Update app.py

app.py

@@ -1,290 +1,215 @@
 import os
 import re
-import time
-from typing import Dict, Optional, Tuple
-
 import cv2
+import time
 import numpy as np
 import pandas as pd
 import requests
 import streamlit as st
-from paddleocr import PaddleOCR
 from PIL import Image
+from paddleocr import PaddleOCR
 
-# ---------------------------
-#  Konfigurasi / Constants
-# ---------------------------
-APP_TITLE = "🥗 Nutri-Grade Label Detection"
-APP_VERSION = "v2.1 (refactor)"
+# ==============================
+# 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"
-GRADE_ORDER = ["Grade A", "Grade B", "Grade C", "Grade D"]
-
-# ---------------------------
-#  Util / Helpers
-# ---------------------------
-def get_openrouter_api_key() -> Optional[str]:
-    """
-    Ambil API key dari environment variable atau dari Streamlit secrets.
-    Jangan meletakkan API key langsung di kode.
-    """
-    return os.environ.get("OPENROUTER_API_KEY") or st.secrets.get("OPENROUTER_API_KEY", None)
-
-
-def safe_float_from_text(txt: str) -> float:
-    """
-    Ambil angka dari teks (mengatasi koma desimal) dan konversi ke float.
-    Jika gagal, kembalikan 0.0.
-    """
-    if txt is None:
-        return 0.0
-    s = str(txt).strip()
-    # ganti koma desimal jika ada
-    s = s.replace(",", ".")
-    # ambil simbol angka, titik, minus
-    cleaned = re.sub(r"[^\d\.\-]", "", s)
-    if cleaned in ("", ".", "-", "-.", ".-"):
-        return 0.0
-    try:
-        return float(cleaned)
-    except ValueError:
-        return 0.0
-
-
-def get_grade_from_value(value: float, thresholds: Dict[str, float]) -> str:
-    """
-    Thresholds contoh: {"A": 1.0, "B": 5.0, "C": 10.0}
-    kembalikan "Grade X"
-    """
-    try:
-        if value <= thresholds["A"]:
-            return "Grade A"
-        if value <= thresholds["B"]:
-            return "Grade B"
-        if value <= thresholds["C"]:
-            return "Grade C"
-        return "Grade D"
-    except Exception:
-        return "Grade D"
-
-
-def get_grade_color(grade: str) -> Tuple[str, str]:
-    """
-    kembalikan (background_color, text_color)
-    """
-    colors = {
-        "Grade A": ("#2ecc71", "white"),
-        "Grade B": ("#f1c40f", "black"),
-        "Grade C": ("#e67e22", "white"),
-        "Grade D": ("#e74c3c", "white"),
-    }
-    return colors.get(grade, ("#bdc3c7", "black"))
 
 
-def worse_grade(g1: str, g2: str) -> str:
-    """Pilih grade yang 'lebih buruk' menurut urutan Grade A..D"""
-    idx1 = GRADE_ORDER.index(g1) if g1 in GRADE_ORDER else len(GRADE_ORDER) - 1
-    idx2 = GRADE_ORDER.index(g2) if g2 in GRADE_ORDER else len(GRADE_ORDER) - 1
-    return g1 if idx1 > idx2 else g2
+# ==============================
+# FUNGSI UTAMA
+# ==============================
 
-
-# ---------------------------
-#  OCR Initialization
-# ---------------------------
 @st.cache_resource
-def init_paddleocr() -> Optional[PaddleOCR]:
-    """Inisialisasi PaddleOCR (cached)."""
+def initialize_ocr():
+    """Inisialisasi model PaddleOCR (CPU)."""
     try:
-        # Anda bisa tambahkan lang list sesuai kebutuhan, mis: ['en','id']
-        return PaddleOCR(lang="en", use_angle_cls=True)
+        return PaddleOCR(lang='en', use_angle_cls=True)
     except Exception as e:
-        st.error(f"Gagal inisialisasi PaddleOCR: {e}")
+        st.error(f"Gagal inisialisasi OCR: {e}")
         return None
 
 
-# ---------------------------
-#  OCR & Parsing
-# ---------------------------
-def ocr_read_image(ocr_model: PaddleOCR, img_bgr: np.ndarray) -> str:
-    """
-    Jalankan OCR pada numpy BGR image dan kembalikan teks gabungan (lowercase).
-    Jika error, kembalikan string kosong.
-    """
+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:
-        # PaddleOCR menerima image sebagai numpy array BGR/RGB — kami pakai seperti contoh awal
-        result = ocr_model.ocr(img_bgr, cls=True)
-        # result struktur: list of lists; ambil teks tiap baris
-        lines = []
-        for block in result:
-            for line in block:
-                if len(line) >= 2 and isinstance(line[1], (list, tuple)) is False:
-                    # fallback
-                    continue
-                text = line[1][0] if len(line) >= 2 and isinstance(line[1], (list, tuple)) else ""
-                lines.append(str(text))
-        return " ".join(lines).lower()
-    except Exception as e:
-        st.error(f"OCR gagal: {e}")
-        return ""
+        return float(cleaned)
+    except ValueError:
+        return 0.0
 
