Update app.py

app.py

@@ -7,7 +7,6 @@ import pandas as pd
 from PIL import Image
 import time
 import requests
-import json
 from paddleocr import PaddleOCR, draw_ocr
 
 # --- KONFIGURASI APLIKASI ---
@@ -20,15 +19,8 @@ st.set_page_config(
     initial_sidebar_state="collapsed"
 )
 
-# [SANGAT PENTING] Ambil API Key dari Streamlit Secrets untuk keamanan
-# JANGAN PERNAH MENULIS API KEY LANGSUNG DI KODE!
-try:
-    OPENROUTER_API_KEY = st.secrets["OPENROUTER_API_KEY"]
-except (FileNotFoundError, KeyError):
-    st.error("🚨 Harap tambahkan OPENROUTER_API_KEY Anda ke Streamlit Secrets.")
-    st.info("Buat file bernama .streamlit/secrets.toml dan tambahkan baris: OPENROUTER_API_KEY = 'sk-or-v1-...'")
-    st.stop()
-    
+# [Pengaturan API Key] Gunakan API Key langsung (sesuai permintaan)
+OPENROUTER_API_KEY = "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"
 OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
 
 # --- FUNGSI-FUNGSI UTAMA ---
@@ -37,18 +29,17 @@ OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
 def initialize_ocr():
     """Inisialisasi model PaddleOCR dan menyimpannya di cache."""
     try:
-        # Menggunakan CPU (use_gpu=False) untuk kompatibilitas hosting gratisan yang lebih baik
         ocr = PaddleOCR(use_gpu=False, lang='en', use_angle_cls=True, show_log=False)
         return ocr
     except Exception as e:
         st.error(f"Gagal total inisialisasi OCR: {e}")
         return None
 
+
 def parse_numeric_value(text: str) -> float:
     """Membersihkan dan mengubah string menjadi nilai float."""
     if not text:
         return 0.0
-    # Hanya menyisakan digit, titik, dan tanda minus
     cleaned = re.sub(r"[^\d\.\-]", "", str(text))
     if not cleaned or cleaned in [".", "-"]:
         return 0.0
@@ -57,6 +48,7 @@ def parse_numeric_value(text: str) -> float:
     except (ValueError, TypeError):
         return 0.0
 
+
 def get_nutrition_advice(serving_size, sugar_norm, fat_norm, sugar_grade, fat_grade, final_grade):
     """Mendapatkan saran nutrisi dari model AI melalui OpenRouter."""
     prompt = f"""
@@ -76,7 +68,7 @@ def get_nutrition_advice(serving_size, sugar_norm, fat_norm, sugar_grade, fat_gr
         "Content-Type": "application/json"
     }
     payload = {
-        # Menggunakan model yang lebih umum dan gratis
+        # Model tetap "mistralai/mistral-7b-instruct:free"
         "model": "mistralai/mistral-7b-instruct:free",
         "messages": [{"role": "user", "content": prompt}],
         "max_tokens": 250,
@@ -84,238 +76,128 @@ def get_nutrition_advice(serving_size, sugar_norm, fat_norm, sugar_grade, fat_gr
     }
     try:
         response = requests.post(f"{OPENROUTER_BASE_URL}/chat/completions", headers=headers, json=payload, timeout=30)
-        response.raise_for_status() # Akan error jika status code bukan 2xx
+        response.raise_for_status()
         data = response.json()
         return data["choices"][0]["message"]["content"].strip()
     except requests.exceptions.HTTPError as e:
-        return f"Error: Gagal menghubungi server AI ({e.response.status_code}). Model mungkin sedang sibuk atau API key tidak valid."
+        return f"Error: Gagal menghubungi server AI ({e.response.status_code})."
     except requests.exceptions.RequestException as e:
-        return f"Error: Gagal terhubung ke API. Periksa koneksi internet Anda. ({e})"
+        return f"Error: Gagal terhubung ke API ({e})."
     except Exception as e:
-        return f"Error: Terjadi kesalahan tak terduga. ({e})"
+        return f"Error: Terjadi kesalahan tak terduga ({e})."
+
 
