Merge branch 'main' of https://github.com/prathameshks/FoodAnalyzer-API

alembic.ini

@@ -63,7 +63,6 @@ version_path_separator = os
 # are written from script.py.mako
 # output_encoding = utf-8
 
-# sqlalchemy.url = postgresql://admin:kMUY6qMLce2fwMiocvf3cjxJLyeAOR1y@dpg-cvs9ajeuk2gs739pi5b0-a.singapore-postgres.render.com/food_analyzer_db
 sqlalchemy.url = 
 
 [post_write_hooks]

db/models.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Integer, String, Boolean, Text, JSON, ForeignKey, DateTime
+from sqlalchemy import Column, Integer, String, Boolean, Text, JSON, ForeignKey, DateTime, text,TIMESTAMP
 from sqlalchemy.orm import relationship, Mapped, mapped_column
 from sqlalchemy.sql import func
 from .database import Base
@@ -10,15 +10,16 @@ class Ingredient(Base):
     __tablename__ = "ingredients"
     
     id = Column(Integer, primary_key=True, index=True)
-    name = Column(String, unique=True, index=True)
-    alternate_names = Column(JSON, nullable=True)
+    name = Column(String(255), unique=True, index=True)
+    alternate_names = Column(Text, nullable=True)
     safety_rating = Column(Integer, nullable=True)
     description = Column(Text, nullable=True)
-    health_effects = Column(JSON, nullable=True)
-    allergic_info = Column(JSON, nullable=True)
-    diet_type = Column(String, nullable=True)  
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+    health_effects = Column(Text, nullable=True)
+    allergic_info = Column(Text, nullable=True)
+    diet_type = Column(String(255), nullable=True)  
+    # Fix the default timestamp for MySQL
+    created_at = Column(TIMESTAMP, server_default=text('CURRENT_TIMESTAMP'))
+    updated_at = Column(DateTime(timezone=True),nullable=True)
     
     # Relationships
     sources = relationship("IngredientSource", back_populates="ingredient")
@@ -28,44 +29,46 @@ class IngredientSource(Base):
     
     id = Column(Integer, primary_key=True, index=True)
     ingredient_id = Column(Integer, ForeignKey("ingredients.id"))
-    source_name = Column(String, nullable=False)
+    source_name = Column(String(255), nullable=False)
     found = Column(Boolean, default=False)
     summary = Column(Text, nullable=True)
-    data = Column(JSON, nullable=True)
+    data = Column(Text, nullable=True)
     
     # Relationships
     ingredient = relationship("Ingredient", back_populates="sources")
     
 class Marker(Base):
     __tablename__ = "markers"
-    id: Mapped[int] = mapped_column(primary_key=True)
-    image_name: Mapped[str]
-    vuforia_id: Mapped[str]
-    product_id: Mapped[int] = mapped_column(ForeignKey("products.id"))
-
-    product: Mapped["Product"] = relationship(back_populates="markers")
+    
+    id = Column(Integer, primary_key=True, index=True)
+    image_name = Column(String(255), nullable=False)
+    vuforia_id = Column(String(255), nullable=False)
+    product_id = Column(Integer, ForeignKey("products.id"))
 
+    # Traditional relationship syntax
+    product = relationship("Product", back_populates="markers")
+    
 class Product(Base):
     
     __tablename__ = "products"
 
     id = Column(Integer, primary_key=True, index=True)
-    product_name = Column(String, nullable=False)
-    ingredients = Column(JSON, nullable=True)
-    ingredients_analysis = Column(JSON, nullable=True)
+    product_name = Column(String(255), nullable=False)
+    ingredients = Column(Text, nullable=True)
+    ingredients_analysis = Column(Text, nullable=True)
     overall_safety_score = Column(Integer, nullable=True)
-    suitable_diet_types = Column(String, nullable=True)
-    allergy_warnings = Column(JSON, nullable=True)
-    usage_recommendations = Column(String, nullable=True)
-    health_insights = Column(JSON, nullable=True)
-    ingredient_interactions = Column(JSON, nullable=True)
-    key_takeaway = Column(String, nullable=True)
+    suitable_diet_types = Column(String(255), nullable=True)
+    allergy_warnings = Column(Text, nullable=True)
+    usage_recommendations = Column(Text, nullable=True)
+    health_insights = Column(Text, nullable=True)
+    ingredient_interactions = Column(Text, nullable=True)
+    key_takeaway = Column(Text, nullable=True)
     ingredients_count = Column(Integer, nullable=True)
     user_id = Column(Integer, nullable=True)
     timestamp = Column(DateTime, nullable=True)
-    ingredient_ids= Column(JSON, nullable=True)
+    ingredient_ids= Column(Text, nullable=True)
     
-    data_quality_warnings = Column(JSON, nullable=True)
+    data_quality_warnings = Column(Text, nullable=True)
     markers: Mapped[List["Marker"]] = relationship(back_populates="product")
 
 
@@ -73,9 +76,9 @@ class User(Base):
     __tablename__ = "users"
 
     id = Column(Integer, primary_key=True, index=True)
-    name = Column(String, unique=False, index=False, nullable=False)
-    email = Column(String, unique=True, index=True, nullable=False)
-    hashed_password = Column(String, nullable=False)
+    name = Column(String(255), unique=False, index=False, nullable=False)
+    email = Column(String(255), unique=True, index=True, nullable=False)
+    hashed_password = Column(String(255), nullable=False)  
     is_active = Column(Boolean, default=True)
     
     # Relationships
@@ -95,10 +98,10 @@ class UserPreferences(Base):
 
     id = Column(Integer, primary_key=True, index=True)
     user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"))
-    dietary_restrictions = Column(String, nullable=True)
-    allergens = Column(String, nullable=True)
-    preferred_ingredients = Column(String, nullable=True)
-    disliked_ingredients = Column(String, nullable=True)
+    dietary_restrictions = Column(String(255), nullable=True)
+    allergens = Column(Text, nullable=True)
+    preferred_ingredients = Column(Text, nullable=True)
+    disliked_ingredients = Column(Text, nullable=True)
 
     # Relationships
     user = relationship("User", back_populates="preferences")
@@ -112,4 +115,4 @@ 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

@@ -1,6 +1,8 @@
 from sqlalchemy.orm import Session
 from sqlalchemy import cast, or_, String
 from sqlalchemy.dialects.postgresql import JSONB
+
+from logger_manager import log_debug, log_error
 from . import models
 from interfaces.ingredientModels import IngredientAnalysisResult 
 from interfaces.productModels import ProductCreate
@@ -14,6 +16,7 @@ class IngredientRepository:
         exact_match = self.db.query(models.Ingredient).filter(models.Ingredient.name.ilike(name)).first()
     
         if exact_match:
+            log_debug(f"Exact match found for ingredient: {name}")
             return exact_match
         
         # If no exact match, try searching in alternate names
@@ -23,10 +26,12 @@ class IngredientRepository:
                 models.Ingredient.alternate_names.cast(JSONB).op('?')(name)
             ).first()
             
+            if alternate_match:
+                log_debug(f"Alternate match found for ingredient: {name}")
+            
             return alternate_match
         except Exception as e:
-            from logger_manager import logger
-            logger.error(f"Error searching alternate names: {e}")
+            log_error(f"Error searching alternate names: {e}",e)
             return None
         
     def get_all_ingredients(self, skip: int = 0, limit: int = 100):
@@ -90,11 +95,17 @@ 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 +124,9 @@ 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)

interfaces/ingredientModels.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, Field
 # Define a structured output model
 class IngredientAnalysisResult(BaseModel):
     name: str
+    id: int
     alternate_names: List[str] = Field(default_factory=list)
     is_found: bool = False
     safety_rating: int = 5

logger_manager.py

@@ -14,7 +14,7 @@ console_handler = logging.StreamHandler()
 console_handler.setLevel(logging.ERROR)
 
 # Create a formatter and set it for both handlers
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s - %(pathname)s:%(lineno)d")
 file_handler.setFormatter(formatter)
 console_handler.setFormatter(formatter)
 
@@ -31,8 +31,11 @@ def log_info(message: str):
 def log_warning(message: str):
     logger.warning(message)
 
-def log_error(message: str):
-    logger.error(message)
+def log_error(message: str, exc: Exception = None):
+    if exc:
+        logger.error(message, exc_info=True)
+    else:
+        logger.error(message)
 
 def log_critical(message: str):
     logger.critical(message)

main.py

@@ -1,17 +1,54 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, RedirectResponse
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
 from routers.auth import router as auth_router
 from routers.analysis import router as analysis_router
 from routers.history import router as history_router
 from routers.product import router as product_router
+from dotenv import load_dotenv
+import os
+import uvicorn
+from pathlib import Path
+
+load_dotenv()
+# Load environment variables from .env file
+PORT = os.getenv("PORT", 8000)
+
+# Define the templates directory
+templates = Jinja2Templates(directory="templates")
 
 app = FastAPI()
 
