Create app.py

app.py

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+import streamlit as st
+import os
+import requests
+import pandas as pd
+import json
+
+# Function to create the payload for the model
+def create_tf_serving_json(data):
+    return {'inputs': {name: data[name].tolist() for name in data.keys()} if isinstance(data, dict) else data.tolist()}
+
+# Function to send a request to the model endpoint
+def score_model(dataset):
+    url = 'https://adb-3810412827421523.3.azuredatabricks.net/serving-endpoints/churnpredictionmodel0323/invocations'
+    headers = {'Authorization': f'Bearer {os.environ.get("dapi0c3745fa836d2634501e12bde7463bb1-2")}', 'Content-Type': 'application/json'}
+    ds_dict = {'dataframe_split': dataset.to_dict(orient='split')} if isinstance(dataset, pd.DataFrame) else create_tf_serving_json(dataset)
+    data_json = json.dumps(ds_dict, allow_nan=True)
+    response = requests.request(method='POST', headers=headers, url=url, data=data_json)
+    if response.status_code != 200:
+        raise Exception(f'Request failed with status {response.status_code}, {response.text}')
+    return response.json()
+
+# Streamlit app UI
+st.title('Employee Churn Prediction')
+
+# Create a form
+with st.form(key='churn_form'):
+    satisfaction_level = st.slider('Satisfaction Level', 0.0, 1.0, 0.5)
+    last_evaluation = st.slider('Last Evaluation', 0.0, 1.0, 0.5)
+    number_project = st.slider('Number of Projects', 1, 10, 3)
+    average_montly_hours = st.slider('Average Monthly Hours', 50, 350, 200)
+    time_spend_company = st.slider('Time Spent in Company (years)', 1, 10, 3)
+    work_accident = st.selectbox('Work Accident', [0, 1])
+    promotion_last_5years = st.selectbox('Promotion in Last 5 Years', [0, 1])
+    salary = st.selectbox('Salary Level', ['Low', 'Medium', 'High'])
+
+    # Encode salary into one-hot vectors
+    salaryVec_0, salaryVec_1 = 0.0, 0.0
+    if salary == 'Low':
+        salaryVec_0 = 1.0
+    elif salary == 'Medium':
+        salaryVec_1 = 1.0
+
+    # Submit button
+    submit_button = st.form_submit_button(label='Predict Churn')
+
+# Handle form submission
+if submit_button:
+    # Create a DataFrame with the input data
+    input_data = pd.DataFrame({
+        'satisfaction_level': [satisfaction_level],
+        'last_evaluation': [last_evaluation],
+        'number_project': [number_project],
+        'average_montly_hours': [average_montly_hours],
+        'time_spend_company': [time_spend_company],
+        'Work_accident': [work_accident],
+        'promotion_last_5years': [promotion_last_5years],
+        'salaryVec_0': [salaryVec_0],
+        'salaryVec_1': [salaryVec_1]
+    })
+
+    # Get prediction from the model
+    try:
+        prediction = score_model(input_data)
+        churn_prediction = prediction['predictions'][0]
+        if churn_prediction == 1:
+            st.write('The employee is likely to churn.')
+        else:
+            st.write('The employee is not likely to churn.')
+    except Exception as e:
+        st.error(f'Error: {e}')