bug fix

interfaces/productModels.py

@@ -1,4 +1,4 @@
-from typing import List, Dict
+from typing import List, Dict, Optional
 from pydantic import BaseModel
 from datetime import datetime
 
@@ -21,4 +21,41 @@ class ProductCreate(BaseModel):
     timestamp: datetime
     ingredient_ids: List[int]|str
 
+class SafetyScore(BaseModel):
+    isPresent: bool
+    value: Optional[int] = None
+
+class ProductInfo(BaseModel):
+    id: Optional[int] = None
+    name: Optional[str] = None
+    barcode: Optional[str] = None
+    image_url: Optional[str] = None
+    brand: Optional[str] = None
+    manufacturing_places: Optional[str] = None
+    stores: Optional[str] = None
+    countries: Optional[str] = None
+
+class IngredientInfo(BaseModel):
+    ingredients_text: Optional[str] = None
+    ingredients_analysis: Optional[List[Dict[str, Any]]] = None # Adjust type if analysis has a specific structure
+    additives: Optional[List[str]] = None
+
+class AllergenInfo(BaseModel):
+    allergens: Optional[List[str]] = None
+    traces: Optional[List[str]] = None
+
+class DietInfo(BaseModel):
+    vegan: Optional[bool] = None
+    vegetarian: Optional[bool] = None
+
+class ProductAnalysisResponse(BaseModel):
+    found: bool
+    safety_score: SafetyScore
+    product_info: Optional[ProductInfo] = None
+    ingredient_info: Optional[IngredientInfo] = None
+    allergen_info: Optional[AllergenInfo] = None
+    diet_info: Optional[DietInfo] = None
+    nutritional_info: Optional[Dict[str, Any]] = None # Adjust type if nutritional info has a specific structure
+    manufacturing_info: Optional[Dict[str, Any]] = None # Adjust type if manufacturing info has a specific structure
+
 

routers/analysis.py

@@ -8,7 +8,7 @@ import pytz
 from sqlalchemy.orm import Session
 from typing import List, Dict, Any
 from db.models import User, Ingredient
-from interfaces.ingredientModels import IngredientAnalysisResult, IngredientRequest, ProductAnalysisResponse
+from interfaces.ingredientModels import IngredientAnalysisResult, IngredientRequest
 from interfaces.productModels import ProductIngredientsRequest
 from logger_manager import log_info, log_error
 from db.database import get_db,SessionLocal
@@ -19,6 +19,7 @@ from services.ingredientFinderAgent import IngredientInfoAgentLangGraph
 from services.productAnalyzerAgent import analyze_product_ingredients
 from services.auth_service import get_current_user
 from utils.db_utils import ingredient_db_to_pydantic
+from interfaces.productModels import ProductAnalysisResponse
 from services.analysis_service import get_product_data_by_marker_id as get_analysis_service_data
 from utils.ingredient_utils import process_single_ingredient
 

services/analysis_service.py

@@ -2,8 +2,10 @@ from sqlalchemy.orm import Session
 from db.models import Marker, Product
 from utils.analysis_utils import format_product_analysis_response
 from utils.logger_manager import log_info, log_error
+from interfaces.productModels import ProductAnalysisResponse
+from typing import Optional
 
-def get_product_data_by_marker_id(db: Session, target_id: str):
+def get_product_data_by_marker_id(db: Session, target_id: str) -> Optional[ProductAnalysisResponse]:
     """
     Retrieves product analysis and ingredient information by marker ID.
 
@@ -12,8 +14,8 @@ def get_product_data_by_marker_id(db: Session, target_id: str):
         target_id: The target ID from the marker table.
 
     Returns:
-        A dictionary containing product analysis and ingredient information,
-        or None if no product is found.
+        A ProductAnalysisResponse object or None if no product is found.
+
     """
     log_info(f"Attempting to retrieve product data for marker ID: {target_id}")
     try:

utils/analysis_utils.py

@@ -1,61 +1,54 @@
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, Any
+from interfaces.productModels import ProductAnalysisResponse
 
-def format_product_analysis_response(product_data) -> Dict[str, Any]:
+def format_product_analysis_response(product_data: Optional[Any]) -> Optional[ProductAnalysisResponse]:
     """
     Formats the retrieved product analysis data into a consistent response structure.
 
     Args:
-        product_data: A dictionary containing product data, or None if not found.
+        product_data: A SQLAlchemy Product object, or None if not found.
 
     Returns:
-        A dictionary representing the formatted response.
+        A ProductAnalysisResponse object or None.
     """
     if product_data is None:
-        return {
-            "found": False,
-            "safety_score": {"isPresent": False, "value": None},
-            "product_info": None,
-            "ingredient_info": None,
-            "allergen_info": None,
-            "diet_info": None,
-            "nutritional_info": None,
-            "manufacturing_info": None,
-        }
+        return None
 
-    safety_score_value = product_data.safety_score
+    # Assuming product_data is a SQLAlchemy Product object
+    safety_score_value = product_data.overall_safety_score
     safety_score_isPresent = safety_score_value is not None
 
-    return {
-        "found": True,
-        "safety_score": {
+    return ProductAnalysisResponse(
+        found=True,
+        safety_score={
             "isPresent": safety_score_isPresent,
             "value": safety_score_value,
         },
-        "product_info": {
+        product_info= {
             "id": product_data.id,
-            "name": product_data.name,
-            "barcode": product_data.barcode,
-            "image_url": product_data.image_url,
-            "brand": product_data.brand,
-            "manufacturing_places": product_data.manufacturing_places,
-            "stores": product_data.stores,
-            "countries": product_data.countries,
-        } if product_data.id is not None else None,
-        "ingredient_info": {
-            "ingredients_text": product_data.ingredients_text,
-            "ingredients_analysis": product_data.ingredients_analysis,
-            "additives": product_data.additives,
-        } if product_data.ingredients_text is not None else None,
-        "allergen_info": {
-            "allergens": product_data.allergens,
-            "traces": product_data.traces,
-        } if product_data.allergens is not None or product_data.traces is not None else None,
-        "diet_info": {
-            "vegan": product_data.get("vegan"),
-            "vegetarian": product_data.get("vegetarian"),
-        } if product_data.get("vegan") is not None or product_data.get("vegetarian") is not None else None,
-        "nutritional_info": product_data.get("nutritional_info"),  # Assuming this is already a dict
-        "manufacturing_info": product_data.get("manufacturing_info"),  # Assuming this is already a dict
-    }
+            "name": product_data.product_name,
+            "barcode": None, # Assuming barcode is not in Product model
+            "image_url": None, # Assuming image_url is not in Product model
+            "brand": None, # Assuming brand is not in Product model
+            "manufacturing_places": None, # Assuming manufacturing_places is not in Product model
+            "stores": None, # Assuming stores is not in Product model
+            "countries": None, # Assuming countries is not in Product model
+        },
+        ingredient_info={
+            "ingredients_text": product_data.ingredients,
+            "ingredients_analysis": product_data.ingredient_interactions, # Assuming ingredient_interactions maps to analysis
+            "additives": None, # Assuming additives are not directly in Product model
+        },
+        allergen_info= {
+            "allergens": product_data.allergy_warnings,
+            "traces": None, # Assuming traces are not directly in Product model
+        },
+        diet_info={
+            "vegan": None, # Assuming vegan is not directly in Product model
+            "vegetarian": None, # Assuming vegetarian is not directly in Product model
+        },
+        nutritional_info=None, # Assuming nutritional_info is not directly in Product model
+        manufacturing_info=None # Assuming manufacturing_info is not directly in Product model
+    )
 
 # Add other helper functions as needed for analysis