model_handler.py

import torch
import torchvision.transforms as transforms
from PIL import Image
import torch.nn.functional as F
import random
from typing import Tuple
import os

# Predefined classes for classification (using standard ImageNet classes as example)
CLASSES = [
    "tench", "goldfish", "great_white_shark", "tiger_shark", "hammerhead", 
    "electric_ray", "stingray", "cock", "hen", "ostrich",
    "brambling", "goldfinch", "house_finch", "junco", "indigo_bunting",
    "robin", "bulbul", "jay", "magpie", "chickadee",
    "water_ouzel", "kite", "bald_eagle", "vulture", "great_grey_owl"
]

class ImageClassifier:
    def __init__(self):
        # In a real application, you would load your trained PyTorch model here
        # Example: self.model = torch.load("path/to/your/model.pth")
        # For this template, we'll initialize a placeholder for a model
        self.classes = CLASSES
        self.model = self._load_model()
        
    def _load_model(self):
        """
        Load the PyTorch model during initialization
        In a real application, this would load your trained model
        """
        print("Loading PyTorch model...")
        return None 
    
        # try:
        #     # For this template, we'll simulate loading a pretrained model
        #     # In a real application, you would use:
        #     # model = torch.load("path/to/your/model.pth", map_location='cpu')
        #     # model.eval()  # Set model to evaluation mode
        #     model = torchvision.models.resnet18(pretrained=True)
        #     model.eval()
        #     print("Model loaded successfully!")
        #     return model
        # except Exception as e:
        #     print(f"Error loading model: {e}")
        #     print("Using simulated model for demonstration purposes")
        #     return None
    
    def preprocess_image(self, image_path: str) -> torch.Tensor:
        """
        Preprocess the input image to prepare it for the model
        """
        image = Image.open(image_path)
        
        preprocess = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])
        
        input_tensor = preprocess(image)
        input_batch = input_tensor.unsqueeze(0)  # Create a mini-batch as expected by the model
        
        return input_batch
    
    def predict(self, image_path: str) -> Tuple[str, float]:
        """
        Perform prediction on the given image using the loaded model
        """
        if self.model is not None:
            try:
                # Preprocess the image
                input_batch = self.preprocess_image(image_path)
                
                # Perform inference
                with torch.no_grad():  # Disable gradient computation for inference
                    output = self.model(input_batch)
                
                # Convert to probabilities using softmax
                probabilities = torch.nn.functional.softmax(output[0], dim=0)
                
                # Get the top prediction
                top_prob, top_catid = torch.topk(probabilities, 1)
                
                # Map to class label
                predicted_class_idx = top_catid.item()
                confidence = top_prob.item()
                
                # Handle case where model outputs more classes than we have names for
                if predicted_class_idx < len(self.classes):
                    predicted_class = self.classes[predicted_class_idx]
                else:
                    # If model predicts a class not in our predefined list, pick a random one
                    predicted_class = self.classes[random.randint(0, len(self.classes)-1)]
                    confidence = round(random.uniform(0.5, 1.0), 3)
                
                return predicted_class, confidence
                
            except Exception as e:
                print(f"Error during prediction: {e}")
                # Fallback to simulated prediction
                return self._simulate_prediction()
        else:
            # If model failed to load, use simulation
            return self._simulate_prediction()
    
    def _simulate_prediction(self) -> Tuple[str, float]:
        """
        Simulate model prediction when actual model is not available
        """
        # In a real application, this shouldn't happen as model loading should succeed
        predicted_idx = random.randint(0, len(self.classes) - 1)
        predicted_class = self.classes[predicted_idx]
        confidence = round(random.uniform(0.5, 1.0), 3)
        
        return predicted_class, confidence