Implement tomato severity segmentation model API

- Set up FastAPI project structure with SQLite database
- Create database models for tomato images and severity classifications
- Implement image upload and processing endpoints
- Develop a segmentation model for tomato disease severity detection
- Add API endpoints for analysis and results retrieval
- Implement health check endpoint
- Set up Alembic for database migrations
- Update project documentation

README.md

@@ -1,3 +1,142 @@
-# FastAPI Application
+# Tomato Severity Segmentation Model API
 
-This is a FastAPI application bootstrapped by BackendIM, the AI-powered backend generation platform.
+A FastAPI-based application for detecting and analyzing disease severity in tomato plants using image segmentation techniques.
+
+## Overview
+
+This API allows users to upload tomato plant images and analyze them for various diseases and their severity. The system uses a segmentation model to identify affected areas and classify the severity of diseases such as early blight, late blight, bacterial spot, and septoria leaf spot.
+
+## Features
+
+- **Image Upload**: Upload tomato plant images for analysis
+- **Disease Detection**: Identify multiple disease types in a single image
+- **Severity Classification**: Classify the severity of detected diseases
+- **Segmentation Maps**: Generate segmentation maps highlighting affected areas
+- **Health Monitoring**: Built-in health endpoint for monitoring application status
+
+## Installation
+
+### Prerequisites
+
+- Python 3.8+
+- pip (Python package manager)
+
+### Setup
+
+1. Clone the repository:
+   ```bash
+   git clone <repository-url>
+   cd tomatoseveritysegmentationmodel
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run database migrations:
+   ```bash
+   alembic upgrade head
+   ```
+
+4. Start the application:
+   ```bash
+   uvicorn main:app --reload
+   ```
+
+The API will be available at `http://localhost:8000`
+
+## API Endpoints
+
+### Health Check
+
+- `GET /health`: Check the health of the application
+
+### Image Management
+
+- `POST /api/tomatoes/upload`: Upload a tomato image
+- `GET /api/tomatoes`: List all uploaded tomato images
+- `GET /api/tomatoes/{image_id}`: Get details of a specific image
+- `DELETE /api/tomatoes/{image_id}`: Delete an image and its analysis data
+
+### Analysis
+
+- `POST /api/model/analyze/{image_id}`: Analyze a tomato image for disease severity
+- `GET /api/model/results/{image_id}`: Get all analysis results for a specific image
+- `GET /api/model/info`: Get information about the segmentation model
+
+## Database Schema
+
+The application uses SQLite with the following main tables:
+
+- `tomato_images`: Stores uploaded image metadata
+- `analysis_results`: Stores analysis results for each image
+- `severity_details`: Stores detailed severity data for each analysis
+
+## Segmentation Model
+
+The current implementation uses conventional computer vision techniques for segmentation as a proof of concept. In a production environment, this would be replaced with a trained deep learning model like U-Net or DeepLabV3.
+
+The model classifies the following disease categories:
+- Healthy
+- Early Blight
+- Late Blight
+- Bacterial Spot
+- Septoria Leaf Spot
+
+## Example Usage
+
+### Upload an image
+
+```bash
+curl -X POST "http://localhost:8000/api/tomatoes/upload" \
+  -H "accept: application/json" \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@tomato_plant.jpg"
+```
+
+### Analyze the image
+
+```bash
+curl -X POST "http://localhost:8000/api/model/analyze/{image_id}" \
+  -H "accept: application/json"
+```
+
+## Development
+
+### Project Structure
+
+```
+tomatoseveritysegmentationmodel/
+├── app/
+│   ├── api/
+│   │   └── routes/
+│   ├── core/
+│   ├── db/
+│   ├── models/
+│   ├── schemas/
+│   ├── services/
+│   └── utils/
+├── migrations/
+├── storage/
+│   ├── db/
+│   ├── images/
+│   └── models/
+├── alembic.ini
+├── main.py
+└── requirements.txt
+```
+
+### Adding New Features
+
+To add new features:
+
+1. Define models in `app/models/`
+2. Create Pydantic schemas in `app/schemas/`
+3. Add database migrations with Alembic
+4. Implement routes in `app/api/routes/`
+5. Update tests
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

alembic.ini

@@ -0,0 +1,84 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+script_location = migrations
+
+# template used to generate migration files
+# file_template = %%(rev)s_%%(slug)s
+
+# timezone to use when rendering the date
+# within the migration file as well as the filename.
+# string value is passed to dateutil.tz.gettz()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the
+# "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; this defaults
+# to migrations/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path
+# version_locations = %(here)s/bar %(here)s/bat migrations/versions
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+sqlalchemy.url = sqlite:////app/storage/db/db.sqlite
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks=black
+# black.type=console_scripts
+# black.entrypoint=black
+# black.options=-l 79
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARN
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARN
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

app/api/routes/health.py

@@ -0,0 +1,81 @@
+from fastapi import APIRouter, Depends, status
+from sqlalchemy.orm import Session
+from app.db.session import get_db
+from typing import Dict
+import time
+from pathlib import Path
+from app.core.config import settings
+
+router = APIRouter()
+
+@router.get("/health", status_code=status.HTTP_200_OK)
+def health_check(db: Session = Depends(get_db)) -> Dict[str, any]:
+    """
+    Perform a health check of the service.
