Generate Spatial Quality Heatmaps for Industrial Surfaces with Perception Encoder and Supervision

                      
                     
"""
Production implementation for generating spatial quality heatmaps for industrial surfaces using a perception encoder with supervision.
Provides secure, scalable operations for processing and visualizing surface quality data.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import numpy as np
import pandas as pd
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker, scoped_session
import time

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///heatmap.db')

# Database connection pooling setup
engine = create_engine(Config.database_url, pool_size=5, max_overflow=10)
Session = scoped_session(sessionmaker(bind=engine))

# Pydantic model for input validation
class HeatmapRequest(BaseModel):
    surface_id: str = Field(..., description="Unique identifier for the industrial surface")
    data: List[Dict[str, Any]] = Field(..., description="List of measurement records")

async def validate_input(data: HeatmapRequest) -> bool:
    """Validate request data.
    
    Args:
        data: HeatmapRequest model instance
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not data.surface_id:
        raise ValueError('Missing surface_id')
    if not data.data:
        raise ValueError('No measurement data provided')
    return True

async def sanitize_fields(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Sanitize input data fields.
    
    Args:
        data: List of measurement records
    Returns:
        Sanitized list of records
    """
    sanitized_data = []
    for record in data:
        sanitized_record = {key: str(value).strip() for key, value in record.items()}
        sanitized_data.append(sanitized_record)
    return sanitized_data

async def normalize_data(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """Normalize and convert input data to DataFrame.
    
    Args:
        data: List of measurement records
    Returns:
        A DataFrame representation of the data
    """
    df = pd.DataFrame(data)
    return df.fillna(0)  # Replace NaN with zero

async def process_batch(df: pd.DataFrame) -> Tuple[np.ndarray, np.ndarray]:
    """Process the DataFrame to generate heatmap data.
    
    Args:
        df: DataFrame containing normalized data
    Returns:
        Tuple containing heatmap matrix and coordinates
    """
    heatmap_matrix = np.zeros((100, 100))  # Example heatmap size
    coordinates = []
    for index, row in df.iterrows():
        x, y, value = row['x_coord'], row['y_coord'], row['value']
        heatmap_matrix[int(x), int(y)] += value
        coordinates.append((x, y))
    return heatmap_matrix, coordinates

async def save_to_db(surface_id: str, heatmap_matrix: np.ndarray) -> None:
    """Save the generated heatmap to the database.
    
    Args:
        surface_id: Unique identifier for the surface
        heatmap_matrix: Matrix of heatmap data
    """
    session = Session()
    try:
        # Example SQL to save heatmap
        heatmap_data = heatmap_matrix.flatten().tolist()
        session.execute(text("INSERT INTO heatmaps (surface_id, data) VALUES (:surface_id, :data)"),
            {'surface_id': surface_id, 'data': heatmap_data})
        session.commit()
    except Exception as e:
        session.rollback()
        logger.error(f'Error saving heatmap to database: {e}')
        raise HTTPException(status_code=500, detail="Database error")
    finally:
        session.close()

async def fetch_data(surface_id: str) -> List[Dict[str, Any]]:
    """Fetch surface data from the database.
    
    Args:
        surface_id: Unique identifier for the surface
    Returns:
        List of measurement records
    """
    session = Session()
    try:
        result = session.execute(text("SELECT * FROM measurements WHERE surface_id = :surface_id"),
                                 {'surface_id': surface_id})
        return [dict(row) for row in result]
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise HTTPException(status_code=500, detail="Database error")
    finally:
        session.close()

app = FastAPI()

@app.post("/generate_heatmap/")
async def generate_heatmap(request: HeatmapRequest):
    """Endpoint to generate heatmap for industrial surfaces.
    
    Args:
        request: HeatmapRequest model instance
    Returns:
        Message confirming heatmap generation
    """
    try:
        # Validate input
        await validate_input(request)
        # Sanitize fields
        sanitized_data = await sanitize_fields(request.data)
        # Normalize data
        df = await normalize_data(sanitized_data)
        # Process batch and generate heatmap
        heatmap_matrix, coordinates = await process_batch(df)
        # Save heatmap to DB
        await save_to_db(request.surface_id, heatmap_matrix)
        return {"message": "Heatmap generated successfully!"}
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')
        raise HTTPException(status_code=400, detail=str(ve))
    except Exception as e:
        logger.error(f'Unhandled error: {e}')
        raise HTTPException(status_code=500, detail="Internal Server Error")

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)