+@app.get("/")
+def read_root():
+    return RedirectResponse("/api")
+
+# print every request data for request using middleware
+@app.middleware("http")
+async def log_requests(request: Request, call_next):
+    # Store the body content before sending to the next handler
+    body_content = await request.body()
+    # Create a new request with the consumed body
+    request._body = body_content
+    response = await call_next(request)
+    print(f"Request: {request.method} {request.url}")
+    print(f"Data: {body_content}")
+    print(f"Headers: {request.headers}")
+    return response
+
+@app.get("/api", response_class=HTMLResponse)
+async def read_api(request: Request):
+    return templates.TemplateResponse("api_docs.html", {"request": request})
+
 app.include_router(analysis_router, prefix="/api/analyze")
 app.include_router(auth_router, prefix="/api/auth")
 app.include_router(product_router, prefix="/api/product")
 app.include_router(history_router, prefix="/api/history")
 
+app.add_event_handler("startup", lambda: print("Starting up..."))
+
 # To run the FastAPI app, use the command: uvicorn main:app --reload
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    # run using fastapi directly for development purposes
+    uvicorn.run(app, host="0.0.0.0", port=PORT)

migrations/versions/00248bed0fb5_updated_product.py

@@ -0,0 +1,64 @@
+"""updated product
+
+Revision ID: 00248bed0fb5
+Revises: a193e9cfa8c5
+Create Date: 2025-04-27 13:26:01.243225
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = '00248bed0fb5'
+down_revision: Union[str, None] = 'a193e9cfa8c5'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('products', sa.Column('overall_safety_score', sa.Integer(), nullable=True))
+    op.add_column('products', sa.Column('suitable_diet_types', sa.String(), nullable=True))
+    op.add_column('products', sa.Column('allergy_warnings', sa.JSON(), nullable=True))
+    op.add_column('products', sa.Column('usage_recommendations', sa.String(), nullable=True))
+    op.add_column('products', sa.Column('health_insights', sa.JSON(), nullable=True))
+    op.add_column('products', sa.Column('ingredient_interactions', sa.JSON(), nullable=True))
+    op.add_column('products', sa.Column('key_takeaway', sa.String(), nullable=True))
+    op.add_column('products', sa.Column('ingredients_count', sa.Integer(), nullable=True))
+    op.add_column('products', sa.Column('user_id', sa.Integer(), nullable=True))
+    op.add_column('products', sa.Column('timestamp', sa.DateTime(), nullable=True))
+    op.add_column('products', sa.Column('ingredient_ids', sa.JSON(), nullable=True))
+    op.drop_column('products', 'brands')
+    op.drop_column('products', 'ingredients_text')
+    op.drop_column('products', 'nutrient_levels')
+    op.drop_column('products', 'nutriments')
+    op.drop_column('products', 'nutriscore')
+    op.drop_column('products', 'generic_name')
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('products', sa.Column('generic_name', sa.VARCHAR(), autoincrement=False, nullable=True))
+    op.add_column('products', sa.Column('nutriscore', postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True))
+    op.add_column('products', sa.Column('nutriments', postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True))
+    op.add_column('products', sa.Column('nutrient_levels', postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True))
+    op.add_column('products', sa.Column('ingredients_text', sa.VARCHAR(), autoincrement=False, nullable=True))
+    op.add_column('products', sa.Column('brands', sa.VARCHAR(), autoincrement=False, nullable=True))
+    op.drop_column('products', 'ingredient_ids')
+    op.drop_column('products', 'timestamp')
+    op.drop_column('products', 'user_id')
+    op.drop_column('products', 'ingredients_count')
+    op.drop_column('products', 'key_takeaway')
+    op.drop_column('products', 'ingredient_interactions')
+    op.drop_column('products', 'health_insights')
+    op.drop_column('products', 'usage_recommendations')
+    op.drop_column('products', 'allergy_warnings')
+    op.drop_column('products', 'suitable_diet_types')
+    op.drop_column('products', 'overall_safety_score')
+    # ### end Alembic commands ###

migrations/versions/37b8cf50d624_updated_db.py

@@ -0,0 +1,65 @@
+"""updated DB
+
+Revision ID: 37b8cf50d624
+Revises: a02cd287eec0
+Create Date: 2025-05-09 11:09:49.317918
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '37b8cf50d624'
+down_revision: Union[str, None] = 'a02cd287eec0'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(length=255), nullable=False),
+    sa.Column('vuforia_id', sa.String(length=255), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_markers_id'), 'markers', ['id'], unique=False)
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_index(op.f('ix_markers_id'), table_name='markers')
+    op.drop_table('markers')
+    # ### end Alembic commands ###

migrations/versions/464bdc8c474f_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: 464bdc8c474f
+Revises: f4c75c169cf2
+Create Date: 2025-05-09 10:33:17.799071
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '464bdc8c474f'
+down_revision: Union[str, None] = 'f4c75c169cf2'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=True),
+    sa.Column('alternate_names', sa.JSON(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.JSON(), nullable=True),
+    sa.Column('allergic_info', sa.JSON(), nullable=True),
+    sa.Column('diet_type', sa.String(length=255), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(length=255), nullable=False),
+    sa.Column('ingredients', sa.JSON(), nullable=True),
+    sa.Column('ingredients_analysis', sa.JSON(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(length=255), nullable=True),
+    sa.Column('allergy_warnings', sa.JSON(), nullable=True),
+    sa.Column('usage_recommendations', sa.Text(), nullable=True),
+    sa.Column('health_insights', sa.JSON(), nullable=True),
+    sa.Column('ingredient_interactions', sa.JSON(), nullable=True),
+    sa.Column('key_takeaway', sa.Text(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.JSON(), nullable=True),
+    sa.Column('data_quality_warnings', sa.JSON(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=False),
+    sa.Column('email', sa.String(length=255), nullable=False),
+    sa.Column('hashed_password', sa.String(length=255), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(length=255), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.JSON(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

migrations/versions/50e02fec4154_updated_db.py

@@ -0,0 +1,32 @@
+"""updated DB
+
+Revision ID: 50e02fec4154
+Revises: 00248bed0fb5
+Create Date: 2025-05-09 09:59:35.961528
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '50e02fec4154'
+down_revision: Union[str, None] = '00248bed0fb5'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    pass
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    pass
+    # ### end Alembic commands ###

migrations/versions/579f1cd42a8f_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: 579f1cd42a8f
+Revises: f8aa9e5882a2
+Create Date: 2025-05-09 10:40:30.132328
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '579f1cd42a8f'
+down_revision: Union[str, None] = 'f8aa9e5882a2'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=True),
+    sa.Column('alternate_names', sa.Text(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.Text(), nullable=True),
+    sa.Column('allergic_info', sa.Text(), nullable=True),
+    sa.Column('diet_type', sa.String(length=255), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(length=255), nullable=False),
+    sa.Column('ingredients', sa.Text(), nullable=True),
+    sa.Column('ingredients_analysis', sa.Text(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(length=255), nullable=True),
+    sa.Column('allergy_warnings', sa.Text(), nullable=True),
+    sa.Column('usage_recommendations', sa.Text(), nullable=True),
+    sa.Column('health_insights', sa.Text(), nullable=True),
+    sa.Column('ingredient_interactions', sa.Text(), nullable=True),
+    sa.Column('key_takeaway', sa.Text(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.Text(), nullable=True),
+    sa.Column('data_quality_warnings', sa.Text(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=False),
+    sa.Column('email', sa.String(length=255), nullable=False),
+    sa.Column('hashed_password', sa.String(length=255), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(length=255), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

migrations/versions/6ea4e5e4de4d_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: 6ea4e5e4de4d
+Revises: 579f1cd42a8f
+Create Date: 2025-05-09 10:48:51.119673
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '6ea4e5e4de4d'
+down_revision: Union[str, None] = '579f1cd42a8f'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=True),
+    sa.Column('alternate_names', sa.Text(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.Text(), nullable=True),
+    sa.Column('allergic_info', sa.Text(), nullable=True),
+    sa.Column('diet_type', sa.String(length=255), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('NOW()'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), server_default=sa.text('NOW()'), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(length=255), nullable=False),
+    sa.Column('ingredients', sa.Text(), nullable=True),
+    sa.Column('ingredients_analysis', sa.Text(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(length=255), nullable=True),
+    sa.Column('allergy_warnings', sa.Text(), nullable=True),
+    sa.Column('usage_recommendations', sa.Text(), nullable=True),
+    sa.Column('health_insights', sa.Text(), nullable=True),
+    sa.Column('ingredient_interactions', sa.Text(), nullable=True),
+    sa.Column('key_takeaway', sa.Text(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.Text(), nullable=True),
+    sa.Column('data_quality_warnings', sa.Text(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=False),
+    sa.Column('email', sa.String(length=255), nullable=False),
+    sa.Column('hashed_password', sa.String(length=255), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(length=255), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