+    
+    Checks:
+    - Database connection
+    - Storage directories
+    - Any critical services the application depends on
+    """
+    health_data = {
+        "status": "ok",
+        "timestamp": time.time(),
+        "checks": {
+            "database": check_database(db),
+            "storage": check_storage(),
+        }
+    }
+    
+    # If any check failed, update the overall status
+    if any(not check["status"] for check in health_data["checks"].values()):
+        health_data["status"] = "degraded"
+    
+    return health_data
+
+def check_database(db: Session) -> Dict[str, any]:
+    """Check if the database is accessible."""
+    try:
+        # Simple query to check if the database is responding
+        db.execute("SELECT 1")
+        return {
+            "status": True,
+            "message": "Database connection successful"
+        }
+    except Exception as e:
+        return {
+            "status": False,
+            "message": f"Database connection failed: {str(e)}"
+        }
+
+def check_storage() -> Dict[str, any]:
+    """Check if storage directories are accessible and writable."""
+    storage_status = True
+    messages = []
+    
+    # Check main storage directories
+    for directory in [settings.DB_DIR, settings.IMAGE_DIR, settings.MODEL_DIR]:
+        dir_path = Path(directory)
+        
+        if not dir_path.exists():
+            try:
+                dir_path.mkdir(parents=True, exist_ok=True)
+                messages.append(f"Created missing directory: {dir_path}")
+            except Exception as e:
+                storage_status = False
+                messages.append(f"Failed to create directory {dir_path}: {str(e)}")
+                continue
+        
+        # Check if directory is writable by trying to create a temp file
+        try:
+            temp_file = dir_path / ".health_check_temp"
+            temp_file.touch()
+            temp_file.unlink()
+        except Exception as e:
+            storage_status = False
+            messages.append(f"Directory {dir_path} is not writable: {str(e)}")
+    
+    return {
+        "status": storage_status,
+        "message": "; ".join(messages) if messages else "All storage directories are accessible and writable"
+    }

app/api/routes/model.py

@@ -0,0 +1,166 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy.orm import Session
+from typing import List, Dict, Any
+import json
+from pathlib import Path
+
+from app.db.session import get_db
+from app.db.crud_tomato import tomato_image, analysis_result
+from app.schemas.tomato import (
+    AnalysisResponse,
+    AnalysisResultCreate,
+    SeverityDetailCreate
+)
+from app.services.model import segmentation_model
+
+router = APIRouter()
+
+
+@router.post("/analyze/{image_id}", response_model=AnalysisResponse)
+def analyze_tomato_image(
+    image_id: str,
+    db: Session = Depends(get_db)
+):
+    """
+    Analyze a tomato image to detect disease severity.
+    
+    This endpoint processes the image using the segmentation model
+    and returns detailed analysis results, including disease severity
+    classification and segmentation masks.
+    """
+    # Get the image from database
+    db_image = tomato_image.get(db=db, id=image_id)
+    if db_image is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Image not found"
+        )
+    
+    # Check if image file exists
+    image_path = db_image.file_path
+    if not Path(image_path).exists():
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Image file not found on server"
+        )
+    
+    # Analyze the image
+    analysis_results = segmentation_model.analyze_image(image_path)
+    
+    if not analysis_results.get("success", False):
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+            detail=analysis_results.get("error", "Failed to analyze image")
+        )
+    
+    # Store analysis results in database
+    analysis_data = {
+        "image_id": db_image.id,
+        "model_name": analysis_results["model_name"],
+        "model_version": analysis_results["model_version"],
+        "primary_severity": analysis_results["primary_severity"],
+        "severity_confidence": analysis_results["severity_confidence"],
+        "segmentation_data": json.dumps(analysis_results["segmentation_data"]),
+        "processing_time_ms": analysis_results["processing_time_ms"]
+    }
+    
+    analysis_in = AnalysisResultCreate(**analysis_data)
+    
+    # Create severity details
+    severity_details_data = []
+    for detail in analysis_results["severity_details"]:
+        severity_details_data.append(
+            SeverityDetailCreate(
+                severity_class=detail["severity_class"],
+                confidence=detail["confidence"],
+                affected_area_percentage=detail["affected_area_percentage"],
+                analysis_id=""  # Will be set after analysis is created
+            )
+        )
+    
+    # Create analysis result with details
+    db_analysis = analysis_result.create_with_details(
+        db=db, 
+        analysis_in=analysis_in, 
+        details_in=severity_details_data
+    )
+    
+    # Get fresh data with relationships loaded
+    db_analysis = analysis_result.get(db=db, id=db_analysis.id)
+    
+    # Prepare response
+    response = AnalysisResponse(
+        id=db_analysis.id,
+        image=db_image,
+        primary_severity=db_analysis.primary_severity,
+        severity_confidence=db_analysis.severity_confidence,
+        severity_details=db_analysis.severity_details,
+        segmentation_data=db_analysis.segmentation_data,
+        processed_at=db_analysis.processed_at,
+        model_name=db_analysis.model_name,
+        model_version=db_analysis.model_version
+    )
+    
+    return response
+
+
+@router.get("/results/{image_id}", response_model=List[AnalysisResponse])
+def get_analysis_results(
+    image_id: str,
+    db: Session = Depends(get_db)
+):
+    """
+    Get all analysis results for a specific tomato image.
