Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| from PIL import Image | |
| import requests | |
| import io | |
| # Load your custom dataset | |
| def load_my_dataset(): | |
| try: | |
| dataset = load_dataset("Avmromanov/tripoexamples") | |
| return dataset | |
| except Exception as e: | |
| print(f"Error loading dataset: {e}") | |
| return None | |
| # Load car classification model | |
| car_classifier = pipeline("image-classification", | |
| model="dima806/car_brand_classification") | |
| def identify_car(image): | |
| if image is None: | |
| return "Please upload an image of a car" | |
| try: | |
| if image.mode != 'RGB': | |
| image = image.convert('RGB') | |
| predictions = car_classifier(image) | |
| result_text = "🚗 Car Identification Results:\n\n" | |
| top_5 = predictions[:5] | |
| for i, pred in enumerate(top_5, 1): | |
| label = pred['label'].replace('_', ' ').title() | |
| confidence = pred['score'] | |
| result_text += f"{i}. {label}: {confidence:.2%}\n" | |
| result_text += f"\n🔍 Most likely: **{top_5[0]['label'].replace('_', ' ').title()}** " \ | |
| f"(confidence: {top_5[0]['score']:.2%})" | |
| return result_text | |
| except Exception as e: | |
| return f"Error processing image: {str(e)}" | |
| def get_dataset_examples(dataset, num_examples=3): | |
| """Extract example images from the dataset""" | |
| examples = [] | |
| if dataset is None: | |
| return examples | |
| try: | |
| # Adjust this based on your dataset structure | |
| train_data = dataset['train'] | |
| for i in range(min(num_examples, len(train_data))): | |
| example = train_data[i] | |
| # The structure depends on your dataset - adjust accordingly | |
| if 'image' in example: | |
| # If images are stored in the dataset | |
| examples.append(example['image']) | |
| elif 'url' in example: | |
| # If URLs are provided | |
| examples.append(example['url']) | |
| elif 'path' in example: | |
| # If file paths are provided | |
| examples.append(example['path']) | |
| except Exception as e: | |
| print(f"Error extracting examples: {e}") | |
| return examples | |
| # Load your dataset | |
| my_dataset = load_my_dataset() | |
| dataset_examples = get_dataset_examples(my_dataset, num_examples=4) | |
| # Create the interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🚗 Car Identifier with My Dataset") | |
| gr.Markdown("Using examples from: **Avmromanov/tripoexamples**") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(label="Upload Car Photo", type="pil") | |
| identify_btn = gr.Button("Identify Car", variant="primary") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Results", lines=10) | |
| gr.Markdown(f"### Dataset Examples (showing {len(dataset_examples)} samples)") | |
| gr.Examples( | |
| examples=dataset_examples, | |
| inputs=image_input, | |
| outputs=output_text, | |
| fn=identify_car, | |
| cache_examples=True | |
| ) | |
| gr.Markdown(""" | |
| **Dataset Information:** | |
| - Name: tripoexamples | |
| - Author: Avmromanov | |
| - Type: Car images for identification | |
| """) | |
| demo.launch() |