V2.1

api/gemini_service.py

@@ -28,13 +28,15 @@ class GeminiService:
         # Use free tier model with generous quotas
         self.model = "gemini-flash-latest"  # Free tier: 15 RPM, 1M tokens/day
 
-    def process_voice_command(self, audio_bytes, mime_type="audio/mp3"):
+    def process_voice_command(self, audio_bytes, mime_type="audio/mp3", context_products=None):
         """
         Process audio bytes to extract transaction details.
         Returns a dictionary with transcription and structured data.
         """
-        
-        prompt = """
+        if context_products is None:
+            context_products = []
+
+        prompt = f"""
         You are an AI assistant for a financial app called Akompta.
         Your task is to listen to the user's voice command and extract transaction details.
         
@@ -42,30 +44,37 @@ class GeminiService:
         - "J'ai vendu la tomate pour 500FCFA le Kilo" (Income)
         - "J'ai payé un ordinateur à 300000FCFA" (Expense)
         
+        Here is the list of products currently in the user's inventory:
+        {json.dumps(context_products)}
+        
+        If the user mentions a product from this list but doesn't specify the price, use the price from the list to calculate the total amount (amount = quantity * price).
+        
         Please perform the following:
         1. Transcribe the audio exactly as spoken (in French).
         2. Analyze the intent and extract structured data.
         
         Return ONLY a JSON object with the following structure:
-        {
+        {{
             "transcription": "The exact transcription",
             "intent": "create_transaction",
-            "data": {
+            "data": {{
                 "type": "income" or "expense",
                 "amount": number (e.g. 500),
                 "currency": "FCFA" or other,
                 "category": "Category name (e.g. Vente, Alimentation, Transport, Technologie)",
-                "name": "Description of the item or service",
+                "name": "Description of the item or service (e.g. 'Vente de 3 Mangues')",
                 "date": "YYYY-MM-DD" or null if not specified (assume today if null)
-            }
-        }
+            }}
+        }}
+        
+        Important: If a product price is found in the inventory list, ALWAYS calculate: amount = quantity * unit_price.
         
         If the audio is not clear or not related to a transaction, return:
-        {
+        {{
             "transcription": "...",
             "intent": "unknown",
             "error": "Reason"
-        }
+        }}
         """
 
         try:
@@ -96,11 +105,14 @@ class GeminiService:
                 "error": str(e)
             }
 
-    def process_text_command(self, text):
+    def process_text_command(self, text, context_products=None):
         """
         Process text input to extract transaction details.
         """
-        prompt = """
+        if context_products is None:
+            context_products = []
+
+        prompt = f"""
         You are an AI assistant for a financial app called Akompta.
         Your task is to analyze the user's text command and extract transaction details.
         
@@ -109,45 +121,53 @@ class GeminiService:
         - "J'ai payé un ordinateur à 300000FCFA" (Expense)
         - "Ajoute un produit Tomate à 200FCFA le bol, j'en ai 30 en stock" (Create Product)
         
+        Here is the list of products currently in the user's inventory:
+        {json.dumps(context_products)}
+        
+        If the user mentions a product from this list but doesn't specify the price, use the price from the list to calculate the total amount (amount = quantity * price).
+        Example: If "Mangue" is in the list at 100 FCFA and the user says "vendu 3 mangues", the amount should be 300.
+        
         Please perform the following:
         1. Analyze the intent and extract structured data.
         
         Return ONLY a JSON object with the following structure:
         
         For transactions:
-        {
+        {{
             "transcription": "The input text",
             "intent": "create_transaction",
-            "data": {
+            "data": {{
                 "type": "income" or "expense",
                 "amount": number,
                 "currency": "FCFA",
                 "category": "Category name",
-                "name": "Description",
+                "name": "Description (e.g. 'Vente de 3 Mangues')",
                 "date": "YYYY-MM-DD"
-            }
-        }
+            }}
+        }}
+        
+        Important: If a product price is found in the inventory list, ALWAYS calculate: amount = quantity * unit_price.
         
         For products/inventory:
-        {
+        {{
             "transcription": "The input text",
             "intent": "create_product",
-            "data": {
+            "data": {{
                 "name": "Product name",
                 "price": number,
                 "unit": "Unit (e.g. bol, kg, unit)",
                 "description": "Short description",
                 "category": "vente" or "depense" or "stock",
                 "stock_status": "ok" or "low" or "rupture"
-            }
-        }
+            }}
+        }}
         
         If the text is not clear, return:
-        {
+        {{
             "transcription": "...",
             "intent": "unknown",
             "error": "Reason"
-        }
+        }}
         """
 
         try:

api/migrations/0005_user_initial_balance.py

@@ -0,0 +1,19 @@
+# Generated by Django 5.2.8 on 2026-01-22 21:34
+
+from decimal import Decimal
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('api', '0004_aiinsight'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='user',
+            name='initial_balance',
+            field=models.DecimalField(decimal_places=2, default=Decimal('0.00'), max_digits=15),
+        ),
+    ]

api/models.py

@@ -53,6 +53,7 @@ class User(AbstractUser):
         default='personal'
     )
     is_premium = models.BooleanField(default=False)
+    initial_balance = models.DecimalField(max_digits=15, decimal_places=2, default=Decimal('0.00'))
     
     # Champs Business
     business_name = models.CharField(max_length=255, blank=True)

api/serializers.py

@@ -14,7 +14,7 @@ class UserSerializer(serializers.ModelSerializer):
         model = User
         fields = [
             'id', 'email', 'first_name', 'last_name', 'phone_number',
-            'avatar', 'account_type', 'is_premium',
+            'avatar', 'account_type', 'is_premium', 'initial_balance',
             'business_name', 'sector', 'location', 'ifu', 'business_logo',
             'currency', 'language', 'dark_mode',
             'created_at', 'updated_at'

api/views.py

@@ -285,7 +285,7 @@ class TransactionViewSet(viewsets.ModelViewSet):
             user=user, type='expense'
         ).aggregate(total=Sum('amount'))['total'] or Decimal('0.00')
         
-        balance = total_income - total_expenses
+        balance = user.initial_balance + total_income - total_expenses
         
         # Variations en %
         def calc_variation(current, previous):
@@ -449,7 +449,7 @@ class AnalyticsView(APIView):
         transactions = Transaction.objects.filter(user=user).order_by('date')
         
         history = []
-        running_balance = Decimal('0.00')
+        running_balance = user.initial_balance
         
         # Grouper par date pour éviter d'avoir trop de points si plusieurs transactions le même jour
         daily_balances = {}
@@ -598,12 +598,20 @@ class VoiceCommandView(APIView):
         try:
             service = GeminiService()
             
+            # Fetch user products for context
+            user_products = Product.objects.filter(user=request.user)
+            products_list = [
+                {"name": p.name, "price": float(p.price), "unit": p.unit} 
+                for p in user_products
+            ]
+            print(f"VoiceCommandView - Context Products: {products_list}")
+            
             if audio_file:
                 audio_bytes = audio_file.read()
                 mime_type = audio_file.content_type or 'audio/mp3'
-                result = service.process_voice_command(audio_bytes, mime_type)
+                result = service.process_voice_command(audio_bytes, mime_type, context_products=products_list)
             else:
-                result = service.process_text_command(text_command)
+                result = service.process_text_command(text_command, context_products=products_list)
             
             print(f"VoiceCommandView - Result Intent: {result.get('intent')}")