 def get_grade_from_value(value, thresholds):
-    """Menentukan grade (A, B, C, D) berdasarkan nilai dan 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_text):
-    """Mengembalikan kode warna berdasarkan grade."""
     colors = {
-        "Grade A": ("#2ecc71", "white"), # Hijau
-        "Grade B": ("#f1c40f", "black"), # Kuning
-        "Grade C": ("#e67e22", "white"), # Oranye
-        "Grade D": ("#e74c3c", "white")  # Merah
+        "Grade A": ("#2ecc71", "white"),
+        "Grade B": ("#f1c40f", "black"),
+        "Grade C": ("#e67e22", "white"),
+        "Grade D": ("#e74c3c", "white")
     }
-    return colors.get(grade_text, ("#bdc3c7", "black")) # Default Abu-abu
+    return colors.get(grade_text, ("#bdc3c7", "black"))
+
 
 def reset_analysis_state():
-    """Mereset state analisis jika gambar baru di-upload."""
-    keys_to_reset = ['ocr_ran', 'extracted_data', 'analysis_done']
-    for key in keys_to_reset:
+    for key in ['ocr_ran', 'extracted_data', 'analysis_done', 'analysis_results']:
         if key in st.session_state:
             del st.session_state[key]
-            
+
 # --- UI APLIKASI ---
 
-# Inisialisasi model OCR saat aplikasi pertama kali dimuat
 ocr_model = initialize_ocr()
 if ocr_model is None:
-    st.error("Aplikasi tidak dapat berjalan tanpa model OCR. Harap segarkan halaman.")
+    st.error("Aplikasi tidak dapat berjalan tanpa model OCR.")
     st.stop()
 
-# Judul dan Deskripsi
 st.title("🥗 Nutri-Grade Label Detection & Grade Calculator")
 st.caption("Aplikasi ini membantu Anda memahami kandungan gizi produk dengan standar Nutri-Grade Singapura.")
 
 with st.expander("📋 Petunjuk Penggunaan & Info"):
     st.markdown("""
-    **Cara Penggunaan:**
-    1. **Upload Gambar:** Unggah foto tabel gizi produk yang jelas.
-    2. **Mulai Analisis:** Klik tombol "Mulai Analisis OCR" untuk mengekstrak data.
-    3. **Koreksi Data:** Periksa dan perbaiki hasil ekstraksi jika ada yang salah.
-    4. **Hitung Grade:** Klik "Hitung Grade" untuk melihat hasil analisis dan saran nutrisi.
-
-    **⚠️ Tolong Diperhatikan:**
-    - Aplikasi ini adalah prototipe, hasil mungkin tidak 100% akurat.
-    - Kualitas gambar sangat mempengaruhi hasil OCR.
-    - Referensi: [Health Promotion Board Singapura](https://www.hpb.gov.sg/docs/default-source/pdf/nutri-grade-ci-guide_eng-only67e4e36349ad4274bfdb22236872336d.pdf)
+    1. Upload gambar tabel gizi.
+    2. Klik "Mulai Analisis OCR".
+    3. Koreksi nilai jika perlu.
+    4. Klik "Hitung Grade" dan lihat saran nutrisi.
     """)
 
