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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 === | |
| 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.") | |