+    """
+    # Check if image exists
+    db_image = tomato_image.get(db=db, id=image_id)
+    if db_image is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Image not found"
+        )
+    
+    # Get analysis results
+    analyses = analysis_result.get_for_image(db=db, image_id=image_id)
+    
+    # Prepare response
+    responses = []
+    for db_analysis in analyses:
+        responses.append(
+            AnalysisResponse(
+                id=db_analysis.id,
+                image=db_image,
+                primary_severity=db_analysis.primary_severity,
+                severity_confidence=db_analysis.severity_confidence,
+                severity_details=db_analysis.severity_details,
+                segmentation_data=db_analysis.segmentation_data,
+                processed_at=db_analysis.processed_at,
+                model_name=db_analysis.model_name,
+                model_version=db_analysis.model_version
+            )
+        )
+    
+    return responses
+
+
+@router.get("/info", response_model=Dict[str, Any])
+def get_model_info():
+    """
+    Get information about the tomato severity segmentation model.
+    """
+    return {
+        "name": segmentation_model.model_name,
+        "version": segmentation_model.model_version,
+        "description": "Tomato disease severity segmentation model",
+        "input_format": {
+            "type": "image",
+            "size": segmentation_model.input_size,
+            "supported_formats": ["JPEG", "PNG"]
+        },
+        "severity_classes": segmentation_model.severity_classes,
+        "capabilities": [
+            "disease classification",
+            "severity assessment",
+            "leaf segmentation"
+        ]
+    }

app/api/routes/tomato.py

@@ -0,0 +1,112 @@
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, status
+from sqlalchemy.orm import Session
+from typing import List
+from pathlib import Path
+
+from app.db.session import get_db
+from app.db.crud_tomato import tomato_image
+from app.schemas.tomato import TomatoImage, TomatoImageCreate, UploadResponse
+from app.utils.image import save_uploaded_image
+from app.core.config import settings
+
+router = APIRouter()
+
+
+@router.post("/upload", response_model=UploadResponse, status_code=status.HTTP_201_CREATED)
+async def upload_tomato_image(
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db)
+):
+    """
+    Upload a tomato image for analysis.
+    
+    The image will be saved and registered in the database.
+    It can then be analyzed using the analysis endpoint.
+    """
+    # Validate file type
+    if file.content_type not in settings.ALLOWED_IMAGE_TYPES:
+        raise HTTPException(
+            status_code=status.HTTP_415_UNSUPPORTED_MEDIA_TYPE,
+            detail=f"Unsupported file type: {file.content_type}. Allowed types: {', '.join(settings.ALLOWED_IMAGE_TYPES)}"
+        )
+    
+    # Read file content
+    contents = await file.read()
+    
+    # Validate file size
+    if len(contents) > settings.MAX_IMAGE_SIZE:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail=f"File too large. Maximum size allowed: {settings.MAX_IMAGE_SIZE / (1024 * 1024)}MB"
+        )
+    
+    # Save file and get metadata
+    image_data = save_uploaded_image(contents, file.filename)
+    
+    # Create database record
+    image_in = TomatoImageCreate(**image_data)
+    db_image = tomato_image.create(db=db, obj_in=image_in)
+    
+    return UploadResponse(
+        image=db_image,
+        message="Image uploaded successfully"
+    )
+
+
+@router.get("/", response_model=List[TomatoImage])
+def list_tomato_images(
+    skip: int = 0,
+    limit: int = 100,
+    db: Session = Depends(get_db)
+):
+    """
+    List all uploaded tomato images.
+    """
+    images = tomato_image.get_multi(db=db, skip=skip, limit=limit)
+    return images
+
+
+@router.get("/{image_id}", response_model=TomatoImage)
+def get_tomato_image(
+    image_id: str,
+    db: Session = Depends(get_db)
+):
+    """
+    Get a specific tomato image by ID.
+    """
+    db_image = tomato_image.get(db=db, id=image_id)
+    if db_image is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Image not found"
+        )
+    return db_image
+
+
+@router.delete("/{image_id}", status_code=status.HTTP_204_NO_CONTENT, response_model=None)
+def delete_tomato_image(
+    image_id: str,
+    db: Session = Depends(get_db)
+):
+    """
+    Delete a tomato image and its associated data.