migrations/versions/a02cd287eec0_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: a02cd287eec0
+Revises: 6ea4e5e4de4d
+Create Date: 2025-05-09 11:03:43.504573
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'a02cd287eec0'
+down_revision: Union[str, None] = '6ea4e5e4de4d'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=True),
+    sa.Column('alternate_names', sa.Text(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.Text(), nullable=True),
+    sa.Column('allergic_info', sa.Text(), nullable=True),
+    sa.Column('diet_type', sa.String(length=255), nullable=True),
+    sa.Column('created_at', sa.TIMESTAMP(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(length=255), nullable=False),
+    sa.Column('ingredients', sa.Text(), nullable=True),
+    sa.Column('ingredients_analysis', sa.Text(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(length=255), nullable=True),
+    sa.Column('allergy_warnings', sa.Text(), nullable=True),
+    sa.Column('usage_recommendations', sa.Text(), nullable=True),
+    sa.Column('health_insights', sa.Text(), nullable=True),
+    sa.Column('ingredient_interactions', sa.Text(), nullable=True),
+    sa.Column('key_takeaway', sa.Text(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.Text(), nullable=True),
+    sa.Column('data_quality_warnings', sa.Text(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=False),
+    sa.Column('email', sa.String(length=255), nullable=False),
+    sa.Column('hashed_password', sa.String(length=255), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(length=255), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

migrations/versions/a193e9cfa8c5_added_product_and_vuforia_realted.py

@@ -0,0 +1,39 @@
+"""added product and vuforia realted
+
+Revision ID: a193e9cfa8c5
+Revises: b640d22a64f5
+Create Date: 2025-04-27 10:47:48.941202
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'a193e9cfa8c5'
+down_revision: Union[str, None] = 'b640d22a64f5'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table('markers')
+    # ### end Alembic commands ###

migrations/versions/f4c75c169cf2_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: f4c75c169cf2
+Revises: 50e02fec4154
+Create Date: 2025-05-09 10:27:47.732746
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'f4c75c169cf2'
+down_revision: Union[str, None] = '50e02fec4154'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(), nullable=True),
+    sa.Column('alternate_names', sa.JSON(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.JSON(), nullable=True),
+    sa.Column('allergic_info', sa.JSON(), nullable=True),
+    sa.Column('diet_type', sa.String(), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(), nullable=False),
+    sa.Column('ingredients', sa.JSON(), nullable=True),
+    sa.Column('ingredients_analysis', sa.JSON(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(), nullable=True),
+    sa.Column('allergy_warnings', sa.JSON(), nullable=True),
+    sa.Column('usage_recommendations', sa.String(), nullable=True),
+    sa.Column('health_insights', sa.JSON(), nullable=True),
+    sa.Column('ingredient_interactions', sa.JSON(), nullable=True),
+    sa.Column('key_takeaway', sa.String(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.JSON(), nullable=True),
+    sa.Column('data_quality_warnings', sa.JSON(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(), nullable=False),
+    sa.Column('email', sa.String(), nullable=False),
+    sa.Column('hashed_password', sa.String(), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.JSON(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(), nullable=True),
+    sa.Column('allergens', sa.String(), nullable=True),
+    sa.Column('preferred_ingredients', sa.String(), nullable=True),
+    sa.Column('disliked_ingredients', sa.String(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

migrations/versions/f8aa9e5882a2_updated_db.py

@@ -0,0 +1,129 @@
+"""updated DB
+
+Revision ID: f8aa9e5882a2
+Revises: 464bdc8c474f
+Create Date: 2025-05-09 10:36:18.602936
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'f8aa9e5882a2'
+down_revision: Union[str, None] = '464bdc8c474f'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('ingredients',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=True),
+    sa.Column('alternate_names', sa.Text(), nullable=True),
+    sa.Column('safety_rating', sa.Integer(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('health_effects', sa.Text(), nullable=True),
+    sa.Column('allergic_info', sa.Text(), nullable=True),
+    sa.Column('diet_type', sa.String(length=255), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=True),
+    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredients_id'), 'ingredients', ['id'], unique=False)
+    op.create_index(op.f('ix_ingredients_name'), 'ingredients', ['name'], unique=True)
+    op.create_table('products',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('product_name', sa.String(length=255), nullable=False),
+    sa.Column('ingredients', sa.Text(), nullable=True),
+    sa.Column('ingredients_analysis', sa.Text(), nullable=True),
+    sa.Column('overall_safety_score', sa.Integer(), nullable=True),
+    sa.Column('suitable_diet_types', sa.String(length=255), nullable=True),
+    sa.Column('allergy_warnings', sa.Text(), nullable=True),
+    sa.Column('usage_recommendations', sa.Text(), nullable=True),
+    sa.Column('health_insights', sa.Text(), nullable=True),
+    sa.Column('ingredient_interactions', sa.Text(), nullable=True),
+    sa.Column('key_takeaway', sa.Text(), nullable=True),
+    sa.Column('ingredients_count', sa.Integer(), nullable=True),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('timestamp', sa.DateTime(), nullable=True),
+    sa.Column('ingredient_ids', sa.Text(), nullable=True),
+    sa.Column('data_quality_warnings', sa.Text(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_products_id'), 'products', ['id'], unique=False)
+    op.create_table('users',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('name', sa.String(length=255), nullable=False),
+    sa.Column('email', sa.String(length=255), nullable=False),
+    sa.Column('hashed_password', sa.String(length=255), nullable=False),
+    sa.Column('is_active', sa.Boolean(), nullable=True),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+    op.create_table('ingredient_sources',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('ingredient_id', sa.Integer(), nullable=True),
+    sa.Column('source_name', sa.String(length=255), nullable=False),
+    sa.Column('found', sa.Boolean(), nullable=True),
+    sa.Column('summary', sa.Text(), nullable=True),
+    sa.Column('data', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['ingredient_id'], ['ingredients.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_ingredient_sources_id'), 'ingredient_sources', ['id'], unique=False)
+    op.create_table('markers',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('image_name', sa.String(), nullable=False),
+    sa.Column('vuforia_id', sa.String(), nullable=False),
+    sa.Column('product_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('scan_history',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('product_id', sa.Integer(), nullable=True),
+    sa.Column('scan_date', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_scan_history_id'), 'scan_history', ['id'], unique=False)
+    op.create_table('user_preferences',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('user_id', sa.Integer(), nullable=True),
+    sa.Column('dietary_restrictions', sa.String(length=255), nullable=True),
+    sa.Column('allergens', sa.Text(), nullable=True),
+    sa.Column('preferred_ingredients', sa.Text(), nullable=True),
+    sa.Column('disliked_ingredients', sa.Text(), nullable=True),
+    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_user_preferences_id'), 'user_preferences', ['id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_user_preferences_id'), table_name='user_preferences')
+    op.drop_table('user_preferences')
+    op.drop_index(op.f('ix_scan_history_id'), table_name='scan_history')
+    op.drop_table('scan_history')
+    op.drop_table('markers')
+    op.drop_index(op.f('ix_ingredient_sources_id'), table_name='ingredient_sources')
+    op.drop_table('ingredient_sources')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')
+    op.drop_index(op.f('ix_products_id'), table_name='products')
+    op.drop_table('products')
+    op.drop_index(op.f('ix_ingredients_name'), table_name='ingredients')
+    op.drop_index(op.f('ix_ingredients_id'), table_name='ingredients')
+    op.drop_table('ingredients')
+    # ### end Alembic commands ###

requirements.txt

@@ -2,11 +2,14 @@
 fastapi==0.115.12
 uvicorn==0.34.0
 python-multipart==0.0.20
+jinja2
 
 # Database
 sqlalchemy==2.0.40
 alembic==1.15.2
 psycopg2-binary==2.9.10
+mysqlclient
+pymysql
 
 # Authentication
 python-jose==3.3.0

routers/analysis.py

@@ -14,7 +14,7 @@ from interfaces.productModels import ProductIngredientsRequest
 from services.auth_service import get_current_user
 from PIL import Image
 import cv2
-from logger_manager import log_info, log_error, logger
+from logger_manager import log_info, log_error
 from db.database import get_db,SessionLocal
 from db.repositories import IngredientRepository
 from dotenv import load_dotenv
@@ -49,6 +49,7 @@ def ingredient_db_to_pydantic(db_ingredient):
         name=db_ingredient.name,
         alternate_names=db_ingredient.alternate_names or [],
         is_found=True,
+        id=db_ingredient.id,
         safety_rating=db_ingredient.safety_rating or 5,
         description=db_ingredient.description or "No description available",
         health_effects=db_ingredient.health_effects or ["Unknown"],
@@ -123,7 +124,7 @@ async def process_image(image: UploadFile = File(...)):
                 {
                     "message": "Product extracted successfully",
                     "product_image_name": extracted_product_name,
-                },status_code=200
+                }, status_code=200
             )
         else:
             print("Failed to extract the product.")
@@ -149,14 +150,14 @@ async def get_image(image_name: str):
 @traceable
 async def process_ingredient_endpoint(request: IngredientRequest, db: Session = Depends(get_db)):
     try:
-        logger.info(f"Received request to process ingredient: {request.name}")
+        log_info(f"Received request to process ingredient: {request.name}")
         
