["activity", "heart", "person"],
+        default_index=0,
+    )
+
+
+# Diabetes Prediction Page
+if selected == "Diabetes Prediction":
+    # page title
+    st.title("Diabetes Prediction using ML")
+
+    # getting the input data from the user
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        Pregnancies = st.text_input("Number of Pregnancies")
+
+    with col2:
+        Glucose = st.text_input("Glucose Level")
+
+    with col3:
+        BloodPressure = st.text_input("Blood Pressure value")
+
+    with col1:
+        SkinThickness = st.text_input("Skin Thickness value")
+
+    with col2:
+        Insulin = st.text_input("Insulin Level")
+
+    with col3:
+        BMI = st.text_input("BMI value")
+
+    with col1:
+        DiabetesPedigreeFunction = st.text_input("Diabetes Pedigree Function value")
+
+    with col2:
+        Age = st.text_input("Age of the Person")
+
+    # code for Prediction
+    diab_diagnosis = ""
+
+    # creating a button for Prediction
+
+    if st.button("Diabetes Test Result"):
+        diab_prediction = diabetes_model.predict(
+            [
+                [
+                    Pregnancies,
+                    Glucose,
+                    BloodPressure,
+                    SkinThickness,
+                    Insulin,
+                    BMI,
+                    DiabetesPedigreeFunction,
+                    Age,
+                ]
+            ]
+        )
+
+        if diab_prediction[0] == 1:
+            diab_diagnosis = "The person is diabetic"
+        else:
+            diab_diagnosis = "The person is not diabetic"
+
+    st.success(diab_diagnosis)
+
+
+# Heart Disease Prediction Page
+if selected == "Heart Disease Prediction":
+    # page title
+    st.title("Heart Disease Prediction using ML")
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        age = st.text_input("Age")
+
+    with col2:
+        sex = st.text_input("Sex")
+
+    with col3:
+        cp = st.text_input("Chest Pain types")
+
+    with col1:
+        trestbps = st.text_input("Resting Blood Pressure")
+
+    with col2:
+        chol = st.text_input("Serum Cholestoral in mg/dl")
+
+    with col3:
+        fbs = st.text_input("Fasting Blood Sugar > 120 mg/dl")
+
+    with col1:
+        restecg = st.text_input("Resting Electrocardiographic results")
+
+    with col2:
+        thalach = st.text_input("Maximum Heart Rate achieved")
+
+    with col3:
+        exang = st.text_input("Exercise Induced Angina")
+
+    with col1:
+        oldpeak = st.text_input("ST depression induced by exercise")
+
+    with col2:
+        slope = st.text_input("Slope of the peak exercise ST segment")
+
+    with col3:
+        ca = st.text_input("Major vessels colored by flourosopy")
+
+    with col1:
+        thal = st.text_input(
+            "thal: 0 = normal; 1 = fixed defect; 2 = reversable defect"
+        )
+
+    # code for Prediction
+    heart_diagnosis = ""
+
+    # creating a button for Prediction
+
+    if st.button("Heart Disease Test Result"):
+        heart_prediction = heart_disease_model.predict(
+            [
+                [
+                    age,
+                    sex,
+                    cp,
+                    trestbps,
+                    chol,
+                    fbs,
+                    restecg,
+                    thalach,
+                    exang,
+                    oldpeak,
+                    slope,
+                    ca,
+                    thal,
+                ]
+            ]
+        )
+
+        if heart_prediction[0] == 1:
+            heart_diagnosis = "The person is having heart disease"
+        else:
+            heart_diagnosis = "The person does not have any heart disease"
+
+    st.success(heart_diagnosis)
+
+
+# Parkinson's Prediction Page
+if selected == "Parkinsons Prediction":
+    # page title
+    st.title("Parkinson's Disease Prediction using ML")
+
+    col1, col2, col3, col4, col5 = st.columns(5)
+
+    with col1:
+        fo = st.text_input("MDVP:Fo(Hz)")
+
+    with col2:
+        fhi = st.text_input("MDVP:Fhi(Hz)")
+
+    with col3:
+        flo = st.text_input("MDVP:Flo(Hz)")
+
+    with col4:
+        Jitter_percent = st.text_input("MDVP:Jitter(%)")
+
+    with col5:
+        Jitter_Abs = st.text_input("MDVP:Jitter(Abs)")
+
+    with col1:
+        RAP = st.text_input("MDVP:RAP")
+
+    with col2:
+        PPQ = st.text_input("MDVP:PPQ")
+
+    with col3:
+        DDP = st.text_input("Jitter:DDP")
+
+    with col4:
+        Shimmer = st.text_input("MDVP:Shimmer")
+
+    with col5:
+        Shimmer_dB = st.text_input("MDVP:Shimmer(dB)")
+
+    with col1:
+        APQ3 = st.text_input("Shimmer:APQ3")
+
+    with col2:
+        APQ5 = st.text_input("Shimmer:APQ5")
+
+    with col3:
+        APQ = st.text_input("MDVP:APQ")
+
+    with col4:
+        DDA = st.text_input("Shimmer:DDA")
+
+    with col5:
+        NHR = st.text_input("NHR")
+
+    with col1:
+        HNR = st.text_input("HNR")
+
+    with col2:
+        RPDE = st.text_input("RPDE")
+
+    with col3:
+        DFA = st.text_input("DFA")
+
+    with col4:
+        spread1 = st.text_input("spread1")
+
+    with col5:
+        spread2 = st.text_input("spread2")
+
+    with col1:
+        D2 = st.text_input("D2")
+
+    with col2:
+        PPE = st.text_input("PPE")
+
+    # code for Prediction
+    parkinsons_diagnosis = ""
+
+    # creating a button for Prediction
+    if st.button("Parkinson's Test Result"):
+        parkinsons_prediction = parkinsons_model.predict(
+            [
+                [
+                    fo,
+                    fhi,
+                    flo,
+                    Jitter_percent,
+                    Jitter_Abs,
+                    RAP,
+                    PPQ,
+                    DDP,
+                    Shimmer,
+                    Shimmer_dB,
+                    APQ3,
+                    APQ5,
+                    APQ,
+                    DDA,
+                    NHR,
+                    HNR,
+                    RPDE,
+                    DFA,
+                    spread1,
+                    spread2,
+                    D2,
+                    PPE,
+                ]
+            ]
+        )
+
+        if parkinsons_prediction[0] == 1:
+            parkinsons_diagnosis = "The person has Parkinson's disease"
+        else:
+            parkinsons_diagnosis = "The person does not have Parkinson's disease"
+
+    st.success(parkinsons_diagnosis)

requirements.txt

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+streamlit
+streamlit_option_menu
+scikit-learn==1.0.2