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e012bff cda7913 e012bff 790b130 e012bff cda7913 e012bff cda7913 e012bff cda7913 e012bff cda7913 e012bff cda7913 e012bff cda7913 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import os
import joblib
import pandas as pd
import streamlit as st
st.set_page_config(page_title="General Classification Lab", page_icon="🚀", layout="wide")
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@st.cache_resource
def load_models():
return (
joblib.load(os.path.join(ROOT, "spaceship_bundle.joblib")),
joblib.load(os.path.join(ROOT, "iris_bundle.joblib")),
)
spaceship, iris = load_models()
st.title("🚀 General Classification AI Lab")
st.caption("Two classic classification problems rebuilt as interactive, tested applications.")
spaceship_tab, iris_tab = st.tabs(["Spaceship Titanic", "Iris Flower"])
with spaceship_tab:
st.subheader("Will a passenger be transported to another dimension?")
a, b, c = st.columns(3)
home = a.selectbox("Home planet", ["Earth", "Europa", "Mars"])
destination = a.selectbox("Destination", ["TRAPPIST-1e", "55 Cancri e", "PSO J318.5-22"])
age = a.slider("Age", 0, 90, 30)
cryo = a.checkbox("CryoSleep")
vip = a.checkbox("VIP passenger")
group_size = b.slider("Travel group size", 1, 8, 1)
deck = b.selectbox("Cabin deck", list("ABCDEFGT"), index=5)
side = b.selectbox("Cabin side", ["P", "S"], format_func=lambda x: "Port" if x == "P" else "Starboard")
room = b.number_input("Room service spend", 0.0, 30000.0, 0.0, 50.0)
food = b.number_input("Food court spend", 0.0, 30000.0, 500.0, 50.0)
shopping = c.number_input("Shopping mall spend", 0.0, 30000.0, 0.0, 50.0)
spa = c.number_input("Spa spend", 0.0, 30000.0, 0.0, 50.0)
vr = c.number_input("VR deck spend", 0.0, 30000.0, 0.0, 50.0)
if st.button("Predict transportation", type="primary"):
values = {
"HomePlanet": home, "CryoSleep": cryo, "Destination": destination, "Age": age, "VIP": vip,
"RoomService": room, "FoodCourt": food, "ShoppingMall": shopping, "Spa": spa, "VRDeck": vr,
"GroupSize": group_size, "Deck": deck, "CabinSide": side,
"TotalSpend": room + food + shopping + spa + vr,
}
row = pd.DataFrame([values], columns=spaceship["features"])
probability = float(spaceship["model"].predict_proba(row)[0, 1])
st.metric("Transportation probability", f"{probability:.1%}")
st.success("Likely transported") if probability >= .5 else st.warning("Likely not transported")
st.caption("Validation accuracy: 80.16% on held-out Spaceship Titanic competition passengers.")
with iris_tab:
st.subheader("Classify an Iris flower from four measurements")
a, b = st.columns(2)
sepal_length = a.slider("Sepal length (cm)", 4.0, 8.0, 5.8, .1)
sepal_width = a.slider("Sepal width (cm)", 2.0, 5.0, 3.0, .1)
petal_length = b.slider("Petal length (cm)", 1.0, 7.0, 4.3, .1)
petal_width = b.slider("Petal width (cm)", .1, 2.5, 1.3, .1)
if st.button("Classify flower", type="primary"):
row = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]], columns=iris["features"])
probabilities = iris["model"].predict_proba(row)[0]
prediction = int(probabilities.argmax())
st.success(f"Predicted species: **Iris {iris['target_names'][prediction].title()}**")
chart = pd.DataFrame({"Species": [x.title() for x in iris["target_names"]], "Probability": probabilities}).set_index("Species")
st.bar_chart(chart)
st.caption("Validation accuracy: 96.67% on held-out Iris samples.")
st.divider()
st.caption("Portfolio of Jale Summak · Educational classification applications")
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