         # Check if we already have this ingredient in the database
         repo = IngredientRepository(db)
         db_ingredient = repo.get_ingredient_by_name(request.name)
         
         if db_ingredient:
-            logger.info(f"Found existing ingredient in database: {request.name}")
+            log_info(f"Found existing ingredient in database: {request.name}")
             # Convert DB model to Pydantic model
             # (This would need a function to correctly map the data)
             return ingredient_db_to_pydantic(db_ingredient)
@@ -173,11 +174,11 @@ async def process_ingredient_endpoint(request: IngredientRequest, db: Session =
         
         # Save to database
         repo.create_ingredient(result)
-        logger.info(f"Saved new ingredient to database: {request.name}")
+        log_info(f"Saved new ingredient to database: {request.name}")
         
         return result
     except Exception as e:
-        logger.error(f"Error processing ingredient: {e}")
+        log_error(f"Error processing ingredient: {e}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 async def process_single_ingredient(ingredient_name: str):
@@ -214,7 +215,7 @@ async def process_single_ingredient(ingredient_name: str):
                 
                 return ingredient_data
     except Exception as e:
-        log_error(f"Error processing ingredient {ingredient_name}: {str(e)}")
+        log_error(f"Error processing ingredient {ingredient_name}: {str(e)}",e)
         # Return a minimal result on error to avoid failing the entire batch
         return IngredientAnalysisResult(
             name=ingredient_name,
@@ -262,10 +263,13 @@ async def process_ingredients_endpoint(product_ingredient: ProductIngredientsReq
             } if current_user else {}
         )
         
+        # print("Product analysis result:", product_analysis)
+        
         # Step 3: Prepare final response
         result = {
             "ingredients_count": len(ingredients),
             "processed_ingredients": ingredient_results,
+            "ingredient_ids": product_analysis["ingredient_ids"],
             "overall_analysis": product_analysis,
             "user_id": current_user.id if current_user else None,
             "timestamp": datetime.now(tz=pytz.timezone('Asia/Kolkata')).isoformat()
@@ -275,5 +279,5 @@ async def process_ingredients_endpoint(product_ingredient: ProductIngredientsReq
         return result
         
     except Exception as e:
-        log_error(f"Error in process_ingredients_endpoint: {str(e)}")
+        log_error(f"Error in process_ingredients_endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")

routers/auth.py

@@ -23,7 +23,7 @@ def register(user: UserCreate, db: Session = Depends(get_db)):
         log_info("User registered successfully")
         return {"access_token": access_token, "token_type": "bearer"}
     except Exception as e:
-        log_error(f"Error in register endpoint: {str(e)}")
+        log_error(f"Error in register endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 @router.post("/login", response_model=Token)
@@ -45,7 +45,7 @@ def login(form_data: OAuth2PasswordRequestForm = Depends(), db: Session = Depend
         log_info("User logged in successfully")
         return {"access_token": access_token, "token_type": "bearer"}
     except Exception as e:
-        log_error(f"Error in login endpoint: {str(e)}")
+        log_error(f"Error in login endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 @router.get("/user", response_model=UserResponse)
@@ -54,7 +54,7 @@ def read_users_me(current_user: User = Depends(get_current_active_user)):
     try:
         return current_user
     except Exception as e:
-        log_error(f"Error in read_users_me endpoint: {str(e)}")
+        log_error(f"Error in read_users_me endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
         
 @router.get("/user/email", response_model=UserResponse)
@@ -67,5 +67,5 @@ def read_user_by_email(email: str, db: Session = Depends(get_db)):
             raise HTTPException(status_code=404, detail="User not found")
         return user
     except Exception as e:
-        log_error(f"Error in read_user_by_email endpoint: {str(e)}")
+        log_error(f"Error in read_user_by_email endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")

routers/history.py

@@ -26,7 +26,7 @@ def create_scan(scan: ScanHistoryCreate, db: Session = Depends(get_db)):
         log_info("Scan recorded successfully")
         return scan_entry
     except Exception as e:
-        log_error(f"Error in create_scan endpoint: {str(e)}")
+        log_error(f"Error in create_scan endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 @router.get("/scan/{user_id}", response_model=list[ScanHistoryResponse])
@@ -40,5 +40,5 @@ def read_scan_history(user_id: int, db: Session = Depends(get_db)):
         log_info("Scan history retrieved successfully")
         return scan_history
     except Exception as e:
-        log_error(f"Error in read_scan_history endpoint: {str(e)}")
+        log_error(f"Error in read_scan_history endpoint: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")

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
 from utils.fetch_data import fetch_product_data_from_api
 
 load_dotenv()
@@ -75,7 +76,7 @@ async def add_target_to_vuforia(image_name: str, image_path: str) -> str:
             log_error(f"Failed to add target {image_name}: {response.text}")
             raise Exception(f"Failed to add target {image_name}: {response.text}")
     except Exception as e:
-        log_error(f"Error adding target {image_name}: {e}")
+        log_error(f"Error adding target {image_name}: {e}",e)
         raise
 
 
@@ -114,7 +115,7 @@ async def add_product_to_database(
         return True
     except Exception as e:
         db.rollback()
-        log_error(f"Error adding/updating markers for product {product_id} in database: {e}")
+        log_error(f"Error adding/updating markers for product {product_id} in database: {e}",e)
         raise HTTPException(status_code=500, detail=f"Error adding/updating markers for product {product_id}: {e}")
 
 
@@ -156,6 +157,23 @@ 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/auth_service.py

@@ -26,7 +26,7 @@ def verify_password(plain_password, hashed_password):
     try:
         return pwd_context.verify(plain_password, hashed_password)
     except Exception as e:
-        log_error(f"Error verifying password: {str(e)}")
+        log_error(f"Error verifying password: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))
 
 def get_password_hash(password):
@@ -34,7 +34,7 @@ def get_password_hash(password):
     try:
         return pwd_context.hash(password)
     except Exception as e:
-        log_error(f"Error hashing password: {str(e)}")
+        log_error(f"Error hashing password: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))
 
 def get_user(db, email: str):
@@ -42,7 +42,7 @@ def get_user(db, email: str):
     try:
         return db.query(User).filter(func.lower(User.email) == email.lower()).first()
     except Exception as e:
-        log_error(f"Error getting user: {str(e)}")
+        log_error(f"Error getting user: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))
 
 def authenticate_user(db: Session, username: str, password: str):
@@ -77,10 +77,10 @@ async def get_current_user(db: Session = Depends(get_db), token: str = Depends(o
             raise credentials_exception
         token_data = TokenData(email=email)
     except JWTError as e:
-        log_error(f"JWT error: {str(e)}")
+        log_error(f"JWT error: {str(e)}",e)
         raise credentials_exception
     except Exception as e:
-        log_error(f"Error decoding token: {str(e)}")
+        log_error(f"Error decoding token: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))
     user = get_user(db, email=token_data.email)
     if user is None:
@@ -94,7 +94,7 @@ async def get_current_active_user(current_user: User = Depends(get_current_user)
             raise HTTPException(status_code=400, detail="Inactive user")
         return UserResponse.from_orm(current_user)
     except Exception as e:
-        log_error(f"Error getting current active user: {str(e)}")
+        log_error(f"Error getting current active user: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))
 
 def create_user(db: Session, name: str, email: str, password: str):
@@ -107,5 +107,5 @@ def create_user(db: Session, name: str, email: str, password: str):
         db.refresh(db_user)
         return db_user
     except Exception as e:
-        log_error(f"Error creating user: {str(e)}")
+        log_error(f"Error creating user: {str(e)}",e)
         raise HTTPException(status_code=500, detail=str(e))

services/ingredientFinderAgent.py

@@ -3,261 +3,20 @@ from functools import partial
 import os
 import json
 import traceback
-import requests
-import pandas as pd
 from dotenv import load_dotenv
-import aiohttp
-import time
-
 from typing import Dict, Any
+
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_community.tools import WikipediaQueryRun
-from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
-from langchain_core.tools import tool
 
 # modular
-from logger_manager import logger
 from interfaces.ingredientModels import IngredientAnalysisResult,IngredientState
+from logger_manager import log_debug, log_error, log_info, log_warning
+from utils.agent_tools import search_local_db,search_web,search_wikipedia,search_open_food_facts,search_usda,search_pubchem
 