+    """
+    db_image = tomato_image.get(db=db, id=image_id)
+    if db_image is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Image not found"
+        )
+        
+    # Delete the image file
+    try:
+        if db_image.file_path and Path(db_image.file_path).exists():
+            Path(db_image.file_path).unlink()
+    except Exception:
+        # Log error but continue with database deletion
+        pass
+        
+    # Delete database record (cascade will handle related records)
+    tomato_image.remove(db=db, id=image_id)
+    
+    return None

app/core/config.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+from typing import List
+from pydantic_settings import BaseSettings
+
+class Settings(BaseSettings):
+    # Base settings
+    PROJECT_NAME: str = "Tomato Severity Segmentation API"
+    API_V1_STR: str = "/api"
+    DEBUG: bool = True
+    
+    # CORS
+    CORS_ORIGINS: List[str] = ["*"]
+    
+    # Paths
+    BASE_DIR: Path = Path(__file__).resolve().parent.parent.parent
+    STORAGE_DIR: Path = BASE_DIR / "storage"
+    
+    # Database
+    DB_DIR: Path = STORAGE_DIR / "db"
+    DB_DIR.mkdir(parents=True, exist_ok=True)
+    SQLALCHEMY_DATABASE_URL: str = f"sqlite:///{DB_DIR}/db.sqlite"
+    
+    # Image storage
+    IMAGE_DIR: Path = STORAGE_DIR / "images"
+    IMAGE_DIR.mkdir(parents=True, exist_ok=True)
+    MAX_IMAGE_SIZE: int = 10 * 1024 * 1024  # 10MB
+    ALLOWED_IMAGE_TYPES: List[str] = ["image/jpeg", "image/png"]
+    
+    # Model settings
+    MODEL_DIR: Path = STORAGE_DIR / "models"
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    DEFAULT_MODEL_NAME: str = "tomato_severity_model"
+    
+    # Severity classifications
+    SEVERITY_CLASSES: List[str] = [
+        "healthy", 
+        "early_blight", 
+        "late_blight", 
+        "bacterial_spot", 
+        "septoria_leaf_spot"
+    ]
+    
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+settings = Settings()

app/db/crud.py

@@ -0,0 +1,54 @@
+from typing import List, Optional, Generic, TypeVar, Type
+from sqlalchemy.orm import Session
+from pydantic import BaseModel
+from fastapi.encoders import jsonable_encoder
+from app.db.session import Base
+
+ModelType = TypeVar("ModelType", bound=Base)
+CreateSchemaType = TypeVar("CreateSchemaType", bound=BaseModel)
+UpdateSchemaType = TypeVar("UpdateSchemaType", bound=BaseModel)
+
+
+class CRUDBase(Generic[ModelType, CreateSchemaType, UpdateSchemaType]):
+    def __init__(self, model: Type[ModelType]):
+        """
+        CRUD object with default methods to Create, Read, Update, Delete (CRUD).
+        """
+        self.model = model
+
+    def get(self, db: Session, id: str) -> Optional[ModelType]:
+        return db.query(self.model).filter(self.model.id == id).first()
+
+    def get_multi(
+        self, db: Session, *, skip: int = 0, limit: int = 100
+    ) -> List[ModelType]:
+        return db.query(self.model).offset(skip).limit(limit).all()
+
+    def create(self, db: Session, *, obj_in: CreateSchemaType) -> ModelType:
+        obj_in_data = jsonable_encoder(obj_in)
+        db_obj = self.model(**obj_in_data)
+        db.add(db_obj)
+        db.commit()
+        db.refresh(db_obj)
+        return db_obj
+
+    def update(
+        self, db: Session, *, db_obj: ModelType, obj_in: UpdateSchemaType
+    ) -> ModelType:
+        obj_data = jsonable_encoder(db_obj)
+        update_data = obj_in.dict(exclude_unset=True)
+        
+        for field in obj_data:
+            if field in update_data:
+                setattr(db_obj, field, update_data[field])
+                
+        db.add(db_obj)
+        db.commit()
+        db.refresh(db_obj)
+        return db_obj
+
+    def remove(self, db: Session, *, id: str) -> ModelType:
+        obj = db.query(self.model).get(id)
+        db.delete(obj)
+        db.commit()
+        return obj

app/db/crud_tomato.py

@@ -0,0 +1,50 @@
+from typing import List, Optional
+from sqlalchemy.orm import Session
+
+from app.db.crud import CRUDBase
+from app.models.tomato import TomatoImage, AnalysisResult, SeverityDetail
+from app.schemas.tomato import TomatoImageCreate, AnalysisResultCreate, SeverityDetailCreate
+
+
+class CRUDTomatoImage(CRUDBase[TomatoImage, TomatoImageCreate, TomatoImageCreate]):
+    def get_by_path(self, db: Session, *, file_path: str) -> Optional[TomatoImage]:
+        return db.query(TomatoImage).filter(TomatoImage.file_path == file_path).first()
+
+
+class CRUDAnalysisResult(CRUDBase[AnalysisResult, AnalysisResultCreate, AnalysisResultCreate]):
+    def get_for_image(self, db: Session, *, image_id: str) -> List[AnalysisResult]:
+        return db.query(AnalysisResult).filter(AnalysisResult.image_id == image_id).all()
+    
+    def create_with_details(
+        self, 
+        db: Session, 
+        *, 
+        analysis_in: AnalysisResultCreate, 
+        details_in: List[SeverityDetailCreate]
+    ) -> AnalysisResult:
+        # Create the analysis result
+        analysis_obj = self.create(db=db, obj_in=analysis_in)
+        
+        # Create severity details linked to this analysis
+        for detail in details_in:
+            severity_obj = SeverityDetail(
+                analysis_id=analysis_obj.id,
+                severity_class=detail.severity_class,
