app.py

import streamlit as st
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import re
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load tokenizer dan model
MODEL_PATH = 'model/alergen_model.pt'
MODEL_NAME = 'indobenchmark/indobert-base-p1'
TARGET_COLUMNS = ['susu', 'kacang', 'telur', 'makanan_laut', 'gandum']
MAX_LEN = 128
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

class MultilabelBertClassifier(nn.Module):
    def __init__(self, model_name, num_labels):
        super(MultilabelBertClassifier, self).__init__()
        self.bert = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)
        self.bert.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        return outputs.logits

model = MultilabelBertClassifier(MODEL_NAME, len(TARGET_COLUMNS))
model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
model.to(device)
model.eval()

# Fungsi preprocessing
def clean_text(text):
    text = text.replace('--', ' ')
    text = re.sub(r"http\S+", "", text)
    text = re.sub('\n', ' ', text)
    text = re.sub("[^a-zA-Z0-9\s]", " ", text)
    text = re.sub(" {2,}", " ", text)
    text = text.strip()
    text = text.lower()
    return text

# Fungsi prediksi
def predict(text):
    cleaned = clean_text(text)
    encoding = tokenizer.encode_plus(
        cleaned,
        add_special_tokens=True,
        max_length=MAX_LEN,
        return_tensors='pt',
        padding='max_length',
        truncation=True
    )
    input_ids = encoding['input_ids'].to(device)
    attention_mask = encoding['attention_mask'].to(device)

    with torch.no_grad():
        logits = model(input_ids=input_ids, attention_mask=attention_mask)
        probs = torch.sigmoid(logits).cpu().numpy().flatten()
        results = {TARGET_COLUMNS[i]: float(probs[i]) for i in range(len(TARGET_COLUMNS))}
        return results

# STREAMLIT UI
st.title("🔍 Deteksi Alergen dari Bahan Makanan")
st.markdown("Masukkan daftar bahan makanan, dan sistem akan memprediksi kemungkinan alergen.")

user_input = st.text_area("🧾 Bahan makanan (contoh: 2 butir telur, 1 gelas susu, kacang tanah...)")

if st.button("Prediksi Alergen"):
    if user_input.strip() == "":
        st.warning("Silakan masukkan bahan makanan terlebih dahulu.")
    else:
        with st.spinner("Memproses..."):
            predictions = predict(user_input)
            st.subheader("📊 Hasil Prediksi:")
            for allergen, score in predictions.items():
                st.write(f"- **{allergen}**: {'✅ Terdeteksi' if score > 0.5 else '❌ Tidak terdeteksi'} (Probabilitas: {score:.2f})")