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| import gradio as gr | |
| import torch | |
| import torch.nn as nn | |
| import torchvision.models as models | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| from huggingface_hub import hf_hub_download | |
| import json | |
| ######################################## | |
| # 1. Define the Model Architecture | |
| ######################################## | |
| class MultiTaskModel(nn.Module): | |
| def __init__(self, backbone, feature_dim, num_obj_classes): | |
| super(MultiTaskModel, self).__init__() | |
| self.backbone = backbone | |
| self.obj_head = nn.Linear(feature_dim, num_obj_classes) | |
| self.bin_head = nn.Linear(feature_dim, 2) | |
| def forward(self, x): | |
| feats = self.backbone(x) | |
| obj_logits = self.obj_head(feats) | |
| bin_logits = self.bin_head(feats) | |
| return obj_logits, bin_logits | |
| ######################################## | |
| # 2. Load the Label Mapping and Set num_obj_classes | |
| ######################################## | |
| # Load the saved mapping from JSON | |
| with open("obj_label_mapping.json", "r") as f: | |
| obj_label_to_idx = json.load(f) | |
| # Use the mapping as-is; do not override it. | |
| num_obj_classes = len(obj_label_to_idx) | |
| # Create the inverse mapping | |
| idx_to_obj_label = {v: k for k, v in obj_label_to_idx.items()} | |
| bin_label_names = ["AI-Generated", "Real"] | |
| ######################################## | |
| # 3. Define Validation Transforms | |
| ######################################## | |
| val_transforms = 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]) | |
| ]) | |
| ######################################## | |
| # 4. Reconstruct the Model and Load Weights | |
| ######################################## | |
| device = torch.device("cpu") | |
| resnet = models.resnet50(pretrained=False) | |
| resnet.fc = nn.Identity() | |
| feature_dim = 2048 | |
| # Build the model architecture. | |
| model = MultiTaskModel(resnet, feature_dim, num_obj_classes) | |
| model.to(device) | |
| repo_id = "Abdu07/multitask-model" | |
| filename = "DualSight.pth" # Ensure this checkpoint is from training with the same num_obj_classes | |
| weights_path = hf_hub_download(repo_id=repo_id, filename=filename) | |
| state_dict = torch.load(weights_path, map_location="cpu") | |
| model.load_state_dict(state_dict) | |
| model.eval() | |
| ######################################## | |
| # 5. Define the Inference Function | |
| ######################################## | |
| def predict_image(img: Image.Image) -> str: | |
| img = img.convert("RGB") | |
| img_tensor = val_transforms(img).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| obj_logits, bin_logits = model(img_tensor) | |
| obj_pred = torch.argmax(obj_logits, dim=1).item() | |
| bin_pred = torch.argmax(bin_logits, dim=1).item() | |
| obj_name = idx_to_obj_label.get(obj_pred, "Unknown") | |
| bin_name = bin_label_names[bin_pred] | |
| return f"Prediction: {obj_name} ({bin_name})" | |
| ######################################## | |
| # 6. Create Gradio UI | |
| ######################################## | |
| demo = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title="Multi-Task Image Classifier Trained by [Abdellahi El Moustapha](https://abmstpha.github.io/), | |
| description="Upload an image to receive two predictions:\n1) The primary object in the image,\n2) Whether the image is AI-generated or Real." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", share=True) | |