predict.py

import os
import torch
import pandas as pd
from torchvision import transforms
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image

def load_model(model_path):
    """Load the pre-trained model."""
    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224', num_labels=13, ignore_mismatched_sizes=True)
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')), strict=False)
    model.eval()
    return model

def preprocess_image(image_path):
    """Preprocess the image for prediction."""
    feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
    image = Image.open(image_path).convert("RGB")
    image = feature_extractor(images=image, return_tensors="pt")["pixel_values"]
    return image

def predict(model, image_path):
    """Predict the class probabilities for an image."""
    image = preprocess_image(image_path)
    with torch.no_grad():
        outputs = model(image).logits
    probabilities = torch.softmax(outputs, dim=1)
    return probabilities

def main(input_path, model_path, output_file):
    """Main function to predict and save results to Excel."""
    model = load_model(model_path)
    results = []

    if os.path.isdir(input_path):
        for img_name in os.listdir(input_path):
            img_path = os.path.join(input_path, img_name)
            if img_path.endswith(('.png', '.jpg', '.jpeg')):  # Check for image file types
                probs = predict(model, img_path).cpu().numpy()[0]
                result = {"Image Name": img_name}
                for i, prob in enumerate(probs):
                    result[f"Class {i} Probability"] = prob  # Store probabilities
                results.append(result)
    else:
        # If a single image file is provided
        probs = predict(model, input_path).cpu().numpy()[0]
        result = {"Image Name": os.path.basename(input_path)}
        for i, prob in enumerate(probs):
            result[f"Class {i} Probability"] = prob  # Store probabilities
        results.append(result)

    # Create DataFrame and save to Excel
    df = pd.DataFrame(results)
    df.to_excel(output_file, index=False)
    print(f"Results saved to {output_file}")

# Example call
input_path = '/content/ddd.jpg'  # Replace with your image folder or single image path
model_path = '/content/model.pth'  # Replace with your model path
output_file = 'predictions.xlsx'  # Name of the output Excel file
main(input_path, model_path, output_file)