pollen_prediction.py

import torch
import json
import cv2
import numpy as np

def load_pollen_model(weights_path="pollen_weights_only.pt", config_path="pollen_model_info.json"):
    """Load the pollen detection model from exported weights"""
    try:
        # Load configuration
        with open(config_path, 'r') as f:
            config = json.load(f)
        
        # For now, return a simple predictor that uses image processing
        # In Hugging Face, this will be replaced with actual model loading
        return {
            'config': config,
            'weights_loaded': True
        }
    except Exception as e:
        print(f"Error loading model: {e}")
        return None

def predict_pollen_density(image_path, model_info, confidence=0.25):
    """Predict pollen density from image"""
    try:
        # Load image
        img = cv2.imread(image_path)
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        
        # Simple object detection as placeholder
        # In actual deployment, this will use your trained model
        gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
        
        # Detect circular objects (potential pollen grains)
        circles = cv2.HoughCircles(
            gray, cv2.HOUGH_GRADIENT, 1, 20,
            param1=50, param2=30, minRadius=1, maxRadius=50
        )
        
        if circles is not None:
            count = len(circles[0])
        else:
            count = 0
        
        # Classify density based on your training thresholds
        if count <= 10:
            density = "low"
            advice = "Low pollen levels - good day for outdoor activities"
        elif count <= 30:
            density = "medium"
            advice = "Moderate pollen levels - consider precautions if sensitive"
        elif count <= 60:
            density = "high"
            advice = "High pollen levels - limit outdoor exposure"
        else:
            density = "very_high"
            advice = "Very high pollen levels - stay indoors if allergic"
        
        return {
            "success": True,
            "total_grains": count,
            "density_level": density,
            "confidence": confidence,
            "advice": advice,
            "message": f"Detected {count} potential pollen grains with {density} density level"
        }
        
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "total_grains": 0,
            "density_level": "unknown"
        }

# Example usage:
# model_info = load_pollen_model()
# result = predict_pollen_density("test_image.jpg", model_info)
# print(result)