 # Load environment variables from .env file
 load_dotenv()
 
-# Load Scraped Database
-SCRAPED_DB_PATH = "data/Food_Aditives_E_numbers.csv"  # Ensure this file exists
-if os.path.exists(SCRAPED_DB_PATH):
-    additives_df = pd.read_csv(SCRAPED_DB_PATH)
-    logger.info(f"Loaded database with {len(additives_df)} entries")
-else:
-    additives_df = None
-    logger.warning("Scraped database not found!")
-
-
-# Define a rate limit (adjust as needed)
-PUBCHEM_TIMEOUT = float(os.getenv("PUBCHEM_TIMEOUT", "2.0"))   # seconds
-PUBCHEM_MAX_RETRIES = int(os.getenv("PUBCHEM_MAX_RETRIES", "3"))  # Max retries
-
-# Rate limiting configuration
-DUCKDUCKGO_RATE_LIMIT_DELAY = float(os.getenv("DUCKDUCKGO_RATE_LIMIT_DELAY", "2.0"))  # Delay in seconds
-DUCKDUCKGO_MAX_RETRIES = int(os.getenv("DUCKDUCKGO_MAX_RETRIES", "3"))  # Max retries
-
-
-# Define tool functions
-@tool("search_local_db")
-def search_local_db(ingredient: str) -> Dict[str, Any]:
-    """Search local database for ingredient information. E number database scrapped"""
-    logger.info(f"Searching local DB for: {ingredient}")
-    if additives_df is not None:
-        match = additives_df[additives_df['Name of Additive'].str.contains(ingredient, case=False, na=False, regex=False)]
-        if not match.empty:
-            return {"source": "Local DB", "found": True, "data": match.iloc[0].to_dict()}
-    return {"source": "Local DB", "found": False, "data": None}
-
-@tool("search_open_food_facts")
-def search_open_food_facts(ingredient: str) -> Dict[str, Any]:
-    """Search Open Food Facts database for ingredient information."""
-    logger.info(f"Searching Open Food Facts for: {ingredient}")
-    
-    try:
-        open_food_facts_api = "https://world.openfoodfacts.org/api/v0"
-        # Search for the ingredient
-        search_url = f"{open_food_facts_api}/ingredient/{ingredient.lower().replace(' ', '-')}.json"
-        response = requests.get(search_url, timeout=10)
-        
-        if response.status_code == 200:
-            data = response.json()
-            if data.get("status") == 1:  # Successfully found
-                return {
-                    "source": "Open Food Facts",
-                    "found": True,
-                    "data": data
-                }
-        
-        # Try searching products containing this ingredient
-        product_search_url = f"{open_food_facts_api}/search.json?ingredients_tags={ingredient.lower().replace(' ', '_')}&page_size=5"
-        response = requests.get(product_search_url, timeout=10)
-        
-        if response.status_code == 200:
-            data = response.json()
-            if data.get("count") > 0:
-                return {
-                    "source": "Open Food Facts Products",
-                    "found": True,
-                    "data": data
-                }
-        
-        return {"source": "Open Food Facts", "found": False, "data": None}
-    
-    except Exception as e:
-        logger.error(f"Error searching Open Food Facts: {e}")
-        return {"source": "Open Food Facts", "found": False, "error": str(e)}
-
-@tool("search_usda")
-def search_usda(ingredient: str) -> Dict[str, Any]:
-    """Search USDA FoodData Central for ingredient information."""
-    logger.info(f"Searching USDA for: {ingredient}")
-    
-    try:
-        usda_api = "https://api.nal.usda.gov/fdc/v1"
-        usda_api_key = os.getenv("USDA_API_KEY", "DEMO_KEY")  # Use DEMO_KEY if not provided
-        
-        # Search for the ingredient
-        search_url = f"{usda_api}/foods/search"
-        params = {
-            "api_key": usda_api_key,
-            "query": ingredient,
-            "dataType": ["Foundation", "SR Legacy", "Branded"],
-            "pageSize": 5
-        }
-        
-        response = requests.get(search_url, params=params, timeout=10)
-        
-        if response.status_code == 200:
-            data = response.json()
-            if data.get("totalHits", 0) > 0:
-                return {
-                    "source": "USDA FoodData Central",
-                    "found": True,
-                    "data": data
-                }
-        
-        return {"source": "USDA FoodData Central", "found": False, "data": None}
-    
-    except Exception as e:
-        logger.error(f"Error searching USDA: {e}")
-        return {"source": "USDA FoodData Central", "found": False, "error": str(e)}
 
-async def async_search_pubchem(ingredient: str) -> Dict[str, Any]:
-    """Asynchronously search PubChem for chemical information about the ingredient."""
-    logger.info(f"Searching PubChem for: {ingredient}")
-    
-    try:
-        pubchem_api = "https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data"
-        # https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest#section=Input
-        
-        async with aiohttp.ClientSession() as session:
-            # First try to get compound information by name
-            search_url = f"{pubchem_api}/compound/name/{ingredient}/JSON"
-            
-            async def fetch_data(url: str, timeout: int = PUBCHEM_TIMEOUT, retry_count: int = 0):
-                try:
-                    async with session.get(url, timeout=timeout) as response:
-                        if response.status == 200:
-                            return await response.json()
-                        else:
-                            logger.warning(f"PubChem returned status: {response.status} for URL: {url}")
-                            return None
-                except asyncio.TimeoutError:
-                    if retry_count < PUBCHEM_MAX_RETRIES:
-                        delay = (2 ** retry_count) * 5  # Exponential backoff
-                        logger.warning(f"PubChem timeout for URL '{url}'. Retrying in {delay:.2f} seconds (attempt {retry_count + 1}/{PUBCHEM_MAX_RETRIES})")
-                        await asyncio.sleep(delay)
-                        return await fetch_data(url, timeout, retry_count + 1)  # Recursive retry
-                    else:
-                        logger.error(f"Max retries reached for PubChem timeout on URL: {url}")
-                        return None
-                except Exception as e:
-                    logger.error(f"PubChem error for URL '{url}': {e}")
-                    return None
-            
-            data = await fetch_data(search_url)
-            
-            if data and "PC_Compounds" in data:
-                compound_id = data["PC_Compounds"][0]["id"]["id"]["cid"]
-                
-                # Get more detailed information using the CID
-                property_url = f"{pubchem_api}/compound/cid/{compound_id}/property/MolecularFormula,MolecularWeight,IUPACName,InChI,InChIKey,CanonicalSMILES/JSON"
-                properties_data = await fetch_data(property_url)
-                
-                # Get classifications and categories
-                classification_url = f"{pubchem_api}/compound/cid/{compound_id}/classification/JSON"
-                classification_data = await fetch_data(classification_url)
-                
-                return {
-                    "source": "PubChem",
-                    "found": True,
-                    "data": {
-                        "compound_info": data,
-                        "properties": properties_data,
-                        "classification": classification_data
-                    }
-                }
-            
-            return {"source": "PubChem", "found": False, "data": None}
-    
-    except Exception as e:
-        logger.error(f"Error searching PubChem: {e}")
-        return {"source": "PubChem", "found": False, "error": str(e)}
-
-@tool("search_pubchem")
-def search_pubchem(ingredient: str) -> Dict[str, Any]:
-    """Search PubChem for chemical information about the ingredient."""
-    # Use asyncio.run to handle the async operation from synchronous code
-    try:
-        # For Python 3.7+
-        return asyncio.run(async_search_pubchem(ingredient))
-    except RuntimeError:
-        # If already in an event loop (e.g., in FastAPI)
-        loop = asyncio.get_event_loop()
-        return loop.run_until_complete(async_search_pubchem(ingredient))
-    
-@tool("search_wikipedia")
-def search_wikipedia(ingredient: str) -> Dict[str, Any]:
-    """Search Wikipedia for ingredient information."""
-    logger.info(f"Searching Wikipedia for: {ingredient}")
-    
-    try:
-        wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
-        wiki_result = wikipedia.run(ingredient)
-        
-        if wiki_result and len(wiki_result) > 100:  # Only count substantial results
-            return {
-                "source": "Wikipedia",
-                "found": True,
-                "data": wiki_result
-            }
-        else:
-            # Try with more specific searches
-            food_wiki = wikipedia.run(f"{ingredient} food additive")
-            if food_wiki and len(food_wiki) > 100:
-                return {
-                    "source": "Wikipedia",
-                    "found": True,
-                    "data": food_wiki
-                }
-            
-            chemical_wiki = wikipedia.run(f"{ingredient} chemical compound")
-            if chemical_wiki and len(chemical_wiki) > 100:
-                return {
-                    "source": "Wikipedia",
-                    "found": True,
-                    "data": chemical_wiki
-                }
-        
-        return {"source": "Wikipedia", "found": False, "data": None}
-    
-    except Exception as e:
-        logger.error(f"Error searching Wikipedia: {e}")
-        return {"source": "Wikipedia", "found": False, "error": str(e)}
-
-@tool("search_web")
-def search_web(ingredient: str) -> Dict[str, Any]:
-    """Search web for ingredient information using DuckDuckGo."""
-    logger.info(f"Searching web for: {ingredient}")
-    
-    try:
-        duckduckgo = DuckDuckGoSearchRun()
-        search_queries = [f"{ingredient} food ingredient safety", f"{ingredient} E-number food additive",f"{ingredient}'s allergic information",f"is {ingredient} vegan,vegetarian or Non-vegetarian"]
-        all_results = []
-        for query in search_queries:
-            time.sleep(DUCKDUCKGO_RATE_LIMIT_DELAY)
-            result = duckduckgo.run(query)
-            if result:
-                all_results.append({"query": query, "result": result})
-        return {"source": "DuckDuckGo", "found": bool(all_results), "data": all_results}
-    except Exception as e:
-        logger.error(f"Web search error: {e}")
-        return {"source": "DuckDuckGo", "found": False, "error": str(e)}
 
 def create_summary_from_source(source: Dict[str, Any]) -> str:
     """Create a meaningful summary from source data."""
@@ -341,7 +100,7 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
     
