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| import gradio as gr | |
| from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer | |
| import sentence_transformers | |
| from sentence_transformers import SentenceTransformer, util | |
| import pickle | |
| from PIL import Image | |
| import os | |
| ## Define model | |
| model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") | |
| #Open the precomputed embeddings | |
| emb_filename = 'unsplash-25k-photos-embeddings.pkl' | |
| with open(emb_filename, 'rb') as fIn: | |
| img_names, img_emb = pickle.load(fIn) | |
| def search_text(query, top_k=1): | |
| """" Search an image based on the text query. | |
| Args: | |
| query ([string]): [query you want search for] | |
| top_k (int, optional): [Amount of images o return]. Defaults to 1. | |
| Returns: | |
| [list]: [list of images that are related to the query.] | |
| """ | |
| # First, we encode the query. | |
| inputs = tokenizer([query], padding=True, return_tensors="pt") | |
| query_emb = model.get_text_features(**inputs) | |
| # Then, we use the util.semantic_search function, which computes the cosine-similarity | |
| # between the query embedding and all image embeddings. | |
| # It then returns the top_k highest ranked images, which we output | |
| hits = util.semantic_search(query_emb, img_emb, top_k=top_k)[0] | |
| image=[] | |
| for hit in hits: | |
| print(img_names[hit['corpus_id']]) | |
| object = Image.open(os.path.join("photos/", img_names[hit['corpus_id']])) | |
| image.append(object) | |
| return image | |
| iface = gr.Interface( | |
| title = "Text to Image using CLIP Model", | |
| description = "Gradio Demo fo CLIP model. \n This demo is based on assessment for the :hugging: Huggingface course 2. \n To use it, simply write which image you are looking for. Read more at the links below.", | |
| fn=search_text, | |
| inputs=[gr.inputs.Textbox(lines=2, | |
| label="Write what you are looking for...", | |
| placeholder="Name Here..."), | |
| gr.inputs.Slider(0, 5, step=1)], | |
| outputs=gr.outputs.Carousel(gr.outputs.Image(type="pil")) | |
| ,examples=[[("Dog in the beach"), 2], | |
| [("Paris during night."), 1], | |
| [("A cute kangaroo"), 5], | |
| [("Dois cachorros"), 2], | |
| [("un homme marchant sur le parc"), 3]] | |
| ).launch(debug=True) |