-# --- LANGKAH 1: UPLOAD GAMBAR ---
 st.header("1. 📸 Upload Gambar Tabel Gizi")
-uploaded_file = st.file_uploader(
-    "Pilih file gambar (JPG, PNG, JPEG)",
-    type=["jpg", "jpeg", "png"],
-    help="Upload gambar tabel gizi untuk dianalisis.",
-    on_change=reset_analysis_state # Reset state jika file berubah
-)
-
-if uploaded_file is not None:
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-    
-    if img is None:
-        st.error("Gagal memproses file gambar. Silakan coba file lain.")
-        st.stop()
-
-    # Tampilkan gambar yang di-upload
-    st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), caption="Gambar yang diupload", width=300)
-
-    # --- LANGKAH 2: PROSES OCR ---
-    if st.button("Mulai Analisis OCR", type="primary"):
-        with st.spinner("🔍 Menganalisis teks pada gambar..."):
-            try:
-                ocr_result = ocr_model.ocr(img, cls=True)
-                if not ocr_result or not ocr_result[0]:
-                    st.error("OCR tidak dapat menemukan teks pada gambar. Coba gambar yang lebih jelas.")
-                    st.stop()
-
-                # Ekstrak teks dan lokasinya
-                ocr_data = ocr_result[0]
-                extracted_texts = [line[1][0] for line in ocr_data]
-                
-                # Cari kata kunci nutrisi
-                target_keys = {
-                    "gula": "gula|sugar|sugars",
-                    "takaran saji": r"takaran saji|serving size|per serving",
-                    "lemak jenuh": r"lemak jenuh|saturated fat|sat fat"
-                }
-
-                extracted_values = {}
-                # Gabungkan semua teks untuk pencarian yang lebih mudah
-                full_text = " ".join(extracted_texts).lower()
-
-                for key, pattern in target_keys.items():
-                    # Cari nilai setelah kata kunci (misal: "Gula 15g")
-                    match = re.search(f"({pattern})[^\d\n]*([\d\.]+)", full_text, re.IGNORECASE)
-                    if match:
-                        extracted_values[key.replace(" ", "_").capitalize()] = match.group(2)
-                
-                st.session_state.extracted_data = extracted_values
-                st.session_state.ocr_ran = True
-                st.success("Analisis OCR selesai!")
-                time.sleep(1) # Beri jeda agar user melihat pesan sukses
-                st.rerun() # Muat ulang state untuk menampilkan form koreksi
-
-# --- LANGKAH 3: KOREKSI MANUAL & PERHITUNGAN ---
-if st.session_state.get('ocr_ran', False):
+uploaded_file = st.file_uploader("Pilih file JPG/PNG", type=["jpg","jpeg","png"], on_change=reset_analysis_state)
+
+if uploaded_file:
+    img_arr = np.frombuffer(uploaded_file.read(), np.uint8)
+    img = cv2.imdecode(img_arr, cv2.IMREAD_COLOR)
+    st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), width=300)
+
+    if st.button("Mulai Analisis OCR"):
+        with st.spinner("Mendeteksi teks..."):
+            ocr_result = ocr_model.ocr(img, cls=True)
+        if not ocr_result or not ocr_result[0]:
+            st.error("OCR gagal menemukan teks!")
+        else:
+            texts = [line[1][0] for line in ocr_result[0]]
+            full_text = " ".join(texts).lower()
+            patterns = {
+                "Takaran_Saji": r"(takaran saj(i|a)|serving size)[^\d]*(\d+\.?\d*)",
+                "Gula": r"(gula|sugar)[^\d]*(\d+\.?\d*)",
+                "Lemak_Jenuh": r"(lemak jenuh|saturated fat)[^\d]*(\d+\.?\d*)"
+            }
+            extracted = {}
+            for key, pat in patterns.items():