+                confidence=detail.confidence,
+                affected_area_percentage=detail.affected_area_percentage
+            )
+            db.add(severity_obj)
+        
+        db.commit()
+        db.refresh(analysis_obj)
+        return analysis_obj
+
+
+class CRUDSeverityDetail(CRUDBase[SeverityDetail, SeverityDetailCreate, SeverityDetailCreate]):
+    def get_for_analysis(self, db: Session, *, analysis_id: str) -> List[SeverityDetail]:
+        return db.query(SeverityDetail).filter(SeverityDetail.analysis_id == analysis_id).all()
+
+
+tomato_image = CRUDTomatoImage(TomatoImage)
+analysis_result = CRUDAnalysisResult(AnalysisResult)
+severity_detail = CRUDSeverityDetail(SeverityDetail)

app/db/session.py

@@ -0,0 +1,22 @@
+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+
+from app.core.config import settings
+
+engine = create_engine(
+    settings.SQLALCHEMY_DATABASE_URL,
+    connect_args={"check_same_thread": False}
+)
+
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+Base = declarative_base()
+
+# Dependency to get DB session
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

app/models/tomato.py

@@ -0,0 +1,69 @@
+import uuid
+from datetime import datetime
+from sqlalchemy import Column, String, DateTime, Float, Text, ForeignKey, Integer
+from sqlalchemy.orm import relationship
+from app.db.session import Base
+
+
+class TomatoImage(Base):
+    __tablename__ = "tomato_images"
+    
+    id = Column(String(36), primary_key=True, index=True, default=lambda: str(uuid.uuid4()))
+    filename = Column(String(255), nullable=False)
+    file_path = Column(String(512), nullable=False, unique=True)
+    file_size = Column(Integer, nullable=False)
+    mime_type = Column(String(50), nullable=False)
+    width = Column(Integer, nullable=True)
+    height = Column(Integer, nullable=True)
+    
+    uploaded_at = Column(DateTime, default=datetime.utcnow, nullable=False)
+    
+    # Relationships
+    analysis_results = relationship("AnalysisResult", back_populates="image", cascade="all, delete-orphan")
+    
+    def __repr__(self):
+        return f"<TomatoImage {self.filename}>"
+
+
+class AnalysisResult(Base):
+    __tablename__ = "analysis_results"
+    
+    id = Column(String(36), primary_key=True, index=True, default=lambda: str(uuid.uuid4()))
+    image_id = Column(String(36), ForeignKey("tomato_images.id", ondelete="CASCADE"), nullable=False)
+    model_name = Column(String(255), nullable=False)
+    model_version = Column(String(50), nullable=False)
+    
+    # Overall severity data
+    primary_severity = Column(String(50), nullable=True)
+    severity_confidence = Column(Float, nullable=True)
+    
+    # Segmentation data (stored as JSON string)
+    segmentation_data = Column(Text, nullable=True)
+    
+    # Additional metadata
+    processed_at = Column(DateTime, default=datetime.utcnow, nullable=False)
+    processing_time_ms = Column(Integer, nullable=True)
+    
+    # Relationships
+    image = relationship("TomatoImage", back_populates="analysis_results")
+    severity_details = relationship("SeverityDetail", back_populates="analysis_result", cascade="all, delete-orphan")
+    
+    def __repr__(self):
+        return f"<AnalysisResult {self.id} for image {self.image_id}>"
+
+
+class SeverityDetail(Base):
+    __tablename__ = "severity_details"
+    
+    id = Column(String(36), primary_key=True, index=True, default=lambda: str(uuid.uuid4()))
+    analysis_id = Column(String(36), ForeignKey("analysis_results.id", ondelete="CASCADE"), nullable=False)
+    
+    severity_class = Column(String(50), nullable=False)
+    confidence = Column(Float, nullable=False)
+    affected_area_percentage = Column(Float, nullable=True)
+    
+    # Relationships
+    analysis_result = relationship("AnalysisResult", back_populates="severity_details")
+    
+    def __repr__(self):
+        return f"<SeverityDetail {self.severity_class} ({self.confidence:.2f})>"

app/schemas/tomato.py

@@ -0,0 +1,96 @@
+from datetime import datetime
+from typing import List, Optional, Dict, Any
+from pydantic import BaseModel, validator
+
+
+# TomatoImage schemas
+class TomatoImageBase(BaseModel):
+    filename: str
+    mime_type: str
+    width: Optional[int] = None
+    height: Optional[int] = None
+    file_size: int
+
+
+class TomatoImageCreate(TomatoImageBase):
+    file_path: str
+
+
+class TomatoImage(TomatoImageBase):
+    id: str
+    file_path: str
+    uploaded_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+
+# SeverityDetail schemas
+class SeverityDetailBase(BaseModel):
+    severity_class: str
+    confidence: float
+    affected_area_percentage: Optional[float] = None
+
+
+class SeverityDetailCreate(SeverityDetailBase):
+    analysis_id: str
+
+
+class SeverityDetail(SeverityDetailBase):
+    id: str
+    analysis_id: str
+    
+    class Config:
+        from_attributes = True
+
+
+# AnalysisResult schemas
+class AnalysisResultBase(BaseModel):
+    model_name: str
+    model_version: str
+    primary_severity: Optional[str] = None
+    severity_confidence: Optional[float] = None
+    segmentation_data: Optional[str] = None
+    processing_time_ms: Optional[int] = None
+
+
+class AnalysisResultCreate(AnalysisResultBase):
+    image_id: str
+
+
