import os
import re
import cv2
import time
import numpy as np
import pandas as pd
import requests
import streamlit as st
from PIL import Image
from paddleocr import PaddleOCR
# ==============================
# KONFIGURASI APLIKASI
# ==============================
st.set_page_config(
page_title="Nutri-Grade Label Detection",
page_icon="🥗",
layout="wide",
initial_sidebar_state="collapsed"
)
# API OpenRouter
OPENROUTER_API_KEY = "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"
OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
# ==============================
# FUNGSI UTAMA
# ==============================
@st.cache_resource
def initialize_ocr():
"""Inisialisasi model PaddleOCR (CPU)."""
try:
return PaddleOCR(lang='en', use_angle_cls=True)
except Exception as e:
st.error(f"Gagal inisialisasi OCR: {e}")
return None
def parse_numeric_value(text: str) -> float:
"""Mengubah string menjadi float, hanya menyisakan angka/desimal."""
cleaned = re.sub(r"[^\d\.\-]", "", str(text))
if cleaned in ['', '.', '-']:
return 0.0
try:
return float(cleaned)
except ValueError:
return 0.0
def get_nutrition_advice(serving_size, sugar_norm, fat_norm, sugar_grade, fat_grade, final_grade):
"""Memanggil API OpenRouter untuk menghasilkan saran nutrisi singkat."""
prompt = f"""
Anda adalah ahli gizi dari Indonesia yang ramah.
- Takaran Saji: {serving_size} g/ml
- Gula (per 100): {sugar_norm:.2f} g (Grade {sugar_grade.replace('Grade ', '')})
- Lemak Jenuh (per 100): {fat_norm:.2f} g (Grade {fat_grade.replace('Grade ', '')})
- Grade Akhir: {final_grade.replace('Grade ', '')}
Berikan saran nutrisi singkat 50-80 kata, fokus pada dampak kesehatan dan tips praktis.
"""
headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}", "Content-Type": "application/json"}
payload = {
"model": "mistralai/mistral-7b-instruct:free",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 250,
"temperature": 0.7,
}
try:
r = requests.post(f"{OPENROUTER_BASE_URL}/chat/completions", headers=headers, json=payload, timeout=30)
r.raise_for_status()
return r.json()["choices"][0]["message"]["content"].strip()
except Exception as e:
return f"Error: {e}"
def get_grade_from_value(value, thresholds):
"""Menentukan grade berdasarkan ambang batas."""
if value <= thresholds["A"]:
return "Grade A"
if value <= thresholds["B"]:
return "Grade B"
if value <= thresholds["C"]:
return "Grade C"
return "Grade D"
def get_grade_color(grade):
"""Mengembalikan warna background & teks untuk tiap grade."""
return {
"Grade A": ("#2ecc71", "white"),
"Grade B": ("#f1c40f", "black"),
"Grade C": ("#e67e22", "white"),
"Grade D": ("#e74c3c", "white")
}.get(grade, ("#bdc3c7", "black"))
def reset_state():
"""Reset session_state agar analisis bisa diulang."""
for key in ['ocr_done', 'data', 'calculated', 'calc']:
st.session_state.pop(key, None)
# ==============================
# UI APLIKASI
# ==============================
# Inisialisasi OCR
ocr_engine = initialize_ocr()
if ocr_engine is None:
st.error("Model OCR tidak tersedia.")
st.stop()
st.title("🥗 Nutri-Grade Detection & Grade Calculator")
st.caption("Analisis gizi produk berdasarkan standar Nutri-Grade Singapura.")
with st.expander("📋 Petunjuk Penggunaan"):
st.markdown("""
1. Upload gambar (JPG/PNG).
2. Klik **Analisis OCR**.
3. Koreksi hasil jika perlu.
4. Klik **Hitung Grade**.
""")
# --- Step 1: Upload ---
st.header("1. Upload Gambar")
file = st.file_uploader("Pilih gambar tabel gizi", type=["jpg", "jpeg", "png"], on_change=reset_state)
if file:
arr = np.frombuffer(file.read(), np.uint8)
img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), width=300)
if st.button("Analisis OCR"):
with st.spinner("Mendeteksi teks..."):
res = ocr_engine.ocr(img)
texts = [ln[1][0] for ln in (res[0] if res else [])]
full_text = " ".join(texts).lower()
patterns = {
'serving': r"(takaran saj[i|a]|serving size)[^\d]*(\d+\.?\d*)",
'sugar': r"(gula|sugar)[^\d]*(\d+\.?\d*)",
'fat': r"(lemak jenuh|saturated fat)[^\d]*(\d+\.?\d*)"
}
data = {}
for key, pattern in patterns.items():
match = re.search(pattern, full_text)
if match:
data[key] = match.group(2)
st.session_state.data = data
st.session_state.ocr_done = True
st.success("OCR selesai!")
st.rerun()
# --- Step 2: Koreksi & Hitung ---
if st.session_state.get('ocr_done'):
st.header("2. Koreksi & Hitung Grade")
d = st.session_state.data
with st.form("form2"):
serving = st.text_input("Takaran Saji (g/ml)", value=d.get('serving', '100'))
sugar = st.text_input("Gula (g)", value=d.get('sugar', '0'))
fat = st.text_input("Lemak Jenuh (g)", value=d.get('fat', '0'))
ok = st.form_submit_button("Hitung Grade")
if ok:
sv = parse_numeric_value(serving)
sg = parse_numeric_value(sugar)
fg = parse_numeric_value(fat)
sugar_per100 = (sg / sv) * 100 if sv > 0 else 0
fat_per100 = (fg / sv) * 100 if sv > 0 else 0
st.session_state.calc = {'sv': sv, 'sp': sugar_per100, 'fp': fat_per100}
st.session_state.calculated = True
# --- Step 3: Tampilkan Hasil ---
if st.session_state.get('calculated'):
c = st.session_state.calc
gs = get_grade_from_value(c['sp'], {"A": 1.0, "B": 5.0, "C": 10.0})
gf = get_grade_from_value(c['fp'], {"A": 0.7, "B": 1.2, "C": 2.8})
final_grade = max(gs, gf, key=lambda x: ['Grade A', 'Grade B', 'Grade C', 'Grade D'].index(x))
st.header("3. Hasil Grading")
cols = st.columns(3)
def show(col, title, value, unit, grade):
bg, text_color = get_grade_color(grade)
col.markdown(
f""
f"
{title}{value:.2f} {unit}
{grade}
Nutri-Grade App v2.1 © 2024
", unsafe_allow_html=True)