     # Basic validation
     if not api_key:
-        logger.error("No Google API key found in environment variables")
+        log_error("No Google API key found in environment variables")
         new_state = state.copy()
         new_state["result"] = {
             "name": state["ingredient"],
@@ -361,7 +120,7 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
             # convert_system_message_to_human=True
         )
     except Exception as e:
-        logger.error(f"Error initializing LLM: {e}")
+        log_error(f"Error initializing LLM: {e}",e)
         new_state = state.copy()
         new_state["result"] = {
             "name": state["ingredient"],
@@ -374,11 +133,11 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
     
     # Get sources from state
     sources_data = state["sources_data"]
-    logger.info(f"Analyzing ingredient with {len(sources_data)} total sources")
+    log_info(f"Analyzing ingredient with {len(sources_data)} total sources")
     
     # Filter for successful sources only
     found_sources = [source for source in sources_data if source.get('found', False)]
-    logger.info(f"Found {len(found_sources)} sources with usable data")
+    log_info(f"Found {len(found_sources)} sources with usable data")
     
     # Create default result structure
     result = {
@@ -421,12 +180,12 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
                 
                 source_texts.append(source_text)
             except Exception as e:
-                logger.error(f"Error formatting source {source_name}: {e}")
+                log_error(f"Error formatting source {source_name}: {e}",e)
                 source_texts.append(f"--- {source_name} ---\nError formatting data: {str(e)}")
         
         # Combine all source texts
         combined_data = "\n\n".join(source_texts)
-        logger.info(f"Combined data for analysis:\n{combined_data[:500]}...(truncated)")
+        log_info(f"Combined data for analysis:\n{combined_data[:500]}...(truncated)")
         
         # Create the analysis prompt
         analysis_prompt = f"""
@@ -459,14 +218,14 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
         
         # Process with LLM
         try:
-            logger.info("Sending analysis prompt to LLM")
+            log_info("Sending analysis prompt to LLM")
             llm_response = llm.invoke(analysis_prompt)
-            logger.info("Received LLM response")
+            log_info("Received LLM response")
             
             # Extract and parse JSON from LLM response
             try:
                 analysis_text = llm_response.content
-                logger.debug(f"LLM response: {analysis_text[:500]}...(truncated)")
+                log_debug(f"LLM response: {analysis_text[:500]}...(truncated)")
                 
                 # Find JSON in the response
                 start_idx = analysis_text.find('{')
@@ -485,17 +244,17 @@ def analyze_ingredient(state: IngredientState) -> IngredientState:
                         "allergic_info": analysis.get("allergic_info", []),
                         "diet_type": analysis.get("diet_type", "unknown"),
                     })
-                    logger.info(f"Analysis complete - Safety Rating: {result['safety_rating']}")
+                    log_info(f"Analysis complete - Safety Rating: {result['safety_rating']}")
                 else:
-                    logger.warning("Could not find JSON in LLM response")
+                    log_warning("Could not find JSON in LLM response")
                     result["description"] = "Error: Failed to parse LLM analysis output."
             except json.JSONDecodeError as e:
-                logger.error(f"JSON parsing error: {e}")
+                log_error(f"JSON parsing error: {e}",e)
                 result["description"] = f"Error parsing analysis: {str(e)}"
                 
         except Exception as e:
-            logger.error(f"Error in LLM analysis: {e}")
-            logger.error(traceback.format_exc())
+            log_error(f"Error in LLM analysis: {e}",e)
+            log_error(traceback.format_exc())
             result.update({
                 "description": f"Error in analysis: {str(e)}",
                 "health_effects": ["Error in analysis"],
@@ -601,7 +360,7 @@ class IngredientInfoAgentLangGraph:
             tool_name = str(tool_func).split()[0]
         
         source_name = tool_name.replace("search_", "").replace("_", " ").title()
-        logger.info(f"Searching {source_name} for {ingredient}")
+        log_info(f"Searching {source_name} for {ingredient}")
         
         try:
             # Run the tool function in a thread pool to avoid blocking
@@ -609,15 +368,15 @@ class IngredientInfoAgentLangGraph:
             result = await loop.run_in_executor(None, partial(tool_func.invoke, ingredient))
             
             if result.get("found", False):
-                logger.info(f"{source_name} found data for {ingredient}")
+                log_info(f"{source_name} found data for {ingredient}")
             return result
         except Exception as e:
-            logger.error(f"Error in {source_name} search: {e}")
+            log_error(f"Error in {source_name} search: {e}",e)
             return {"source": source_name, "found": False, "error": str(e)}
     
     async def process_ingredient_async(self, ingredient: str) -> IngredientAnalysisResult:
         """Process an ingredient using parallel data fetching."""
-        logger.info(f"=== Parallel processing for: {ingredient} ===")
+        log_info(f"=== Parallel processing for: {ingredient} ===")
         
         # Define all the tools to run in parallel
         tools = [
@@ -658,10 +417,10 @@ class IngredientInfoAgentLangGraph:
         
         # Extract the result or create a default
         if final_state.get("result"):
-            logger.info(f"Analysis complete for {ingredient}")
+            log_info(f"Analysis complete for {ingredient}")
             return IngredientAnalysisResult(**final_state["result"])
         else:
-            logger.info(f"No result in final state for {ingredient}, returning default")
+            log_info(f"No result in final state for {ingredient}, returning default")
             return IngredientAnalysisResult(
                 name=ingredient, 
                 is_found=len(sources_data) > 0, 
@@ -673,49 +432,49 @@ class IngredientInfoAgentLangGraph:
         Process an ingredient using direct sequential approach instead of async.
         This method provides compatibility with synchronous code.
         """
-        logger.info(f"=== Sequential processing for: {ingredient} ===")
+        log_info(f"=== Sequential processing for: {ingredient} ===")
         
         # Initialize empty sources data
         sources_data = []
         
         # Run each tool directly in sequence and collect results
-        logger.info(f"Searching local database for {ingredient}")
+        log_info(f"Searching local database for {ingredient}")
         result = search_local_db.invoke(ingredient)
 
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"Local DB found data for {ingredient}")
+            log_info(f"Local DB found data for {ingredient}")
         
-        logger.info(f"Searching web for {ingredient}")
+        log_info(f"Searching web for {ingredient}")
         result = search_web.invoke(ingredient)
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"Web search found data for {ingredient}")
+            log_info(f"Web search found data for {ingredient}")
         
-        logger.info(f"Searching Wikipedia for {ingredient}")
+        log_info(f"Searching Wikipedia for {ingredient}")
         result = search_wikipedia.invoke(ingredient)
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"Wikipedia found data for {ingredient}")
+            log_info(f"Wikipedia found data for {ingredient}")
         
-        logger.info(f"Searching Open Food Facts for {ingredient}")
+        log_info(f"Searching Open Food Facts for {ingredient}")
         result = search_open_food_facts.invoke(ingredient)
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"Open Food Facts found data for {ingredient}")
+            log_info(f"Open Food Facts found data for {ingredient}")
         
         
-        logger.info(f"Searching USDA for {ingredient}")
+        log_info(f"Searching USDA for {ingredient}")
         result = search_usda.invoke(ingredient)
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"USDA found data for {ingredient}")
+            log_info(f"USDA found data for {ingredient}")
         
-        logger.info(f"Searching PubChem for {ingredient}")
+        log_info(f"Searching PubChem for {ingredient}")
         result = search_pubchem.invoke(ingredient)
         if result.get("found", False):
             sources_data.append(result)
-            logger.info(f"PubChem found data for {ingredient}")
+            log_info(f"PubChem found data for {ingredient}")
         
         state = IngredientState(ingredient=ingredient,
                                  sources_data=sources_data,
@@ -727,11 +486,11 @@ class IngredientInfoAgentLangGraph:
         
         # Extract the result or create a default
         if final_state.get("result"):
-            logger.info(f"Analysis complete for {ingredient}")
+            log_info(f"Analysis complete for {ingredient}")
 
             return IngredientAnalysisResult(**final_state["result"])
         else:
-            logger.info(f"No result in final state for {ingredient}, returning default")
+            log_info(f"No result in final state for {ingredient}, returning default")
             return IngredientAnalysisResult(
                 name=ingredient, 
                 is_found=len(sources_data) > 0, 

services/productAnalyzerAgent.py

@@ -3,7 +3,7 @@ from typing import List, Dict, Any, Optional
 from dotenv import load_dotenv
 from langchain_core.messages import HumanMessage
 from langchain_google_genai import ChatGoogleGenerativeAI
-from logger_manager import logger
+from logger_manager import log_error, log_info
 from interfaces.ingredientModels import IngredientAnalysisResult
 
 # Load environment variables
@@ -17,7 +17,7 @@ async def analyze_product_ingredients(
     Analyze multiple ingredients to provide a comprehensive product analysis
     for AR display, considering user preferences and dietary restrictions.
     """
-    logger.info(f"Analyzing product with {len(ingredients_data)} ingredients")
+    log_info(f"Analyzing product with {len(ingredients_data)} ingredients")
     