+class AnalysisResult(AnalysisResultBase):
+    id: str
+    image_id: str
+    processed_at: datetime
+    severity_details: List[SeverityDetail] = []
+    
+    class Config:
+        from_attributes = True
+
+
+# Response schemas
+class AnalysisResponse(BaseModel):
+    id: str
+    image: TomatoImage
+    primary_severity: Optional[str] = None
+    severity_confidence: Optional[float] = None
+    severity_details: List[SeverityDetail] = []
+    segmentation_data: Optional[Dict[str, Any]] = None
+    processed_at: datetime
+    model_name: str
+    model_version: str
+    
+    @validator('segmentation_data', pre=True)
+    def parse_segmentation_data(cls, v):
+        import json
+        if v and isinstance(v, str):
+            try:
+                return json.loads(v)
+            except Exception:
+                return None
+        return v
+
+
+class UploadResponse(BaseModel):
+    image: TomatoImage
+    message: str = "Image uploaded successfully"

app/services/model.py

@@ -0,0 +1,148 @@
+import numpy as np
+import cv2
+import time
+from typing import Dict, Optional, Any
+
+from app.core.config import settings
+from app.utils.image import load_image, resize_image, normalize_image
+
+
+class TomatoSegmentationModel:
+    """
+    Model for tomato disease severity segmentation.
+    
+    This class implements a simple image segmentation model using
+    conventional computer vision techniques as a placeholder.
+    In a production scenario, this would be replaced with a proper
+    deep learning model like U-Net, DeepLabV3, etc.
+    """
+    
+    def __init__(self):
+        self.model_name = settings.DEFAULT_MODEL_NAME
+        self.model_version = "0.1.0"
+        self.severity_classes = settings.SEVERITY_CLASSES
+        self.input_size = (224, 224)  # Standard input size
+        
+    def preprocess_image(self, image_path: str) -> Optional[np.ndarray]:
+        """Preprocess an image for analysis."""
+        # Load image
+        image = load_image(image_path)
+        if image is None:
+            return None
+            
+        # Resize
+        image = resize_image(image, self.input_size)
+        
+        # Normalize
+        image = normalize_image(image)
+        
+        return image
+        
+    def analyze_image(self, image_path: str) -> Dict[str, Any]:
+        """
+        Analyze a tomato image to determine disease severity.
+        
+        Args:
+            image_path: Path to the image file
+            
+        Returns:
+            Dictionary with analysis results including segmentation masks and severity scores
+        """
+        start_time = time.time()
+        
+        # Preprocess image
+        image = self.preprocess_image(image_path)
+        if image is None:
+            return {
+                "error": "Failed to load or process image",
+                "success": False
+            }
+        
+        # Simple color-based segmentation to identify potential disease areas
+        # This is a simplified placeholder implementation
+        
+        # Convert back to BGR for OpenCV
+        image_bgr = (image * 255).astype(np.uint8)
+        image_bgr = cv2.cvtColor(image_bgr, cv2.COLOR_RGB2BGR)
+        
+        # Convert to HSV for better color segmentation
+        image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)
+        
+        # Define color ranges for different severity classes
+        # These are placeholder values and would need to be calibrated for real use
+        color_ranges = {
+            "healthy": [(35, 50, 50), (85, 255, 255)],  # Green healthy leaves
+            "early_blight": [(15, 50, 50), (35, 255, 255)],  # Yellowish
+            "late_blight": [(0, 50, 50), (15, 255, 255)],  # Reddish-brown
+            "bacterial_spot": [(0, 0, 0), (180, 50, 100)],  # Dark spots
+            "septoria_leaf_spot": [(0, 0, 100), (180, 50, 255)]  # Light spots
+        }
+        
+        # Create segmentation masks and calculate affected areas
+        masks = {}
+        severity_details = []
+        total_pixels = image.shape[0] * image.shape[1]
+        
+        for severity_class, (lower, upper) in color_ranges.items():
+            # Create mask for this color range
+            lower = np.array(lower, dtype=np.uint8)
+            upper = np.array(upper, dtype=np.uint8)
+            mask = cv2.inRange(image_hsv, lower, upper)
+            
+            # Apply morphological operations to clean up the mask
+            kernel = np.ones((5, 5), np.uint8)
+            mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+            mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+            
+            # Calculate affected area percentage
+            affected_pixels = np.count_nonzero(mask)
+            affected_percentage = (affected_pixels / total_pixels) * 100
+            
+            # Generate a random confidence score for demo purposes
+            # In a real model, this would be the model's actual confidence
+            confidence = min(affected_percentage / 50, 1.0)
+            if severity_class == "healthy" and affected_percentage < 10:
+                confidence = 0.9  # Boost confidence for low healthy area (likely diseased)
+            
+            # Store results
