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| import gradio as gr | |
| import torch | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| import json | |
| # Load ImageNet class labels | |
| LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json" | |
| response = requests.get(LABELS_URL) | |
| labels = json.loads(response.text) | |
| def load_model(): | |
| """ | |
| Load model and processor from Hugging Face Hub | |
| """ | |
| model_id = "jatingocodeo/ImageNet" # Updated model repository ID | |
| model = AutoModelForImageClassification.from_pretrained(model_id) | |
| processor = AutoImageProcessor.from_pretrained(model_id) | |
| return model, processor | |
| def predict(image): | |
| """ | |
| Make prediction on input image | |
| """ | |
| if image is None: | |
| return None | |
| try: | |
| # Load model and processor (with caching) | |
| model, processor = load_model() | |
| model.eval() | |
| # Process image | |
| inputs = processor(image, return_tensors="pt") | |
| # Get predictions | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # Get probabilities and classes | |
| probs = torch.nn.functional.softmax(logits, dim=1)[0] | |
| top_probs, top_indices = torch.topk(probs, k=5) | |
| # Format results | |
| results = {} | |
| for prob, idx in zip(top_probs, top_indices): | |
| label = labels[idx.item()] | |
| confidence = prob.item() * 100 | |
| results[label] = confidence | |
| return results | |
| except Exception as e: | |
| return f"Error processing image: {str(e)}" | |
| # Create Gradio interface | |
| title = "ImageNet Classifier" | |
| description = """ | |
| ## ResNet50 ImageNet Classifier | |
| This model classifies images into 1000 ImageNet categories. Upload an image or use one of the example images to get predictions. | |
| ### Instructions: | |
| 1. Upload an image using the input box below | |
| 2. The model will predict the top 5 classes for the image | |
| 3. Results show class names and confidence scores | |
| ### Model Details: | |
| - Architecture: ResNet50 | |
| - Dataset: ImageNet | |
| - Input Size: 224x224 | |
| - Number of Classes: 1000 | |
| """ | |
| # Example images | |
| examples = [ | |
| "examples/dog.jpg", | |
| "examples/cat.jpg", | |
| "examples/bird.jpg", | |
| "examples/car.jpg", | |
| "examples/flower.jpg" | |
| ] | |
| # Create the interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil", label="Upload Image"), | |
| outputs=gr.Label(num_top_classes=5, label="Predictions"), | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| theme=gr.themes.Soft(), | |
| allow_flagging="never" | |
| ) | |
| # Launch the app | |
| iface.launch() |