app.py

#!/usr/bin/env python
# coding: utf-8

# In[3]:


import streamlit as st
import urllib.request
import json

# Streamlit app title
st.title("Stroke Prediction")

# Define input fields
st.sidebar.header("Input Parameters")
gender = st.sidebar.selectbox("Gender", ["Male", "Female"])
age = st.sidebar.slider("Age", 0, 100, 30)
hypertension = st.sidebar.checkbox("Hypertension")
heart_disease = st.sidebar.checkbox("Heart Disease")
ever_married = st.sidebar.checkbox("Ever Married")
work_type = st.sidebar.selectbox("Work Type", ["Private", "Self-employed", "Govt_job", "Children", "Never_worked"])
residence_type = st.sidebar.selectbox("Residence Type", ["Urban", "Rural"])
avg_glucose_level = st.sidebar.slider("Average Glucose Level", 0.0, 300.0, 100.0)
bmi = st.sidebar.number_input("BMI", 0.0, 100.0, 25.0)
smoking_status = st.sidebar.selectbox("Smoking Status", ["Smokes", "Never Smoked", "Unknown"])

# Create a button to trigger prediction
if st.sidebar.button("Predict"):
    # Prepare data
    data = {
        "Inputs": {
            "data": [
                {
                    "id": 0,
                    "gender": gender,
                    "age": age,
                    "hypertension": 1 if hypertension else 0,
                    "heart_disease": 1 if heart_disease else 0,
                    "ever_married": ever_married,
                    "work_type": work_type,
                    "Residence_type": residence_type,
                    "avg_glucose_level": avg_glucose_level,
                    "bmi": bmi,
                    "smoking_status": smoking_status
                }
            ]
        },
        "GlobalParameters": {
            "method": "predict"
        }
    }

    # Convert data to JSON
    data_json = json.dumps(data).encode()

    # Azure ML Model URL
    model_url = 'http://38d9a89f-0a86-4fdb-bf82-50ed33213947.southeastasia.azurecontainer.io/score'

    # Set headers
    headers = {'Content-Type': 'application/json'}

    # Make the HTTP request to the model
    try:
        response = urllib.request.urlopen(urllib.request.Request(model_url, data_json, headers))
        result = response.read()
        st.success(f"Prediction Result: {result}")
    except urllib.error.HTTPError as error:
        st.error(f"Prediction failed with status code: {error.code}")


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