+            masks[severity_class] = mask.tolist()  # Convert to list for JSON serialization
+            severity_details.append({
+                "severity_class": severity_class,
+                "confidence": float(confidence),
+                "affected_area_percentage": float(affected_percentage)
+            })
+        
+        # Determine primary severity class
+        severity_details.sort(key=lambda x: x["confidence"], reverse=True)
+        primary_severity = severity_details[0]["severity_class"]
+        severity_confidence = severity_details[0]["confidence"]
+        
+        # If healthy has high confidence, it takes precedence
+        for detail in severity_details:
+            if detail["severity_class"] == "healthy" and detail["confidence"] > 0.7:
+                primary_severity = "healthy"
+                severity_confidence = detail["confidence"]
+                break
+        
+        # Prepare results
+        processing_time = int((time.time() - start_time) * 1000)  # ms
+        
+        result = {
+            "success": True,
+            "model_name": self.model_name,
+            "model_version": self.model_version,
+            "primary_severity": primary_severity,
+            "severity_confidence": severity_confidence,
+            "severity_details": severity_details,
+            "segmentation_data": {
+                "image_size": self.input_size,
+                "masks": masks
+            },
+            "processing_time_ms": processing_time
+        }
+        
+        return result
+
+
+# Singleton instance
+segmentation_model = TomatoSegmentationModel()

app/utils/image.py

@@ -0,0 +1,88 @@
+import cv2
+import numpy as np
+from pathlib import Path
+import uuid
+import os
+from datetime import datetime
+from typing import Tuple, Dict, Any, Optional
+
+from app.core.config import settings
+
+
+def save_uploaded_image(file_data: bytes, filename: str) -> Dict[str, Any]:
+    """
+    Save an uploaded image to the storage directory and return metadata.
+    
+    Args:
+        file_data: The binary content of the uploaded file
+        filename: Original filename of the uploaded file
+        
+    Returns:
+        Dict with image metadata including path, size, etc.
+    """
+    # Generate a unique filename to avoid conflicts
+    extension = Path(filename).suffix.lower()
+    date_prefix = datetime.now().strftime("%Y%m%d")
+    unique_id = str(uuid.uuid4())
+    safe_filename = f"{date_prefix}_{unique_id}{extension}"
+    
+    # Create full path
+    file_path = settings.IMAGE_DIR / safe_filename
+    
+    # Write file to disk
+    with open(file_path, "wb") as f:
+        f.write(file_data)
+    
+    # Get image dimensions if it's an image file
+    width, height = None, None
+    try:
+        img = cv2.imread(str(file_path))
+        if img is not None:
+            height, width, _ = img.shape
+    except Exception:
+        pass
+    
+    return {
+        "file_path": str(file_path),
+        "filename": filename,
+        "file_size": len(file_data),
+        "width": width,
+        "height": height,
+        "mime_type": get_mime_type(extension)
+    }
+
+
+def get_mime_type(extension: str) -> str:
+    """Map file extension to MIME type."""
+    mime_types = {
+        ".jpg": "image/jpeg",
+        ".jpeg": "image/jpeg",
+        ".png": "image/png",
+        ".gif": "image/gif",
+        ".bmp": "image/bmp",
+        ".tiff": "image/tiff",
+        ".tif": "image/tiff",
+    }
+    return mime_types.get(extension.lower(), "application/octet-stream")
+
+
+def load_image(file_path: str) -> Optional[np.ndarray]:
+    """Load an image from file path."""
+    if not os.path.exists(file_path):
+        return None
+    
+    img = cv2.imread(file_path)
+    if img is None:
+        return None
+    
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    
+
+def resize_image(image: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:
+    """Resize image to target size."""
+    return cv2.resize(image, target_size, interpolation=cv2.INTER_AREA)
+
+
+def normalize_image(image: np.ndarray) -> np.ndarray:
+    """Normalize image pixel values to [0, 1]."""
+    return image.astype(np.float32) / 255.0

main.py

@@ -0,0 +1,35 @@
+import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from app.api.routes import health, tomato, model
+from app.core.config import settings
+
+app = FastAPI(
+    title=settings.PROJECT_NAME,
+    description="Tomato Severity Segmentation Model API",
+    version="0.1.0",
+    docs_url="/docs",
+    redoc_url="/redoc",
+)
+
+# Set up CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include API routes
+app.include_router(health.router, tags=["health"])
+app.include_router(tomato.router, prefix="/api/tomatoes", tags=["tomatoes"])
+app.include_router(model.router, prefix="/api/model", tags=["model"])
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=settings.DEBUG,
+    )

migrations/env.py

@@ -0,0 +1,79 @@
+from logging.config import fileConfig
+
+from sqlalchemy import engine_from_config
+from sqlalchemy import pool
+
+from alembic import context
+from app.models.tomato import Base
+
+# this is the Alembic Config object, which provides
+# access to the values within the .ini file in use.