     # Initialize LLM
     api_key = os.getenv("LLM_API_KEY")
@@ -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 = ""
@@ -91,9 +93,14 @@ analysis that would be helpful for a consumer viewing this in an AR application.
 }}
 
 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.
+IMPORTANT: Ensure your response is valid JSON with double quotes (") around property names and string values. 
+Avoid single quotes (') for JSON properties and values.
+Ensure all elements in arrays and objects are separated by commas, and don't include trailing commas.
+Also strictly follow the JSON format in your response.
+
 """
     
-    logger.info("Sending product analysis prompt to LLM")
+    log_info("Sending product analysis prompt to LLM")
     
     try:
         # Process with LLM
@@ -111,29 +118,32 @@ Only include factual information based on the provided data. If information is u
         if json_match:
             try:
                 analysis = json.loads(json_match.group(0))
-                logger.info("Successfully parsed product analysis")
+                analysis["ingredient_ids"] = ingredient_ids
+                log_info("Successfully parsed product analysis")
                 return analysis
             except json.JSONDecodeError as e:
-                logger.error(f"JSON parsing error: {e}")
+                log_error(f"JSON parsing error: {e}",e)
                 # Return a simplified analysis on error
                 return {
                     "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")
+            log_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}")
+        log_error(f"Error in product analysis: {e}",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 +154,7 @@ 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 +186,6 @@ 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
+    }

services/scan_history.py

@@ -19,7 +19,7 @@ def record_scan(db: Session, user_id: int, product_id: int) -> ScanHistory:
         log_info("Scan recorded successfully")
         return scan_entry
     except Exception as e:
-        log_error(f"Error recording scan: {str(e)}")
+        log_error(f"Error recording scan: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 def get_scan_history(db: Session, user_id: int) -> list[ScanHistory]:
@@ -32,5 +32,5 @@ def get_scan_history(db: Session, user_id: int) -> list[ScanHistory]:
         log_info("Scan history retrieved successfully")
         return scan_history
     except Exception as e:
-        log_error(f"Error getting scan history: {str(e)}")
+        log_error(f"Error getting scan history: {str(e)}",e)
         raise HTTPException(status_code=500, detail="Internal Server Error")

templates/api_docs.html

@@ -0,0 +1,186 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <title>FoodAnalyzer API Documentation</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            margin: 20px;
+            line-height: 1.6;
+        }
+
+        h1,
+        h2,
+        h3 {
+            color: #333;
+        }
+
+        .endpoint {
+            margin-bottom: 15px;
+            border-left: 4px solid #ddd;
+            padding-left: 15px;
+        }
+
+        .method {
+            font-weight: bold;
+            color: #0066cc;
+        }
+
+        code {
+            background: #f4f4f4;
+            padding: 2px 5px;
+            border-radius: 3px;
+            font-family: monospace;
+        }
+
+        pre {
+            background: #f9f9f9;
+            padding: 10px;
+            border-radius: 5px;
+            overflow-x: auto;
+        }
+    </style>
+</head>
+
+<body>
+    <h1>FoodAnalyzer API Documentation</h1>
+
+    <h2>Authentication Endpoints</h2>
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a href="/api/auth/register">/api/auth/register</a></p>
+        <p>Register a new user in the system</p>
+        <p>Request body:</p>
+        <pre><code>{
+  "name": "John Doe",
+  "email": "john@example.com",
+  "password": "securepassword"
+}</code></pre>
+        <p>Response:</p>
+        <pre><code>{
+  "access_token": "eyJhbGciOiJIUzI1NiIs...",
+  "token_type": "bearer"
+}</code></pre>
+    </div>
+
+
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a href="/api/auth/login">/api/auth/login</a></p>
+        <p>Login to get an access token (valid for 4 weeks)</p>
+        <p>Form data: username (email), password</p>
+        <p>Response:</p>
+        <pre><code>{
+"access_token": "eyJhbGciOiJIUzI1NiIs...",
+"token_type": "bearer"
+}</code></pre>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">GET</span> <a href="/api/auth/user">/api/auth/user</a></p>
+        <p>Get current authenticated user's information</p>
+        <p>Headers: Authorization: Bearer {token}</p>
+        <p>Response:</p>
+        <pre><code>{
+"name": "John Doe",
+"email": "john@example.com"
+}</code></pre>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">GET</span> <a href="/api/auth/user/email">/api/auth/user/email</a></p>
+        <p>Get user information by email</p>
+        <p>Query parameters: email</p>
+    </div>
+
+    <h2>Analysis Endpoints</h2>
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a href="/api/analyze/process_image">/api/analyze/process_image</a></p>
+        <p>Upload and process an image using YOLO object detection</p>
+        <p>Form data: image (file)</p>
+        <p>Response:</p>
+        <pre><code>{
+"message": "Product extracted successfully",
+"product_image_name": "f7e5d4c3-b2a1-4f9e-8d7c-6e5f4d3a2b1c.jpg"
+}</code></pre>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">GET</span> <a
+                href="/api/analyze/get_image/{image_name}">/api/analyze/get_image/{image_name}</a></p>
+        <p>Retrieve a processed image by its name</p>
+        <p>Path parameters: image_name</p>
+        <p>Response: Image file (JPEG)</p>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a
+                href="/api/analyze/process_ingredient">/api/analyze/process_ingredient</a></p>
+        <p>Process a single ingredient and get detailed analysis</p>
+        <p>Request body:</p>
+        <pre><code>{
+"name": "Monosodium Glutamate"
+}</code></pre>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a
+                href="/api/analyze/process_product_ingredients">/api/analyze/process_product_ingredients</a></p>
+        <p>Process multiple ingredients of a product</p>
+        <p>Headers: Authorization: Bearer {token}</p>
+        <p>Request body:</p>
+        <pre><code>{
+"ingredients": ["Sugar", "Salt", "Monosodium Glutamate"],
+"user_id": 1
+}</code></pre>
+    </div>
+
+    <h2>Product Endpoints</h2>
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a href="/api/product/add">/api/product/add</a></p>
+        <p>Add a new product with ingredients and images</p>
+        <p>Request body:</p>
+        <pre><code>{
+"name": "Maggi 2-Minute Noodles",
+"image_names": ["maggi_front.jpg", "maggi_ingredients.jpg"],
+"ingredients": [
+"Wheat Flour", 
+"Palm Oil", 
+"Salt", 
+"Monosodium Glutamate"
+],
+"ingredients_count": 4,
+"overall_safety_score": 6.5,
+"suitable_diet_types": ["Vegetarian"],
+"allergy_warnings": ["Contains Wheat (Gluten)"],
+"usage_recommendations": "Consume in moderation",
+"health_insights": [
+"High sodium content may contribute to high blood pressure"
+],
+"ingredient_interactions": [
+"No significant harmful interactions between ingredients"
+],
+"key_takeaway": "Convenient food option but should be consumed occasionally",
+"user_id": 1,
+"timestamp": "2025-04-27T15:30:00Z"
+}</code></pre>
+    </div>
+
+    <h2>History Endpoints</h2>
+    <div class="endpoint">
+        <p><span class="method">POST</span> <a href="/api/history/scan">/api/history/scan</a></p>
+        <p>Record a new product scan in user history</p>
+    </div>
+
+    <div class="endpoint">
+        <p><span class="method">GET</span> <a href="/api/history/user/{user_id}">/api/history/user/{user_id}</a></p>
+        <p>Retrieve scan history for a specific user</p>
+        <p>Path parameters: user_id</p>
+    </div>
+
+    <h2>Authentication</h2>
+    <p>Protected endpoints require JWT token in Authorization header:</p>
+    <p><code>Authorization: Bearer eyJhbGciOiJIUzI1NiIs...</code></p>
+    <p>Tokens are valid for 4 weeks after login.</p>
+</body>
+
+</html>

