Update app.py

app.py

@@ -1,162 +1,77 @@
-import faicons as fa
-import plotly.express as px
-
-# Load data and compute static values
-from shared import app_dir, tips
-from shinywidgets import render_plotly
-
-from shiny import reactive, render
-from shiny.express import input, ui
-
-bill_rng = (min(tips.total_bill), max(tips.total_bill))
-
-# Add page title and sidebar
-ui.page_opts(title="Restaurant tipping", fillable=True)
-
-with ui.sidebar(open="desktop"):
-    ui.input_slider(
-        "total_bill",
-        "Bill amount",
-        min=bill_rng[0],
-        max=bill_rng[1],
-        value=bill_rng,
-        pre="$",
-    )
-    ui.input_checkbox_group(
-        "time",
-        "Food service",
-        ["Lunch", "Dinner"],
-        selected=["Lunch", "Dinner"],
-        inline=True,
-    )
-    ui.input_action_button("reset", "Reset filter")
-
-# Add main content
-ICONS = {
-    "user": fa.icon_svg("user", "regular"),
-    "wallet": fa.icon_svg("wallet"),
-    "currency-dollar": fa.icon_svg("dollar-sign"),
-    "ellipsis": fa.icon_svg("ellipsis"),
-}
-
-with ui.layout_columns(fill=False):
-    with ui.value_box(showcase=ICONS["user"]):
-        "Total tippers"
-
-        @render.express
-        def total_tippers():
-            tips_data().shape[0]
-
-    with ui.value_box(showcase=ICONS["wallet"]):
-        "Average tip"
-
-        @render.express
-        def average_tip():
-            d = tips_data()
-            if d.shape[0] > 0:
-                perc = d.tip / d.total_bill
-                f"{perc.mean():.1%}"
-
-    with ui.value_box(showcase=ICONS["currency-dollar"]):
-        "Average bill"
-
-        @render.express
-        def average_bill():
-            d = tips_data()
-            if d.shape[0] > 0:
-                bill = d.total_bill.mean()
-                f"${bill:.2f}"
-
-
-with ui.layout_columns(col_widths=[6, 6, 12]):
-    with ui.card(full_screen=True):
-        ui.card_header("Tips data")
-
-        @render.data_frame
-        def table():
-            return render.DataGrid(tips_data())
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Total bill vs tip"
-            with ui.popover(title="Add a color variable", placement="top"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "scatter_color",
-                    None,
-                    ["none", "sex", "smoker", "day", "time"],
-                    inline=True,
-                )
-
-        @render_plotly
-        def scatterplot():
-            color = input.scatter_color()
-            return px.scatter(
-                tips_data(),
-                x="total_bill",
-                y="tip",
-                color=None if color == "none" else color,
-                trendline="lowess",
-            )
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Tip percentages"
-            with ui.popover(title="Add a color variable"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "tip_perc_y",
-                    "Split by:",
-                    ["sex", "smoker", "day", "time"],
-                    selected="day",
-                    inline=True,
-                )
-
-        @render_plotly
-        def tip_perc():
-            from ridgeplot import ridgeplot
-
-            dat = tips_data()
-            dat["percent"] = dat.tip / dat.total_bill
-            yvar = input.tip_perc_y()
-            uvals = dat[yvar].unique()
-
-            samples = [[dat.percent[dat[yvar] == val]] for val in uvals]
-
-            plt = ridgeplot(
-                samples=samples,
-                labels=uvals,
-                bandwidth=0.01,
-                colorscale="viridis",
-                colormode="row-index",
-            )
-
-            plt.update_layout(
-                legend=dict(
-                    orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
-                )
-            )
-
-            return plt
-
-
-ui.include_css(app_dir / "styles.css")
-
-# --------------------------------------------------------
-# Reactive calculations and effects
-# --------------------------------------------------------
-
-
-@reactive.calc
-def tips_data():
-    bill = input.total_bill()
-    idx1 = tips.total_bill.between(bill[0], bill[1])
-    idx2 = tips.time.isin(input.time())
-    return tips[idx1 & idx2]
-
-
-@reactive.effect
-@reactive.event(input.reset)
-def _():
-    ui.update_slider("total_bill", value=bill_rng)
-    ui.update_checkbox_group("time", selected=["Lunch", "Dinner"])
+# main.py
+
+import pandas as pd
+import joblib
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+
+# Inisialisasi aplikasi FastAPI
+app = FastAPI()
+
+# 1. Tentukan struktur data input menggunakan Pydantic
+# Ini akan menjadi format JSON yang diterima oleh API Anda.
+# Pastikan nama field sama persis dengan kolom yang Anda gunakan saat training.
+class StudentFeatures(BaseModel):
+    IPK_Terakhir: float
+    IPS_Terakhir: float
+    Total_SKS: int
+    IPS_Tertinggi: float
+    IPS_Terendah: float
+    Rentang_IPS: float
+    Jumlah_MK_Gagal: int
+    Total_SKS_Gagal: int
+    Tren_IPS_Slope: float
+    Perubahan_Kinerja_Terakhir: float
+    IPK_Ternormalisasi_SKS: float
+    Profil_Tren: str  # Ini adalah fitur kategorikal sebelum di-encode
+
+# 2. Muat model yang sudah Anda simpan
+# Model akan dimuat sekali saat aplikasi dimulai untuk efisiensi
+model = joblib.load('model_risiko_akademik.joblib')
+
+# Daftar kolom fitur yang diharapkan oleh model setelah one-hot encoding
+MODEL_FEATURES = [
+    'IPK_Terakhir', 'IPS_Terakhir', 'Total_SKS', 'IPS_Tertinggi',
+    'IPS_Terendah', 'Rentang_IPS', 'Jumlah_MK_Gagal', 'Total_SKS_Gagal',
+    'Tren_IPS_Slope', 'Perubahan_Kinerja_Terakhir',
+    'IPK_Ternormalisasi_SKS', 'Tren_Menaik', 'Tren_Menurun', 'Tren_Stabil'
+]
+
+@app.get("/")
+def read_root():
+    return {"message": "API untuk Prediksi Risiko Akademik Mahasiswa"}
+
+# 3. Buat endpoint untuk prediksi
+@app.post("/predict/")
+def predict_risk(student_data: StudentFeatures):
+    # Konversi data input Pydantic ke dictionary
+    data = student_data.dict()
+    
+    # Buat DataFrame dari input (hanya satu baris)
+    input_df = pd.DataFrame([data])
+    
+    # Lakukan pra-pemrosesan yang SAMA PERSIS seperti di notebook
+    # a. One-Hot Encoding
+    input_encoded = pd.get_dummies(input_df, columns=['Profil_Tren'], prefix='Tren')
+    
+    # b. Pastikan semua kolom yang diharapkan model ada
+    # Ini PENTING. Jika input 'Profil_Tren' adalah 'Menaik', kolom 'Tren_Menurun'
+    # dan 'Tren_Stabil' tidak akan dibuat. Kita harus menambahkannya secara manual.
+    input_encoded = input_encoded.reindex(columns=MODEL_FEATURES, fill_value=False)
+    
+    # Lakukan prediksi
+    prediction = model.predict(input_encoded)
+    
+    # Ambil probabilitas prediksi (opsional, tapi informatif)
+    prediction_proba = model.predict_proba(input_encoded)
+    
+    # Ambil label kelas dari probabilitas
+    classes = model.classes_
+    probabilities = dict(zip(classes, prediction_proba[0]))
+    
+    # Kembalikan hasil dalam format JSON
+    return {
+        "prediction": prediction[0],
+        "probabilities": probabilities
+    }