+config = context.config
+
+# Interpret the config file for Python logging.
+# This line sets up loggers basically.
+fileConfig(config.config_file_name)
+
+# add your model's MetaData object here
+# for 'autogenerate' support
+target_metadata = Base.metadata
+
+# other values from the config, defined by the needs of env.py,
+# can be acquired:
+# my_important_option = config.get_main_option("my_important_option")
+# ... etc.
+
+
+def run_migrations_offline():
+    """Run migrations in 'offline' mode.
+
+    This configures the context with just a URL
+    and not an Engine, though an Engine is acceptable
+    here as well.  By skipping the Engine creation
+    we don't even need a DBAPI to be available.
+
+    Calls to context.execute() here emit the given string to the
+    script output.
+
+    """
+    url = config.get_main_option("sqlalchemy.url")
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,
+        literal_binds=True,
+        dialect_opts={"paramstyle": "named"},
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def run_migrations_online():
+    """Run migrations in 'online' mode.
+
+    In this scenario we need to create an Engine
+    and associate a connection with the context.
+
+    """
+    connectable = engine_from_config(
+        config.get_section(config.config_ini_section),
+        prefix="sqlalchemy.",
+        poolclass=pool.NullPool,
+    )
+
+    with connectable.connect() as connection:
+        is_sqlite = connection.dialect.name == 'sqlite'
+        context.configure(
+            connection=connection, 
+            target_metadata=target_metadata,
+            render_as_batch=is_sqlite  # Key configuration for SQLite
+        )
+
+        with context.begin_transaction():
+            context.run_migrations()
+
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    run_migrations_online()

migrations/script.py.mako

@@ -0,0 +1,24 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision = ${repr(up_revision)}
+down_revision = ${repr(down_revision)}
+branch_labels = ${repr(branch_labels)}
+depends_on = ${repr(depends_on)}
+
+
+def upgrade():
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade():
+    ${downgrades if downgrades else "pass"}

migrations/versions/e5d7de4b3a28_initial_migration.py

@@ -0,0 +1,58 @@
+"""Initial migration
+
+Revision ID: e5d7de4b3a28
+Revises: 
+Create Date: 2023-10-10 12:00:00.000000
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = 'e5d7de4b3a28'
+down_revision = None
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # Create tomato_images table
+    op.create_table('tomato_images',
+        sa.Column('id', sa.String(length=36), primary_key=True, index=True),
+        sa.Column('filename', sa.String(length=255), nullable=False),
+        sa.Column('file_path', sa.String(length=512), nullable=False, unique=True),
+        sa.Column('file_size', sa.Integer(), nullable=False),
+        sa.Column('mime_type', sa.String(length=50), nullable=False),
+        sa.Column('width', sa.Integer(), nullable=True),
+        sa.Column('height', sa.Integer(), nullable=True),
+        sa.Column('uploaded_at', sa.DateTime(), nullable=False),
+    )
+    
+    # Create analysis_results table
+    op.create_table('analysis_results',
+        sa.Column('id', sa.String(length=36), primary_key=True, index=True),
+        sa.Column('image_id', sa.String(length=36), sa.ForeignKey('tomato_images.id', ondelete='CASCADE'), nullable=False),
+        sa.Column('model_name', sa.String(length=255), nullable=False),
+        sa.Column('model_version', sa.String(length=50), nullable=False),
+        sa.Column('primary_severity', sa.String(length=50), nullable=True),
+        sa.Column('severity_confidence', sa.Float(), nullable=True),
+        sa.Column('segmentation_data', sa.Text(), nullable=True),
+        sa.Column('processed_at', sa.DateTime(), nullable=False),
+        sa.Column('processing_time_ms', sa.Integer(), nullable=True),
+    )
+    
+    # Create severity_details table
+    op.create_table('severity_details',
+        sa.Column('id', sa.String(length=36), primary_key=True, index=True),
+        sa.Column('analysis_id', sa.String(length=36), sa.ForeignKey('analysis_results.id', ondelete='CASCADE'), nullable=False),
+        sa.Column('severity_class', sa.String(length=50), nullable=False),
+        sa.Column('confidence', sa.Float(), nullable=False),
+        sa.Column('affected_area_percentage', sa.Float(), nullable=True),
+    )
+
+
+def downgrade():
+    op.drop_table('severity_details')
+    op.drop_table('analysis_results')
+    op.drop_table('tomato_images')

requirements.txt

@@ -0,0 +1,26 @@
+# FastAPI and server
+fastapi>=0.95.0
+uvicorn>=0.21.1
+python-multipart>=0.0.6
+pydantic>=2.0.0
+pydantic-settings>=2.0.0
+
+# Database
+sqlalchemy>=2.0.0
+alembic>=1.10.0
+
+# Image processing and ML
+opencv-python-headless>=4.5.0
+numpy>=1.20.0
+scikit-image>=0.19.0
+scikit-learn>=1.0.0
+torch>=2.0.0
+torchvision>=0.15.0
+
+# Utils
+python-dotenv>=1.0.0
+httpx>=0.24.0
+
+# Code quality
+ruff>=0.1.0
+black>=23.0.0