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| # to install transformers | |
| !pip install -qq transformers | |
| # import and precomputed clips | |
| import pickle | |
| precomputed_filename = 'precomputed_clips' | |
| def load_precomputed(precomputed_filename): | |
| with open(precomputed_filename + '.pickle', 'rb') as f: | |
| return pickle.load(f) | |
| precomputed_dict = load_precomputed(precomputed_filename) | |
| # embeddings and similar pictures | |
| import torch | |
| from PIL import Image | |
| from transformers import CLIPProcessor, CLIPModel | |
| import os | |
| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def get_clip_embeddings(input_data, input_type='text'): | |
| # Load the CLIP model and processor | |
| model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| # Prepare the input based on the type | |
| if input_type == 'text': | |
| inputs = processor(text=input_data, return_tensors="pt", padding=True, truncation=True) | |
| elif input_type == 'image': | |
| if isinstance(input_data, str): | |
| image = Image.open(input_data) | |
| elif isinstance(input_data, Image.Image): | |
| image = input_data | |
| else: | |
| raise ValueError("For image input, provide either a file path or a PIL Image object") | |
| inputs = processor(images=image, return_tensors="pt") | |
| else: | |
| raise ValueError("Invalid input_type. Choose 'text' or 'image'") | |
| # Get the embeddings | |
| with torch.no_grad(): | |
| if input_type == 'text': | |
| embeddings = model.get_text_features(**inputs) | |
| else: | |
| embeddings = model.get_image_features(**inputs) | |
| return embeddings.numpy() | |
| def find_similar_images(text_input, image_embeddings, all_images, take_best = 4): | |
| # Získání embeddingu pro text | |
| text_embedding = get_clip_embeddings(text_input, input_type='text') | |
| # Výpočet kosinové podobnosti mezi textem a obrázky | |
| similarities = cosine_similarity(text_embedding, image_embeddings) | |
| # Seřazení podle podobnosti | |
| best_indices = np.argsort(similarities[0])[::-1][:take_best] | |
| # Výběr nejlepších 4 obrázků | |
| best_images = [all_images[i] for i in best_indices] | |
| return [Image.open(img) for img in best_images] | |
| # find the most similar pictures compared to text inserted | |
| def find_most_similar(text_input): | |
| return find_similar_images(text_input, precomputed_dict['image_clips'], precomputed_dict['image_paths']) | |
| # gradio run | |
| import gradio as gr # Importing Gradio for creating the web interface | |
| # vytvoření Gradio rozhraní | |
| interface = gr.Interface( | |
| fn=find_most_similar, | |
| inputs="text", | |
| outputs=gr.Gallery(label="Most Similar Images"), | |
| title="Find Similar Images with CLIP", | |
| description="Enter a text prompt to find the most similar images." | |
| ) | |
| # app launch | |
| interface.launch() | |