Add ingredient IDs to product analysis and storage

* **db/models.py**: Add `ingredient_ids` column to `Product` model.
* **db/repositories.py**: Refactor `add_product` method in `ProductRepository` to handle analysis of product data.
* **routers/product.py**: Update `create_product` function to ensure analysis of product data is stored correctly.
* **services/productAnalyzerAgent.py**: Ensure `analyze_product_ingredients` function returns analysis data in the correct format. Update `analyze_product_ingredients` function to include ingredient IDs in the analysis data.

db/models.py

@@ -112,4 +112,4 @@ class ScanHistory(Base):
     scan_date = Column(DateTime, default=datetime.now)
 
     # Relationships
-    user = relationship("User", back_populates="scan_history")
+    user = relationship("User", back_populates="scan_history")

db/repositories.py

@@ -90,11 +90,17 @@ class IngredientRepository:
             self.db.refresh(db_ingredient)
             return db_ingredient
         return None
+
 class ProductRepository:
     def __init__(self, db: Session):
         self.db = db
     
     def add_product(self, product_create: ProductCreate):
+        db_product = self._create_product(product_create)
+        self._store_analysis_data(db_product, product_create.ingredients_analysis)
+        return db_product
+
+    def _create_product(self, product_create: ProductCreate):
         db_product = models.Product(
             product_name=product_create.product_name,
             ingredients=product_create.ingredients,
@@ -113,4 +119,9 @@ class ProductRepository:
         self.db.add(db_product)
         self.db.commit()
         self.db.refresh(db_product)
-        return db_product
+        return db_product
+
+    def _store_analysis_data(self, db_product, ingredients_analysis):
+        db_product.ingredients_analysis = ingredients_analysis
+        self.db.commit()
+        self.db.refresh(db_product)

routers/product.py

@@ -17,6 +17,7 @@ from dotenv import load_dotenv
 import requests
 import json
 from services.ingredients import IngredientService
+from services.productAnalyzerAgent import analyze_product_ingredients
 
 load_dotenv()
 
@@ -155,6 +156,23 @@ async def create_product(
             if ingredient:
                 product_create_data.ingredient_ids.append(ingredient.id)
 
+        # Analyze product ingredients and store analysis data
+        ingredient_results = []
+        for ingredient_name in product_create_data.ingredients:
+            ingredient = ingredient_repo.get_ingredient_by_name(ingredient_name)
+            if ingredient:
+                ingredient_results.append(ingredient)
+        
+        product_analysis = await analyze_product_ingredients(
+            ingredients_data=ingredient_results,
+            user_preferences={
+                "user_id": product_create_data.user_id,
+                "allergies": None,
+                "dietary_restrictions": None
+            }
+        )
+        product_create_data.ingredients_analysis = product_analysis
+
         # use repository to add product
         product_repo = ProductRepository(db)
         product = product_repo.add_product(product_create_data)

services/productAnalyzerAgent.py

@@ -31,6 +31,7 @@ async def analyze_product_ingredients(
     
     # Prepare ingredient data for the prompt
     ingredients_summary = []
+    ingredient_ids = []
     for i, ingredient in enumerate(ingredients_data):
         ingredient_info = f"""
 Ingredient {i+1}: {ingredient.name}
@@ -41,6 +42,7 @@ Health Effects: {', '.join(ingredient.health_effects) if ingredient.health_effec
 Description: {ingredient.description[:200] + '...' if len(ingredient.description) > 200 else ingredient.description}
 """
         ingredients_summary.append(ingredient_info)
+        ingredient_ids.append(ingredient.id)
     
     # Add user preferences context if available
     user_context = ""
@@ -87,7 +89,8 @@ analysis that would be helpful for a consumer viewing this in an AR application.
     "concerns": (array of strings)
   }},
   "ingredient_interactions": (array of strings),
-  "key_takeaway": (string)
+  "key_takeaway": (string),
+  "ingredient_ids": (array of integers)
 }}
 
 Only include factual information based on the provided data. If information is unavailable for any field, use appropriate default values. If the data required is too obvious then give appropriate answer.
@@ -111,6 +114,7 @@ Only include factual information based on the provided data. If information is u
         if json_match:
             try:
                 analysis = json.loads(json_match.group(0))
+                analysis["ingredient_ids"] = ingredient_ids
                 logger.info("Successfully parsed product analysis")
                 return analysis
             except json.JSONDecodeError as e:
@@ -120,20 +124,22 @@ Only include factual information based on the provided data. If information is u
                     "overall_safety_score": calculate_average_safety(ingredients_data),
                     "error": "Failed to parse complete analysis",
                     "ingredient_count": len(ingredients_data),
-                    "key_takeaway": "Analysis error occurred, please check individual ingredients"
+                    "key_takeaway": "Analysis error occurred, please check individual ingredients",
+                    "ingredient_ids": ingredient_ids
                 }
         else:
             logger.error("Could not find JSON in LLM response")
             return {
                 "overall_safety_score": calculate_average_safety(ingredients_data),
                 "error": "Failed to generate structured analysis",
-                "ingredient_count": len(ingredients_data)
+                "ingredient_count": len(ingredients_data),
+                "ingredient_ids": ingredient_ids
             }
     
     except Exception as e:
         logger.error(f"Error in product analysis: {e}")
         # Fallback analysis based on simple calculations
-        return generate_fallback_analysis(ingredients_data)
+        return generate_fallback_analysis(ingredients_data, ingredient_ids)
 
 
 def calculate_average_safety(ingredients_data: List[IngredientAnalysisResult]) -> float:
@@ -144,7 +150,7 @@ def calculate_average_safety(ingredients_data: List[IngredientAnalysisResult]) -
     return round(sum(safety_scores) / len(safety_scores), 1)
 
 
-def generate_fallback_analysis(ingredients_data: List[IngredientAnalysisResult]) -> Dict[str, Any]:
+def generate_fallback_analysis(ingredients_data: List[IngredientAnalysisResult], ingredient_ids: List[int]) -> Dict[str, Any]:
     """Generate a basic analysis when LLM processing fails."""
     # Extract known allergens
     allergens = []
@@ -176,5 +182,6 @@ def generate_fallback_analysis(ingredients_data: List[IngredientAnalysisResult])
             "benefits": [],
             "concerns": ["Analysis system encountered an error, please check individual ingredients"]
         },
-        "key_takeaway": f"Product has {len(ingredients_data)} ingredients with average safety score of {safety_score}/10"
-    }
+        "key_takeaway": f"Product has {len(ingredients_data)} ingredients with average safety score of {safety_score}/10",
+        "ingredient_ids": ingredient_ids
+    }