+                m = re.search(pat, full_text)
+                if m:
+                    extracted[key] = m.group(2)
+            st.session_state.extracted_data = extracted
+            st.session_state.ocr_ran = True
+            st.success("OCR selesai!")
+            st.rerun()
+
+if st.session_state.get('ocr_ran'):
     st.header("2. ✏️ Koreksi Data & Hitung Grade")
-    
-    extracted = st.session_state.get('extracted_data', {})
-    
-    with st.form("correction_form"):
-        st.write("Periksa dan koreksi hasil OCR jika diperlukan. Masukkan **hanya angka**.")
-        
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            takaran_saji = st.text_input(
-                "Takaran Saji (g/ml)",
-                value=str(extracted.get("Takaran_saji", "100")),
-                help="Ukuran satu porsi sajian dalam gram atau ml."
-            )
-        with col2:
-            gula = st.text_input(
-                "Gula per Porsi (g)",
-                value=str(extracted.get("Gula", "0")),
-                help="Total gula dalam satu takaran saji."
-            )
-        with col3:
-            lemak_jenuh = st.text_input(
-                "Lemak Jenuh per Porsi (g)",
-                value=str(extracted.get("Lemak_jenuh", "0")),
-                help="Total lemak jenuh dalam satu takaran saji."
-            )
-            
-        submit_button = st.form_submit_button("🧮 Hitung Grade", type="primary", use_container_width=True)
-
-        if submit_button:
-            serving_size = parse_numeric_value(takaran_saji)
-            sugar_value = parse_numeric_value(gula)
-            fat_value = parse_numeric_value(lemak_jenuh)
-
-            if serving_size <= 0:
-                st.error("Takaran Saji harus lebih besar dari 0!")
-            else:
-                # Normalisasi ke per 100g/ml
-                sugar_norm = (sugar_value / serving_size) * 100
-                fat_norm = (fat_value / serving_size) * 100
-                
-                # Simpan hasil ke session state untuk ditampilkan
-                st.session_state.analysis_results = {
-                    "serving_size": serving_size,
-                    "sugar_norm": sugar_norm,
-                    "fat_norm": fat_norm
-                }
-                st.session_state.analysis_done = True
-
-# --- LANGKAH 4: TAMPILKAN HASIL ---
-if st.session_state.get('analysis_done', False):
-    results = st.session_state.analysis_results
-    sugar_norm = results['sugar_norm']
-    fat_norm = results['fat_norm']
-    serving_size = results['serving_size']
-
-    st.header("3. 📈 Hasil Analisis")
-
-    # Hitung Grade
-    thresholds_sugar = {"A": 1.0, "B": 5.0, "C": 10.0}
-    thresholds_fat = {"A": 0.7, "B": 1.2, "C": 2.8}
-    sugar_grade = get_grade_from_value(sugar_norm, thresholds_sugar)
-    fat_grade = get_grade_from_value(fat_norm, thresholds_fat)
-
-    # Tentukan grade akhir (yang terburuk)
-    grade_scores = {"Grade A": 1, "Grade B": 2, "Grade C": 3, "Grade D": 4}
-    final_grade = max(sugar_grade, fat_grade, key=lambda g: grade_scores[g])
-    
-    # Tampilkan Grade
-    st.subheader("🏆 Hasil Grading Produk")
-    col1, col2, col3 = st.columns(3)
-    
-    def display_grade_card(container, title, value, unit, grade):
-        bg_color, text_color = get_grade_color(grade)
-        container.markdown(f"""
-        <div style="background-color: {bg_color}; padding: 15px; border-radius: 10px; text-align: center; color: {text_color}; font-weight: bold; margin: 5px;">
-            <h4 style="margin: 0; color: {text_color};">{title}</h4>
