Update app.py

app.py

@@ -1,7 +1,7 @@
 from flask import Flask, request, jsonify
 from flask_cors import CORS
 from google import genai
-from google.genai import types # PENTING: Import types untuk konfigurasi JSON
+from google.genai import types 
 import os
 import json
 import random
@@ -29,7 +29,6 @@ FS_TOKEN_EXPIRY = 0
 # --- HELPER FUNCTIONS ---
 
 def clean_json_text(text):
-    """Membersihkan markdown ```json ... ``` jika AI tidak sengaja mengirimnya"""
     text = text.strip()
     if text.startswith("```"):
         parts = text.split("\n", 1)
@@ -153,7 +152,7 @@ def format_user_context(context_data):
 def home():
     return jsonify({
         "status": "online",
-        "message": "GastroGuard AI Backend is Running (Stable JSON Mode)!",
+        "message": "GastroGuard AI Backend is Running (Aggressive Data Mode)!",
         "endpoints": ["/analyze-text", "/analyze-image", "/chat"]
     })
 
@@ -166,13 +165,11 @@ def analyze_text():
     if not query:
         return jsonify({"error": "No query provided"}), 400
 
-    # 1. Try FatSecret First
     if FS_CLIENT_ID and FS_CLIENT_SECRET:
          fs_data = search_fatsecret(query)
          if fs_data:
              return jsonify(fs_data)
 
-    # 2. Check API Key
     if not client:
          return jsonify(mock_analyze_food(query))
         
@@ -180,18 +177,14 @@ def analyze_text():
         user_context_str = format_user_context(user_context)
         
         prompt_content = f"""
-ROLE:
-You are GastroGuard AI. You are a STRICT CALORIE LOGGING ASSISTANT.
-
-CONTEXT:
-{user_context_str}
+ROLE: You are GastroGuard AI. STRICT CALORIE LOGGING ASSISTANT.
+CONTEXT: {user_context_str}
 USER INPUT: "{query}"
 
 INSTRUCTIONS:
 1. Identify if the user mentioned a food.
 2. If yes, ESTIMATE the nutrition facts accurately.
-3. If no (e.g. general chat), keep nutrition values at 0.
-4. Your 'chat_response' must be friendly but SHORT (max 2 sentences).
+3. Your 'chat_response' must be friendly but SHORT.
 
 OUTPUT JSON FORMAT:
 {{
@@ -220,7 +213,6 @@ OUTPUT JSON FORMAT:
         
         result = json.loads(clean_json_text(response.text))
         
-        # Mapping
         food_data = result.get("data_makanan", {})
         nutrisi = food_data.get("nutrisi", {})
         decision = result.get("keputusan_sistem", {})
@@ -271,7 +263,6 @@ def analyze_image():
             user_context = {}  
         user_context_str = format_user_context(user_context)
 
-        # 1. System Instruction (Aturan Main)
         system_instruction_text = f"""
 SYSTEM OVERRIDE: YOU ARE A JSON-ONLY API.
 ROLE: GastroGuard AI Vision Engine.
@@ -301,21 +292,17 @@ OUTPUT SCHEMA (STRICT):
   }}
 }}
 """
-
-        # 2. Build Content Parts
         request_contents = [
-            system_instruction_text,  # Part 1: Instruksi
-            types.Part.from_bytes(    # Part 2: Gambar
+            system_instruction_text,
+            types.Part.from_bytes(
                 data=image_bytes,
                 mime_type=file.content_type or "image/jpeg"
             )
         ]
 
-        # Part 3: User Query
         if user_prompt:
              request_contents.append(f"USER QUERY: {user_prompt}")
 
-        # 3. Execute with JSON Config
         response_vision = client.models.generate_content(
             model="gemini-2.5-flash",
             contents=request_contents,
@@ -328,14 +315,12 @@ OUTPUT SCHEMA (STRICT):
         text_res = clean_json_text(response_vision.text)
         result = json.loads(text_res)
 
-        # 4. Mapping Result
         food_data = result.get("data_makanan", {})
         nutrisi = food_data.get("nutrisi", {})
         decision = result.get("keputusan_sistem", {})
         
         health_msg = f"[{decision.get('safety_score', 'Info')}] {decision.get('alasan_utama', '')}"
         