 
-def extract_nutri_from_text(text: str) -> Dict[str, str]:
-    """
-    Cari nilai serving, sugar, saturated fat dari teks OCR.
-    Mengembalikan dict string untuk diisi ulang oleh user jika perlu.
-    Pola regex dibuat toleran terhadap kata dalam Bahasa Indonesia & Inggris.
-    """
-    patterns = {
-        "serving": r"(takaran\s*saj(?:i|a)|serving\s*size)[^\d,\.]*([\d\.,]+)",
-        "sugar": r"(gula|sugar)[^\d,\.]*([\d\.,]+)",
-        "fat": r"(lemak\s*jenuh|saturated\s*fat)[^\d,\.]*([\d\.,]+)",
-    }
-    found = {}
-    for key, pat in patterns.items():
-        m = re.search(pat, text, flags=re.IGNORECASE)
-        if m:
-            found[key] = m.group(2)
-    return found
-
-
-# ---------------------------
-#  AI Advice (OpenRouter)
-# ---------------------------
-def get_nutrition_advice_openrouter(api_key: str, serving_size: float, sugar_norm: float, fat_norm: float,
-                                    sugar_grade: str, fat_grade: str, final_grade: str) -> str:
-    """
-    Panggil OpenRouter Chat Completions untuk meminta saran nutrisi singkat.
-    Pastikan API key tersedia (diperoleh dari env / secrets).
+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.
     """
-    if not api_key:
-        return "OpenRouter API key tidak ditemukan. Silakan set OPENROUTER_API_KEY di environment atau streamlit secrets."
-    prompt = (
-        "Anda adalah ahli gizi dari Indonesia yang ramah.\n"
-        f"- Takaran Saji: {serving_size} g/ml\n"
-        f"- Gula (per 100): {sugar_norm:.2f} g (Grade {sugar_grade.replace('Grade ', '')})\n"
-        f"- Lemak Jenuh (per 100): {fat_norm:.2f} g (Grade {fat_grade.replace('Grade ', '')})\n"
-        f"- Grade Akhir: {final_grade.replace('Grade ', '')}\n\n"
-        "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,
     }
-    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
     try:
-        r = requests.post(f"{OPENROUTER_BASE_URL}/chat/completions", json=payload, headers=headers, timeout=30)
+        r = requests.post(f"{OPENROUTER_BASE_URL}/chat/completions", headers=headers, json=payload, timeout=30)
         r.raise_for_status()
-        data = r.json()
-        return data["choices"][0]["message"]["content"].strip()
+        return r.json()["choices"][0]["message"]["content"].strip()
     except Exception as e:
-        return f"Error saat meminta saran AI: {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"))
 
 
-# ---------------------------
-#  Streamlit App UI
-# ---------------------------
 def reset_state():
-    """Reset beberapa key di session state saat user upload file baru."""
-    for k in ("ocr_done", "ocr_text", "extracted", "calc", "calculated", "ai_advice"):
-        if k in st.session_state:
-            del st.session_state[k]
-
-
-def render_grade_box(col, title: str, value: float, unit: str, grade: str):
-    bg, txtc = get_grade_color(grade)
-    col.markdown(
-        f"<div style='background:{bg};padding:12px;border-radius:10px;text-align:center;color:{txtc};'>"
-        f"<strong>{title}</strong>"
-        f"<p style='font-size:18px;margin:6px 0'>{value:.2f} {unit}</p>"
-        f"<h4 style='margin:2px'>{grade}</h4></div>",
-        unsafe_allow_html=True,
-    )
-
-
-def main():
-    st.set_page_config(page_title=APP_TITLE, page_icon="🥗", layout="wide", initial_sidebar_state="collapsed")
-    st.title(APP_TITLE)
-    st.caption(f"Analisis gizi produk berdasarkan standar Nutri-Grade. {APP_VERSION}")
-
-    with st.expander("📋 Petunjuk Penggunaan"):
-        st.markdown(
-            "1. Upload gambar (JPG/PNG).\n"
-            "2. Klik **Analisis OCR**.\n"
-            "3. Koreksi hasil jika diperlukan.\n"
-            "4. Klik **Hitung Grade**."
+    """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"<div style='background:{bg};padding:10px;border-radius:8px;text-align:center;color:{text_color};'>"
+            f"<strong>{title}</strong><p>{value:.2f} {unit}</p><h4>{grade}</h4></div>",
+            unsafe_allow_html=True
         )
 
