src/streamlit_app.py

import streamlit as st
import pandas as pd
import joblib
import os

# ======================
# LOAD MODEL
# ======================
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
model_path = os.path.join(BASE_DIR, "model.pkl")
model = joblib.load(model_path)

# ======================
# PAGE CONFIG
# ======================
st.set_page_config(
    page_title="Breast Cancer Prediction",
    page_icon="🎗️",
    layout="centered"
)

st.title("🎗️ Breast Cancer Prediction")
st.write("Auto-generated inputs based on trained model features")

# ======================
# GET FEATURE NAMES
# ======================
feature_names = model.feature_names_in_

# ======================
# CREATE INPUTS
# ======================
st.sidebar.header("Input Features")

input_data = {}

for feature in feature_names:
    input_data[feature] = st.sidebar.number_input(
        feature,
        value=float(model.feature_names_in_.shape[0])  # tijdelijk
    )

input_df = pd.DataFrame([input_data])

st.subheader("Input Data")
st.write(input_df)

# ======================
# PREDICTION
# ======================
if st.button("Predict"):

    prediction = model.predict(input_df)[0]
    probability = model.predict_proba(input_df)[0][1]

    st.subheader("Result")

    if prediction == 1:
        st.error(f"⚠️ Malignant Tumor ({probability:.2%})")
    else:
        st.success(f"✅ Benign Tumor ({1 - probability:.2%})")