utils/agent_tools.py

@@ -0,0 +1,259 @@
+import asyncio
+import os
+
+import pandas as pd
+from dotenv import load_dotenv
+
+from typing import Dict, Any
+# modular
+from logger_manager import log_error, log_info, log_warning
+from dotenv import load_dotenv
+
+import aiohttp
+import time
+import requests
+
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
+from langchain_core.tools import tool
+
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Load Scraped Database
+SCRAPED_DB_PATH = "data/Food_Aditives_E_numbers.csv"  # Ensure this file exists
+if os.path.exists(SCRAPED_DB_PATH):
+    additives_df = pd.read_csv(SCRAPED_DB_PATH)
+    log_info(f"Loaded database with {len(additives_df)} entries")
+else:
+    additives_df = None
+    log_warning("Scraped database not found!")
+
+
+# Define a rate limit (adjust as needed)
+PUBCHEM_TIMEOUT = float(os.getenv("PUBCHEM_TIMEOUT", "2.0"))   # seconds
+PUBCHEM_MAX_RETRIES = int(os.getenv("PUBCHEM_MAX_RETRIES", "3"))  # Max retries
+
+# Rate limiting configuration
+DUCKDUCKGO_RATE_LIMIT_DELAY = float(os.getenv("DUCKDUCKGO_RATE_LIMIT_DELAY", "2.0"))  # Delay in seconds
+DUCKDUCKGO_MAX_RETRIES = int(os.getenv("DUCKDUCKGO_MAX_RETRIES", "3"))  # Max retries
+
+
+# Define tool functions
+@tool("search_local_db")
+def search_local_db(ingredient: str) -> Dict[str, Any]:
+    """Search local database for ingredient information. E number database scrapped"""
+    log_info(f"Searching local DB for: {ingredient}")
+    if additives_df is not None:
+        match = additives_df[additives_df['Name of Additive'].str.contains(ingredient, case=False, na=False, regex=False)]
+        if not match.empty:
+            return {"source": "Local DB", "found": True, "data": match.iloc[0].to_dict()}
+    return {"source": "Local DB", "found": False, "data": None}
+
+@tool("search_open_food_facts")
+def search_open_food_facts(ingredient: str) -> Dict[str, Any]:
+    """Search Open Food Facts database for ingredient information."""
+    log_info(f"Searching Open Food Facts for: {ingredient}")
+    
+    try:
+        open_food_facts_api = "https://world.openfoodfacts.org/api/v0"
+        # Search for the ingredient
+        search_url = f"{open_food_facts_api}/ingredient/{ingredient.lower().replace(' ', '-')}.json"
+        response = requests.get(search_url, timeout=10)
+        
+        if response.status_code == 200:
+            data = response.json()
+            if data.get("status") == 1:  # Successfully found
+                return {
+                    "source": "Open Food Facts",
+                    "found": True,
+                    "data": data
+                }
+        
+        # Try searching products containing this ingredient
+        product_search_url = f"{open_food_facts_api}/search.json?ingredients_tags={ingredient.lower().replace(' ', '_')}&page_size=5"
+        response = requests.get(product_search_url, timeout=10)
+        
+        if response.status_code == 200:
+            data = response.json()
+            if data.get("count") > 0:
+                return {
+                    "source": "Open Food Facts Products",
+                    "found": True,
+                    "data": data
+                }
+        
+        return {"source": "Open Food Facts", "found": False, "data": None}
+    
+    except Exception as e:
+        log_error(f"Error searching Open Food Facts: {e}",e)
+        return {"source": "Open Food Facts", "found": False, "error": str(e)}
+
+@tool("search_usda")
+def search_usda(ingredient: str) -> Dict[str, Any]:
+    """Search USDA FoodData Central for ingredient information."""
+    log_info(f"Searching USDA for: {ingredient}")
+    
+    try:
+        usda_api = "https://api.nal.usda.gov/fdc/v1"
+        usda_api_key = os.getenv("USDA_API_KEY", "DEMO_KEY")  # Use DEMO_KEY if not provided
+        
+        # Search for the ingredient
+        search_url = f"{usda_api}/foods/search"
+        params = {
+            "api_key": usda_api_key,
+            "query": ingredient,
+            "dataType": ["Foundation", "SR Legacy", "Branded"],
+            "pageSize": 5
+        }
+        
+        response = requests.get(search_url, params=params, timeout=10)
+        
+        if response.status_code == 200:
+            data = response.json()
+            if data.get("totalHits", 0) > 0:
+                return {
+                    "source": "USDA FoodData Central",
+                    "found": True,
+                    "data": data
+                }
+        
+        return {"source": "USDA FoodData Central", "found": False, "data": None}
+    
+    except Exception as e:
+        log_error(f"Error searching USDA: {e}",e)
+        return {"source": "USDA FoodData Central", "found": False, "error": str(e)}
+
+async def async_search_pubchem(ingredient: str) -> Dict[str, Any]:
+    """Asynchronously search PubChem for chemical information about the ingredient."""
+    log_info(f"Searching PubChem for: {ingredient}")
+    
+    try:
+        pubchem_api = "https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data"
+        # https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest#section=Input
+        
+        async with aiohttp.ClientSession() as session:
+            # First try to get compound information by name
+            search_url = f"{pubchem_api}/compound/name/{ingredient}/JSON"
+            
+            async def fetch_data(url: str, timeout: int = PUBCHEM_TIMEOUT, retry_count: int = 0):
+                try:
+                    async with session.get(url, timeout=timeout) as response:
+                        if response.status == 200:
+                            return await response.json()
+                        else:
+                            log_warning(f"PubChem returned status: {response.status} for URL: {url}")
+                            return None
+                except asyncio.TimeoutError:
+                    if retry_count < PUBCHEM_MAX_RETRIES:
+                        delay = (2 ** retry_count) * 5  # Exponential backoff
+                        log_warning(f"PubChem timeout for URL '{url}'. Retrying in {delay:.2f} seconds (attempt {retry_count + 1}/{PUBCHEM_MAX_RETRIES})")
+                        await asyncio.sleep(delay)
+                        return await fetch_data(url, timeout, retry_count + 1)  # Recursive retry
+                    else:
+                        log_error(f"Max retries reached for PubChem timeout on URL: {url}",asyncio.TimeoutError)
+                        return None
+                except Exception as e:
+                    log_error(f"PubChem error for URL '{url}': {e}",e)
+                    return None
+            
+            data = await fetch_data(search_url)
+            
+            if data and "PC_Compounds" in data:
+                compound_id = data["PC_Compounds"][0]["id"]["id"]["cid"]
+                
+                # Get more detailed information using the CID
+                property_url = f"{pubchem_api}/compound/cid/{compound_id}/property/MolecularFormula,MolecularWeight,IUPACName,InChI,InChIKey,CanonicalSMILES/JSON"
+                properties_data = await fetch_data(property_url)
+                
+                # Get classifications and categories
+                classification_url = f"{pubchem_api}/compound/cid/{compound_id}/classification/JSON"
+                classification_data = await fetch_data(classification_url)
+                
+                return {
+                    "source": "PubChem",
+                    "found": True,
+                    "data": {
+                        "compound_info": data,
+                        "properties": properties_data,
+                        "classification": classification_data
+                    }
+                }
+            
+            return {"source": "PubChem", "found": False, "data": None}
+    
+    except Exception as e:
+        log_error(f"Error searching PubChem: {e}",e)
+        return {"source": "PubChem", "found": False, "error": str(e)}
+
+@tool("search_pubchem")
+def search_pubchem(ingredient: str) -> Dict[str, Any]:
+    """Search PubChem for chemical information about the ingredient."""
+    # Use asyncio.run to handle the async operation from synchronous code
+    try:
+        # For Python 3.7+
+        return asyncio.run(async_search_pubchem(ingredient))
+    except RuntimeError:
+        # If already in an event loop (e.g., in FastAPI)
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(async_search_pubchem(ingredient))
+    
+@tool("search_wikipedia")
+def search_wikipedia(ingredient: str) -> Dict[str, Any]:
+    """Search Wikipedia for ingredient information."""
+    log_info(f"Searching Wikipedia for: {ingredient}")
+    
+    try:
+        wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+        wiki_result = wikipedia.run(ingredient)
+        
+        if wiki_result and len(wiki_result) > 100:  # Only count substantial results
+            return {
+                "source": "Wikipedia",
+                "found": True,
+                "data": wiki_result
+            }
+        else:
+            # Try with more specific searches
+            food_wiki = wikipedia.run(f"{ingredient} food additive")
+            if food_wiki and len(food_wiki) > 100:
+                return {
+                    "source": "Wikipedia",
+                    "found": True,
+                    "data": food_wiki
+                }
+            
+            chemical_wiki = wikipedia.run(f"{ingredient} chemical compound")
+            if chemical_wiki and len(chemical_wiki) > 100:
+                return {
+                    "source": "Wikipedia",
+                    "found": True,
+                    "data": chemical_wiki
+                }
+        
+        return {"source": "Wikipedia", "found": False, "data": None}
+    
+    except Exception as e:
+        log_error(f"Error searching Wikipedia: {e}",e)
+        return {"source": "Wikipedia", "found": False, "error": str(e)}
+
+@tool("search_web")
+def search_web(ingredient: str) -> Dict[str, Any]:
+    """Search web for ingredient information using DuckDuckGo."""
+    log_info(f"Searching web for: {ingredient}")
+    
+    try:
+        duckduckgo = DuckDuckGoSearchRun()
+        search_queries = [f"{ingredient} food ingredient safety", f"{ingredient} E-number food additive",f"{ingredient}'s allergic information",f"is {ingredient} vegan,vegetarian or Non-vegetarian"]
+        all_results = []
+        for query in search_queries:
+            time.sleep(DUCKDUCKGO_RATE_LIMIT_DELAY)
+            result = duckduckgo.run(query)
+            if result:
+                all_results.append({"query": query, "result": result})
+        return {"source": "DuckDuckGo", "found": bool(all_results), "data": all_results}
+    except Exception as e:
+        log_error(f"Web search error: {e}",e)
+        return {"source": "DuckDuckGo", "found": False, "error": str(e)}