-            <p style="margin: 5px 0; color: {text_color};">{value:.2f} {unit}</p>
-            <h3 style="margin: 0; color: {text_color};">{grade}</h3>
-        </div>
-        """, unsafe_allow_html=True)
-
-    display_grade_card(col1, "Gula", sugar_norm, "g / 100ml", sugar_grade)
-    # BUG FIX: Menggunakan fat_grade, bukan sugar_grade
-    display_grade_card(col2, "Lemak Jenuh", fat_norm, "g / 100ml", fat_grade)
-
-    with col3:
-        bg_color, text_color = get_grade_color(final_grade)
-        st.markdown(f"""
-        <div style="background-color: {bg_color}; padding: 15px; border-radius: 10px; text-align: center; color: {text_color}; font-weight: bold; margin: 5px; border: 3px solid #333;">
-            <h4 style="margin: 0; color: {text_color};">Grade Akhir</h4>
-            <h2 style="margin: 10px 0; color: {text_color};">{final_grade}</h2>
-        </div>
-        """, unsafe_allow_html=True)
-    
+    ext = st.session_state.extracted_data
+    with st.form("form2"):
+        ts = st.text_input("Takaran Saji (g/ml)", value=ext.get('Takaran_Saji','100'))
+        gu = st.text_input("Gula (g)", value=ext.get('Gula','0'))
+        lj = st.text_input("Lemak Jenuh (g)", value=ext.get('Lemak_Jenuh','0'))
+        sb = st.form_submit_button("Hitung Grade")
+    if sb:
+        s_val = parse_numeric_value(ts)
+        g_val = parse_numeric_value(gu)
+        f_val = parse_numeric_value(lj)
+        s_pct = g_val/s_val*100 if s_val>0 else 0
+        f_pct = f_val/s_val*100 if s_val>0 else 0
+        st.session_state.analysis_results = {'s_pct':s_pct,'f_pct':f_pct,'s_val':s_val}
+        st.session_state.analysis_done = True
+
+if st.session_state.get('analysis_done'):
+    r = st.session_state.analysis_results
+    sg = get_grade_from_value(r['s_pct'],{"A":1.0,"B":5.0,"C":10.0})
+    fg = get_grade_from_value(r['f_pct'],{"A":0.7,"B":1.2,"C":2.8})
+    fg_final = max(sg,fg, key=lambda x: ["Grade A","Grade B","Grade C","Grade D"].index(x))
+    st.header("3. 📈 Hasil Grading")
+    c1,c2,c3 = st.columns(3)
+    def card(c,t,v,u,gr):
+        bc,tc = get_grade_color(gr)
+        c.markdown(f"<div style='background:{bc};padding:10px;border-radius:8px;color:{tc};text-align:center;'>"
+                    f"<strong>{t}</strong><p>{v:.2f} {u}</p><h4>{gr}</h4></div>",unsafe_allow_html=True)
+    card(c1,"Gula",r['s_pct'],"g/100ml",sg)
+    card(c2,"Lemak Jenuh",r['f_pct'],"g/100ml",fg)
+    card(c3,"Grade Akhir",0,"",fg_final)
     st.divider()
-
-    # Tampilkan Saran Nutrisi dari AI
-    st.subheader("🤖 Saran Nutrisi dari AI")
-    with st.spinner("Meminta saran dari ahli gizi AI..."):
-        advice = get_nutrition_advice(serving_size, sugar_norm, fat_norm, sugar_grade, fat_grade, final_grade)
-        
-        if advice.startswith("Error"):
-            st.error(advice)
-        else:
-            st.success("Saran berhasil didapatkan!")
-            st.info(advice)
-
-# --- FOOTER ---
+    st.header("4. 🤖 Saran Nutrisi AI")
+    with st.spinner("Meminta saran..."):
+        adv = get_nutrition_advice(r['s_val'],r['s_pct'],r['f_pct'],sg,fg,fg_final)
+    if adv.startswith("Error"):
+        st.error(adv)
+    else:
+        st.info(adv)
+
+# Footer
 st.markdown("---")
-st.markdown("""
-<div style="text-align: center; padding: 10px;">
-    <p><strong>Nutri-Grade Detection App v2.1</strong> | Dikembangkan oleh Tim Nutri-Grade © 2024</p>
-    <small>Powered by PaddleOCR, OpenRouter API, and Streamlit</small>
-</div>
-""", unsafe_allow_html=True)
+st.markdown("<p style='text-align:center;'>Nutri-Grade Detection App v2.1 &copy; 2024</p>", unsafe_allow_html=True)