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import streamlit as st
import pickle
import pandas as pd
import requests
from bs4 import BeautifulSoup
import re
# === Load model & vectorizer ===
@st.cache_resource
def load_model(model_choice):
with open(f'model/{model_choice}_model.pkl', 'rb') as f:
model = pickle.load(f)
with open('model/tfidf_vectorizer.pkl', 'rb') as f:
vectorizer = pickle.load(f)
return model, vectorizer
label_names = ['susu', 'kacang', 'telur', 'makanan_laut', 'gandum']
# === Text Cleaning ===
def clean_text(text):
text = re.sub(r'[^\w\s]', ' ', text.lower())
text = re.sub(r'\d+', '', text)
return text.strip()
# === Scraping Cookpad ===
def scrape_ingredients(url):
try:
headers = {'User-Agent': 'Mozilla/5.0'}
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.content, 'html.parser')
ingredients_div = soup.find('div', id='ingredients')
if ingredients_div:
return ingredients_div.get_text(separator=' ')
except:
pass
return None
# === UI ===
st.title("🍲 Deteksi Alergen dari Resep Cookpad")
# Hapus SVM dari pilihan
model_choice = st.selectbox("🔍 Pilih model:", options=["KNN", "RF"])
model, vectorizer = load_model(model_choice)
input_mode = st.radio("Pilih mode input:", ["Teks Manual", "Link Cookpad.com"])
if input_mode == "Teks Manual":
user_input = st.text_area("📝 Masukkan teks bahan makanan:")
else:
url_input = st.text_input("🔗 Masukkan URL resep dari cookpad.com:")
if url_input:
scraped = scrape_ingredients(url_input)
if scraped:
user_input = scraped
st.success("✅ Bahan berhasil diambil dari URL!")
st.text_area("📋 Bahan yang diambil:", value=user_input, height=200)
else:
user_input = ""
st.error("Gagal mengambil data dari URL. Pastikan URL valid dan dari cookpad.com.")
threshold = st.slider("🎚 Threshold prediksi (default 0.5):", 0.0, 1.0, 0.5)
if st.button("🚀 Prediksi"):
if user_input.strip():
cleaned_text = clean_text(user_input)
user_vector = vectorizer.transform([cleaned_text])
if hasattr(model, "predict_proba"):
user_proba = model.predict_proba(user_vector)
user_proba = [p[0][1] for p in user_proba] # probability of class 1
else:
user_proba = model.predict(user_vector)[0]
user_proba = [float(val) for val in user_proba]
st.subheader(f"📊 Hasil Prediksi Alergen ({model_choice}):")
for label, proba in zip(label_names, user_proba):
status = "✅ Ada" if proba >= threshold else "❌ Tidak Ada"
st.write(f"- **{label}**: {status} ({proba:.2f})")
else:
st.warning("❗ Masukkan teks terlebih dahulu atau URL valid.")
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