Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,59 @@
-import altair as alt
+import streamlit as st
+import joblib
 import numpy as np
 import pandas as pd
-import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ----------------------------
+# Load Saved Model & Encoders
+# ----------------------------
+model = joblib.load("best_stroke_model.pkl")
+x_num_scaler = joblib.load("x_num_scaler.pkl")
+x_cat_encoder = joblib.load("x_cat_encoder.pkl")
+x_cat_ordinal_encoder = joblib.load("x_cat_ordinal_encoder.pkl")
+
+# ----------------------------
+# Streamlit App Layout
+# ----------------------------
+st.set_page_config(page_title="Stroke Prediction App", page_icon="🧠", layout="centered")
+st.title("🧠 Stroke Prediction App")
+st.write("Fill in the details below to check the risk of stroke.")
+
+# ----------------------------
+# Input Fields
+# ----------------------------
+gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+age = st.number_input("Age", min_value=1, max_value=120, value=30)
+hypertension = st.selectbox("Hypertension (0=No, 1=Yes)", [0, 1])
+heart_disease = st.selectbox("Heart Disease (0=No, 1=Yes)", [0, 1])
+work_type = st.selectbox("Work Type", ["Private", "Self-employed", "Govt_job", "children", "Never_worked"])
+avg_glucose_level = st.number_input("Average Glucose Level", min_value=50.0, max_value=300.0, value=100.0)
+bmi = st.number_input("BMI", min_value=10.0, max_value=50.0, value=22.0)
+smoking_status = st.selectbox("Smoking Status", ["never smoked", "formerly smoked", "smokes", "Unknown"])
+
+# ----------------------------
+# Prediction Button
+# ----------------------------
+if st.button("Predict"):
+    # Convert to DataFrame for transformation
+    input_df = pd.DataFrame([[gender, age, hypertension, heart_disease, work_type, avg_glucose_level, bmi, smoking_status]],
+                            columns=["gender", "age", "hypertension", "heart_disease", "work_type", "avg_glucose_level", "bmi", "smoking_status"])
+
+    # Transform numeric columns
+    scaled_x_num = x_num_scaler.transform(input_df[["age", "avg_glucose_level", "bmi"]])
+    
+    # Binary values (no transformation needed)
+    binary_x = input_df[["hypertension", "heart_disease"]].values
+    
+    # Encode categorical columns
+    encoded_x_cat = x_cat_encoder.transform(input_df[["work_type", "smoking_status"]])
+    ordinal_encoded_x_cat = x_cat_ordinal_encoder.transform(input_df[["gender"]])
+
+    # Combine all into final input
+    final_x = np.hstack([scaled_x_num, binary_x, encoded_x_cat, ordinal_encoded_x_cat])
+
+    # Prediction
+    prediction = model.predict(final_x)
+    result = "⚠️ High Risk of Stroke" if prediction[0] == 1 else "✅ Low Risk of Stroke"
+
+    st.success(f"**Prediction:** {result}")
+    st.info("This prediction is based on machine learning and should not replace professional medical advice.")