Upload predict.py

predict.py

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+import streamlit as st
+import pandas as pd
+import requests
+import os
+from io import StringIO
+import datetime
+
+# Set page configuration
+st.set_page_config(page_title="Predict", page_icon="🔮",  layout="wide")
+
+
+st.title("Predict Sepsis")
+
+
+def select_model():
+    col1, col2 = st.columns(2)
+    with col1:
+        choice = st.selectbox('Select a model', options=['xgboost', 'random_forest'], key='select_model')
+    with col2:
+        pass
+
+    # if st.session_state['selected_model'] == 'Xgboost':
+    #     pipeline = xgboost_pipeline()
+    # else:
+    #     pipeline = load_random_forest_pipeline()
+
+    # encoder = joblib.load('models/encoder.joblib')
+    return choice
+
+
+def make_prediction(): 
+    selected_model = st.session_state['select_model']
+    age = st.session_state['age']
+    insurance = 1 if st.session_state['insurance'] == 'Yes' else 0
+    m11 = st.session_state['m11']
+    pr = st.session_state['pr']
+    prg = st.session_state['prg']
+    ts = st.session_state['ts']
+    pl = st.session_state['pl']
+    sk = st.session_state['sk']
+    bd2 = st.session_state['bd2']
+
+    base_url = 'https://gabcares-team-curium.hf.space/'
+    url =  base_url + f"{ 'xgboost_prediction' if selected_model=='xgboost' else 'random_forest_prediction'}"
+
+
+    data = {'PRG': prg, 'PL': pl, 'PR': pr,'SK': sk, 'TS': ts, 'M11': m11, 'BD2': bd2, 'Age': age, 'Insurance': insurance}
+    # Send POST request with JSON data using the json parameter
+    response_status = requests.get(base_url)
+
+    if (response_status.status_code == 200):
+        response = requests.post(url, json=data,timeout=30)
+        pred_prob = (response.json()['result'])
+        prediction = pred_prob['prediction']
+        probability = pred_prob['probability']
+
+        st.session_state['prediction'] = prediction
+        st.session_state['probability'] = probability
+    else:
+        st.write('Unable to connect to the server.')
+
+
+# Creating the form
+def display_form():
+    select_model()
+
+    with st.form('input_features'):
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.write('### Patient Demographics')
+            age = st.number_input('Age', min_value=0, max_value=100, step=1, key = 'age')
+            insurance = st.selectbox('Insurance', options = ['Yes', 'No'], key = 'insurance')
+
+            st.write('### Vital Signs')
+            m11 = st.number_input('BMI', min_value=10.0, format="%.2f",step = 1.00, key = 'm11')
+            pr = st.number_input('Blood Pressure', min_value=10.0, format="%.2f",step = 1.00, key = 'pr')
+            prg = st.number_input('PRG(plasma glucose)', min_value=10.0, format="%.2f",step = 1.00, key = 'prg')
+
+        with col2:
+            st.write('### Blood Work')
+            pl = st.number_input('PL(Blood Work Result 1)', min_value=10.0, format="%.2f",step = 1.00, key = 'pl')
+            sk = st.number_input('SK(Blood Work Result 2)', min_value=10.0, format="%.2f",step = 1.00, key = 'sk')
+            ts = st.number_input('TS(Blood Work Result 3)', min_value=10.0, format="%.2f",step = 1.00, key = 'ts')
+            bd2 = st.number_input('BD2(Blood Work Result 4)', min_value=10.0, format="%.2f",step = 1.00, key = 'bd2')
+        
+        st.form_submit_button('Submit', on_click=make_prediction)
+    
+
+if __name__ == '__main__':
+
+    display_form()
+
+    final_prediction = st.session_state.get('prediction')
+    final_probability = st.session_state.get('probability')
+
+    if final_prediction is None:
+        st.write('Predictions show here!')
+        st.divider()
+    else:
+        if final_prediction.lower() == 'positive':
+            st.markdown(f'### Patient is likely to develop sepsis😞.')
+            st.markdown(f'## Probability: {final_probability:.2f}%')
+            
+        else:
+            # st.markdown(f'## Sepsis: {final_prediction}')
+            st.markdown(f'### Patient is unlikely to develop sepsis😊.')
+            st.markdown(f'## Probability: {final_probability:.2f}%')