text: import numpy as np
  import pandas as pd
  from sklearn import preprocessing
  from sklearn import model_selection
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score
  import pickle
  import streamlit as st 
  import time
  
  pickle_in = open("predictor.pkl","rb")
  predictor=pickle.load(pickle_in)
  
  def welcome():
      return "Welcome All"
  
  def diabetes_prediction(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,Bmi,Age):
      
      """Let's Predict 
      This is using docstrings for specifications.
      ---
      parameters:  
        - name: Pregnancies
          in: query
          type: number
          required: true
        - name: Glucose
          in: query
          type: number
          required: true
        - name: BloodPressure
          in: query
          type: number
          required: true
        - name: SkinThickness
          in: query
          type: number
          required: true
        - name: Insulin
          in: query
          type: number
          required: true
        - name: Bmi
          in: query
          type: number
          required: true   
        - name: Age
          in: query
          type: number
          required: true
      responses:
          200:
              description: The output values
          
      """
     
      prediction=predictor.predict([[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,Bmi,Age]])
      print(prediction)
      return prediction
  
  def main():
      #st.title("Diabetes Predictor")
      html_temp = """
      <div style="background-color:tomato;padding:10px">
      <h2 style="color:white;text-align:center;">Diabetes Prediction App </h2>
      </div>
      """
      
      #Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	 Age
      st.markdown(html_temp,unsafe_allow_html=True)
  
      Pregnancies = st.text_input("Number of Pregnancies","Type Here")
      Glucose = st.text_input("Glucose","Type Here")
      BloodPressure = st.text_input("Blood Pressure","Type Here")
      SkinThickness = st.text_input("Skin Thickness","Type Here")
      Insulin = st.text_input("Insulin","Type Here")
      Bmi = st.text_input("Bmi","Type Here")
      Age = st.text_input("Age","Type Here")
      result=""
      if st.button("Predict"):
          result=diabetes_prediction(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,Bmi,Age)
          
          if (result==1):
              pos = "You are at high risk of diabetes. Please visit your nearest hospital for more information on managing the disease."
              st.success('{}'.format(pos))
  
          else:
              neg = "Your results look good. You are healthy and not at risk for diabetes :)"
              st.success('{}'.format(neg))
  
      with st.spinner('Almost done...'):
        time.sleep(2)
        st.success('Done!,Thank you for using this app!')
  
  
      st.info("Caution: This is just a prediction and not doctoral advice. Kindly see a doctor if you feel the symptoms persist.")
  
      if st.button("About"):
          st.text("Lets LEarn")
          st.text("Built with Streamlit")
  
  st.sidebar.subheader("About App")
  
  st.sidebar.info(
      "This web app uses some powerful Machine-Learning techniques and  helps you to find out whether you are at a risk of diagnosing diabetes.")
  st.sidebar.info(
      "Enter the required fields and click on the 'Predict' button to check whether you have a healthy heart")
  
  
  
  
  
  if __name__=='__main__':
      main()
  