-    ocr_model = init_paddleocr()
-    if ocr_model is None:
-        st.error("OCR model tidak tersedia. Pastikan PaddleOCR terinstal dengan benar.")
-        st.stop()
-
-    # --- Upload ---
-    st.header("1. Upload Gambar")
-    uploaded = st.file_uploader("Pilih gambar tabel gizi", type=["jpg", "jpeg", "png"], on_change=reset_state)
-    img_bgr = None
-    if uploaded:
-        # tampilkan preview
-        file_bytes = np.frombuffer(uploaded.read(), np.uint8)
-        img_bgr = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-        if img_bgr is None:
-            st.error("Gagal membaca gambar. Pastikan file gambar valid.")
-        else:
-            st.image(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB), width=320)
-
-    # tombol OCR
-    if uploaded and st.button("Analisis OCR"):
-        with st.spinner("Mendeteksi teks..."):
-            text = ocr_read_image(ocr_model, img_bgr)
-            st.session_state.ocr_text = text
-            st.session_state.extracted = extract_nutri_from_text(text)
-            st.session_state.ocr_done = True
-        st.success("OCR selesai — periksa hasil ekstraksi di langkah berikutnya.")
-
-    # --- Koreksi & Hitung ---
-    if st.session_state.get("ocr_done"):
-        st.header("2. Koreksi & Hitung Grade")
-        extracted = st.session_state.get("extracted", {})
-        default_serving = extracted.get("serving", "100")
-        default_sugar = extracted.get("sugar", "0")
-        default_fat = extracted.get("fat", "0")
-
-        with st.form("calc_form"):
-            serving_input = st.text_input("Takaran Saji (g/ml)", value=default_serving)
-            sugar_input = st.text_input("Gula (g) — total per serving", value=default_sugar)
-            fat_input = st.text_input("Lemak Jenuh (g) — total per serving", value=default_fat)
-            submitted = st.form_submit_button("Hitung Grade")
-
-        if submitted:
-            sv = safe_float_from_text(serving_input)
-            sugar = safe_float_from_text(sugar_input)
-            fat = safe_float_from_text(fat_input)
-
-            # normalisasi ke per 100 g / ml
-            sugar_per100 = (sugar / sv) * 100 if sv > 0 else 0.0
-            fat_per100 = (fat / sv) * 100 if sv > 0 else 0.0
-
-            st.session_state.calc = {"serving": sv, "sugar": sugar, "fat": fat,
-                                     "sugar_per100": sugar_per100, "fat_per100": fat_per100}
-            st.session_state.calculated = True
-
-    # --- Tampilkan Hasil ---
-    if st.session_state.get("calculated"):
-        st.header("3. Hasil Grading")
-        c = st.session_state["calc"]
-        # thresholds (contoh standar)
-        sugar_thresholds = {"A": 1.0, "B": 5.0, "C": 10.0}
-        fat_thresholds = {"A": 0.7, "B": 1.2, "C": 2.8}
-
-        sugar_grade = get_grade_from_value(c["sugar_per100"], sugar_thresholds)
-        fat_grade = get_grade_from_value(c["fat_per100"], fat_thresholds)
-        final = worse_grade(sugar_grade, fat_grade)
-
-        cols = st.columns(3, gap="large")
-        render_grade_box(cols[0], "Gula", c["sugar_per100"], "g/100", sugar_grade)
-        render_grade_box(cols[1], "Lemak Jenuh", c["fat_per100"], "g/100", fat_grade)
-        render_grade_box(cols[2], "Grade Akhir", 0.0, "", final)
-
-        st.markdown("---")
-        st.header("4.
+    show(cols[0], "Gula", c['sp'], "g/100ml", gs)
+    show(cols[1], "Lemak Jenuh", c['fp'], "g/100ml", gf)
+    show(cols[2], "Grade Akhir", 0, "", final_grade)
+
+    st.divider()
+    st.header("4. Saran Nutrisi AI")
+
+    with st.spinner("Meminta AI..."):
+        advice = get_nutrition_advice(c['sv'], c['sp'], c['fp'], gs, gf, final_grade)
+
+    st.info(advice)
+
+# --- Footer ---
+st.markdown("---")
+st.markdown("<p style='text-align:center;'>Nutri-Grade App v2.1 &copy; 2024</p>", unsafe_allow_html=True)