-        # APPEND LENGKAP MACROS KE TEKS
         nutrition_text = f"\n\n📊 **{food_data.get('nama_menu', 'Food')} Info:**\n🔥 {nutrisi.get('kalori', 0)} kcal | 🥩 P: {nutrisi.get('protein', 0)}g | 🍞 C: {nutrisi.get('karbohidrat', 0)}g | 🥑 F: {nutrisi.get('lemak_total', 0)}g"
         
         final_reply = result.get("chat_response", "Food detected.") + nutrition_text
@@ -377,20 +362,22 @@ def chat():
     try:
         user_context_str = format_user_context(user_context)
         
-        # --- PERBAIKAN PROMPT CHAT (DIPERKETAT) ---
+        # --- PERBAIKAN LOGIKA CHAT ---
+        # Prompt ini memaksa AI untuk "EXTRACT DATA" jika ada nama makanan
+        # meskipun user menggunakan kata "mau" (intent).
         prompt_content = f"""
 ROLE: GastroGuard AI (Health & Nutrition Expert).
 CONTEXT: {user_context_str}
 USER MESSAGE: "{message}"
 
-INSTRUCTIONS:
-1. Analyze the user's message.
-2. IF USER MENTIONS A FOOD or intent to eat (e.g., "I want to eat ayam geprek", "Nasi goreng calories?"):
-   - IDENTIFY the food name.
-   - ESTIMATE nutritional values (Calories, Protein, Carbs, Fat) based on standard serving.
-   - DO NOT leave values as 0. ESTIMATION IS MANDATORY.
-3. IF GENERAL CHAT (No food mentioned): Keep nutrition values at 0.
-4. Output MUST be valid JSON.
+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.
+   - IGNORE verbs like "want to" or "planning to". If food is named, DATA IS REQUIRED.
+2. If no food is mentioned (e.g., "Hello", "My stomach hurts"), keep nutrition 0.
+3. RETURN JSON ONLY.
 
 OUTPUT JSON SCHEMA:
 {{
@@ -411,8 +398,7 @@ OUTPUT JSON SCHEMA:
             contents=prompt_content,
             config=types.GenerateContentConfig(
                 response_mime_type="application/json",
-                # Temperature diturunkan agar lebih patuh pada estimasi data
-                temperature=0.3 
+                temperature=0.3 # Temperature rendah = patuh instruksi
             )
         )
         
@@ -422,17 +408,22 @@ OUTPUT JSON SCHEMA:
         food_data = result.get("data_makanan", {})
         nutrisi = food_data.get("nutrisi", {})
         
-        # --- PERBAIKAN DISPLAY OUTPUT CHAT ---
-        # Jika ada makanan terdeteksi, tambahkan info makro lengkap ke teks balasan
-        # agar user bisa lihat di chat bubble.
-        if food_data.get("nama_menu") and nutrisi.get("kalori", 0) > 0:
-             nutrition_text = f"\n\n📊 **{food_data.get('nama_menu')} Info:**\n🔥 {nutrisi.get('kalori')} kcal | 🥩 P: {nutrisi.get('protein', 0)}g | 🍞 C: {nutrisi.get('karbohidrat', 0)}g | 🥑 F: {nutrisi.get('lemak_total', 0)}g"
+        # --- PYTHON LOGIC FIX ---
+        # Pastikan kalori dianggap angka (bukan string) untuk pengecekan
+        try:
+            cal_val = float(nutrisi.get("kalori", 0))
+        except:
+            cal_val = 0
+
+        # Jika ada nama menu DAN kalori > 0, tempelkan teks nutrisi
+        if food_data.get("nama_menu") and cal_val > 0:
+             nutrition_text = f"\n\n📊 **{food_data.get('nama_menu')} Info:**\n🔥 {cal_val} kcal | 🥩 P: {nutrisi.get('protein', 0)}g | 🍞 C: {nutrisi.get('karbohidrat', 0)}g | 🥑 F: {nutrisi.get('lemak_total', 0)}g"
              final_reply += nutrition_text
 
         mapped_result = {
             "reply": final_reply,
             "food_name": food_data.get("nama_menu"),
-            "calories": nutrisi.get("kalori", 0),
+            "calories": cal_val,
             "protein": nutrisi.get("protein", 0),
             "carbs": nutrisi.get("karbohidrat", 0),
             "fat": nutrisi.get("lemak_total", 0),