Update app.py

app.py

@@ -4,7 +4,7 @@ from google import genai
 from google.genai import types 
 import os
 import json
-import re
+import re # Import Regex untuk "mencuri" angka dari teks
 import random
 from dotenv import load_dotenv
 import requests
@@ -133,28 +133,17 @@ def format_user_context(context_data):
 2. DAILY STATUS: Target Calories: {stats.get('target_calories', 2000)} kcal, CONSUMED: {stats.get('consumed_calories', 0)} kcal.
 """
 
-# --- NEW HELPER: SUPER ROBUST NUMBER EXTRACTOR ---
 def get_safe_float(data, targets):
-    """
-    Mencari nilai float dari dict SECARA CASE-INSENSITIVE.
-    Juga menangani jika nilai berupa string (e.g., "350 kcal").
-    """
-    # 1. Normalkan semua key di data menjadi huruf kecil
+    """Mencari nilai float dari dict SECARA CASE-INSENSITIVE."""
     data_lower = {k.lower(): v for k, v in data.items()}
-    
     for t in targets:
-        # Cek target (huruf kecil)
         if t in data_lower:
             val = data_lower[t]
             try:
-                # Jika angka langsung
-                if isinstance(val, (int, float)):
-                    return float(val)
-                # Jika string ("350 kcal"), ambil angkanya saja
+                if isinstance(val, (int, float)): return float(val)
                 if isinstance(val, str):
                     nums = re.findall(r"[-+]?\d*\.\d+|\d+", val)
-                    if nums:
-                        return float(nums[0])
+                    if nums: return float(nums[0])
             except:
                 continue
     return 0.0
@@ -165,7 +154,7 @@ def get_safe_float(data, targets):
 def home():
     return jsonify({
         "status": "online",
-        "message": "GastroGuard AI Backend is Running (Case-Insensitive Mode)!",
+        "message": "GastroGuard AI Backend (Regex Fallback Enabled)",
         "endpoints": ["/analyze-text", "/analyze-image", "/chat"]
     })
 
@@ -220,12 +209,10 @@ OUTPUT JSON FORMAT:
         )
         
         result = json.loads(clean_json_text(response.text))
-        
         food_data = result.get("data_makanan", {})
         nutrisi = food_data.get("nutrisi", {})
         decision = result.get("keputusan_sistem", {})
         
-        # Ekstraksi Aman (Bilingual & Case Insensitive)
         cal = get_safe_float(nutrisi, ["kalori", "calories", "energy", "kcal"])
         prot = get_safe_float(nutrisi, ["protein", "protien"])
         carb = get_safe_float(nutrisi, ["karbohidrat", "carbs", "carbohydrate", "karbo"])
@@ -315,7 +302,6 @@ OUTPUT SCHEMA (STRICT):
         nutrisi = food_data.get("nutrisi", {})
         decision = result.get("keputusan_sistem", {})
         
-        # Ekstraksi Aman
         cal = get_safe_float(nutrisi, ["kalori", "calories", "energy", "kcal"])
         prot = get_safe_float(nutrisi, ["protein"])
         carb = get_safe_float(nutrisi, ["karbohidrat", "carbs"])
@@ -361,10 +347,9 @@ CONTEXT: {user_context_str}
 USER MESSAGE: "{message}"
 
 MANDATORY INSTRUCTIONS:
-1. If the user mentions ANY food name (e.g., "I want to eat X", "X calories?", "Eating X"):
-   - YOU MUST ESTIMATE THE NUTRITION FACTS in 'data_makanan'.
-   - DO NOT leave calorie values as 0.
-   - FILL 'nama_menu' with the specific food name.
+1. If the user mentions ANY food name (e.g., "I want to eat X", "X calories?"):
+   - IGNORE Safety checks for a moment, and FOCUS ON ESTIMATING NUTRITION in 'data_makanan'.
+   - ESTIMATE VALUES EVEN IF UNSURE.
 2. If no food is mentioned, keep nutrition 0.
 3. RETURN JSON ONLY.
 
@@ -393,22 +378,36 @@ OUTPUT JSON SCHEMA:
         base_reply = result.get("chat_response", "")
         food_data = result.get("data_makanan", {})
         nutrisi = food_data.get("nutrisi", {})
+        decision = result.get("keputusan_sistem", {})
         
-        # --- DEBUG PRINT (WAJIB DILIHAT DI LOGS) ---
-        print(f"DEBUG CHAT JSON: {json.dumps(result, indent=2)}")
-
-        # --- FIX UTAMA: EKSTRAKSI AMAN ---
+        # 1. Cek JSON Normal
         cal_val = get_safe_float(nutrisi, ["kalori", "calories", "energy", "kcal"])
         prot = get_safe_float(nutrisi, ["protein", "protien"])
         carb = get_safe_float(nutrisi, ["karbohidrat", "carbs", "carbohydrate", "gula"])
         fat = get_safe_float(nutrisi, ["lemak_total", "fat", "fats", "lemak"])
         
         menu_name = food_data.get("nama_menu")
-        
-        # LOGIKA DISPLAY: Jika kalori > 0, TEMPEL TEKS.
+
+        # 2. JURUS PAMUNGKAS (REGEX FALLBACK)
+        # Jika JSON kosong (0), tapi AI nulis angka di teks (misal: "Adding 350 kcal..."), kita curi angkanya.
+        if cal_val == 0:
+            combined_text = base_reply + " " + decision.get("alasan_utama", "")
+            # Cari pola angka + kcal/cal/calories
+            found_cals = re.findall(r"(\d+)\s*(?:kcal|cal|calories)", combined_text, re.IGNORECASE)
+            if found_cals:
+                cal_val = float(found_cals[0]) # Ambil angka pertama yang ketemu
+                if not menu_name: # Jika nama menu kosong, coba ambil dari chat
+                    menu_name = "Detected Food" 
+
+        # 3. LOGIKA DISPLAY: Jika kalori > 0 (baik dari JSON atau curian), TEMPEL TEKS.
         if cal_val > 0:
              display_name = menu_name if menu_name else "Food"
-             nutrition_text = f"\n\n📊 **{display_name} Info:**\n🔥 {int(cal_val)} kcal | 🥩 P: {int(prot)}g | 🍞 C: {int(carb)}g | 🥑 F: {int(fat)}g"
+             # Jika protein/carb/fat masih 0 (karena hasil curian regex cuma kalori), set "-" agar rapi
+             p_str = f"{int(prot)}g" if prot > 0 else "?"
+             c_str = f"{int(carb)}g" if carb > 0 else "?"
+             f_str = f"{int(fat)}g" if fat > 0 else "?"
+             
+             nutrition_text = f"\n\n📊 **{display_name} Info:**\n🔥 {int(cal_val)} kcal | 🥩 P: {p_str} | 🍞 C: {c_str} | 🥑 F: {f_str}"
              final_reply = base_reply + nutrition_text
         else:
              final_reply = base_reply
@@ -420,7 +419,7 @@ OUTPUT JSON SCHEMA:
             "protein": prot,
             "carbs": carb,
             "fat": fat,
-            "health_tip": result.get("keputusan_sistem", {}).get("alasan_utama", "")
+            "health_tip": decision.get("alasan_utama", "")
         }
         return jsonify(mapped_result)