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| import gradio as gr | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| import requests | |
| import traceback | |
| import os | |
| dataset = load_dataset("SaladSlayer00/twin_matcher") | |
| image_classifier = pipeline("image-classification", model="SaladSlayer00/twin_matcher") | |
| def fetch_info(celebrity_label): | |
| try: | |
| parts = celebrity_label.split("_") | |
| formatted_label = " ".join([part.capitalize() for part in parts]) | |
| api_url = f'https://api.api-ninjas.com/v1/celebrity?name={formatted_label}' | |
| token = os.getenv('TOKEN') | |
| response = requests.get(api_url, headers={'X-Api-Key': token}) | |
| if response.status_code == 200: | |
| return response.text | |
| else: | |
| return "Description not available." | |
| except Exception as e: | |
| print(f"Error fetching information: {e}") | |
| traceback.print_exc() | |
| return "Description not available." | |
| # Function to fetch images for the predicted label from the Hugging Face dataset | |
| def fetch_images_for_label(label): | |
| label_data = dataset['train'].filter(lambda example: example['label'] == label) | |
| images = [example['image'] for example in label_data] | |
| return images | |
| # Function to process the image, predict, and fetch images and info | |
| def predict_and_fetch_images(input_image): | |
| try: | |
| # Use the image classifier pipeline | |
| predictions = image_classifier(input_image) | |
| top_prediction = max(predictions, key=lambda x: x['score']) | |
| label, score = top_prediction['label'], top_prediction['score'] | |
| # Fetch images for the predicted label | |
| images = fetch_images_for_label(label) | |
| # Fetch information for the predicted label | |
| info = fetch_info(label) | |
| return label, score, images, info, "No Error" | |
| except Exception as e: | |
| print(f"Error during prediction: {e}") | |
| traceback.print_exc() | |
| return "Error during prediction", 0, [], "N/A", str(e) | |
| # Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_and_fetch_images, | |
| inputs=gr.Image(type="pil", label="Upload or Take a Snapshot"), | |
| outputs=[ | |
| "text", # Predicted label | |
| "number", # Prediction score | |
| gr.Gallery(label="Lookalike Images"), # Slideshow component for images | |
| "text", # Info/Description | |
| gr.Textbox(type="text", label="Feedback", placeholder="Provide feedback here") # Feedback textbox | |
| ], | |
| live=True, | |
| title="Celebrity Lookalike Predictor with Description and Feedback", | |
| description="Take a snapshot or upload an image to see which celebrity you look like!" | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch() | |