update app

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import numpy as np
+import pickle
+from utils.prediction import predict_drug
+
+
+# Loading the model
+filename = "./model/drug_pipeline.sav"
+pipe = pickle.load(open(filename, 'rb'))
+
+
+# Web interface section
+st.title("Drug Classifier")
+
+# Singular prediction
+with st.sidebar.expander("Single Prediction"):
+    with st.form(key="drug_form", clear_on_submit=True):
+        st.title("Drug Information Form")
+        age = st.number_input(label="Age", min_value=15, step=1, max_value=74)
+
+        gender_list = np.array(["Male", "Female"])
+        gender = st.radio("Select your Gender", gender_list)
+
+        blood_pressure_list = np.array(["HIGH", "LOW", "NORMAL"])
+        blood_pressure = st.radio("Select your Blood pressure", blood_pressure_list)
+
+        cholesterol_list = np.array(["HIGH", "NORMAL"])
+        cholesterol = st.radio("Select your Cholesterol", cholesterol_list)
+
+        Na_to_k = st.number_input(label="NA_to_K", min_value=6.2, step=0.1, max_value=38.2)
+        submit_button = st.form_submit_button(label="Predict")
+
+if submit_button:
+    prediction = predict_drug(age, gender[0], blood_pressure, cholesterol, Na_to_k, pipe)
+
+    if prediction is None:
+        st.error("An error occurred while getting the prediction!")
+
+    message = f"The Drug is {prediction}!"
+    message_color = 'red' if prediction == 1 else 'green'
+    st.markdown(f"<h3 style='text-align: left;color:{message_color}'> {(message)} </h3>", unsafe_allow_html=True)

results/metrics.txt

@@ -1,2 +1,2 @@
 
-Accuracy=0.98, F1_score = 0.94
+Accuracy=0.95, F1_score = 0.89

train.py

@@ -63,4 +63,9 @@ with open("./results/metrics.txt", "w") as outfile:
     outfile.write(f"\nAccuracy={round(accuracy, 2)}, F1_score = {round(f1, 2)}")
 
 # Save the model
-sio.dump(pipe, "./model/drug_pipeline.skops")
+import pickle
+# save the model to disk
+filename = './model/drug_pipeline.sav'
+pickle.dump(pipe, open(filename, 'wb'))
+
+#sio.dump(pipe, "./model/drug_pipeline.skops")

utils/prediction.py

@@ -0,0 +1,19 @@
+
+def predict_drug(age, sex, blood_pressure, cholesterol, na_to_k_ratio, pipe):
+    """
+        Predict drugs based on patient features.
+
+    :param age:
+    :param sex:
+    :param blood_pressure:
+    :param cholesterol:
+    :param na_to_k_ratio:
+    :return: str: Predicted drug label.
+
+    """
+
+    features = [age, sex, blood_pressure, cholesterol, na_to_k_ratio]
+    predicted_drug = pipe.predict([features])[0]
+
+    label = f"Predicted Drug: {predicted_drug}"
+    return label