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+unsplash-25k-photos-embeddings.pkl filter=lfs diff=lfs merge=lfs -text

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+venv/

LICENSE

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README.md

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+---
+title: Text2image Search
+emoji: 🐠
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 3.24.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import pathlib
+import pickle
+import zipfile
+
+import gradio as gr
+from PIL import Image
+from sentence_transformers import util
+from tqdm.autonotebook import tqdm
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
+
+img_folder = 'photos/'
+if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
+    os.makedirs(img_folder, exist_ok=True)
+
+    photo_filename = 'unsplash-25k-photos.zip'
+    if not os.path.exists(photo_filename):   #Download dataset if does not exist
+        util.http_get('http://sbert.net/datasets/'+photo_filename, photo_filename)
+
+    #Extract all images
+    with zipfile.ZipFile(photo_filename, 'r') as zf:
+        for member in tqdm(zf.infolist(), desc='Extracting'):
+            zf.extract(member, img_folder)
+
+cwd = pathlib.Path(__file__).parent.absolute()
+
+# 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")
+
+emb_filename = "unsplash-25k-photos-embeddings.pkl"
+emb_path = cwd / emb_filename
+if not os.path.exists(emb_filename):
+    util.http_get("http://sbert.net/datasets/" + emb_filename, emb_path)
+with open(emb_path, "rb") as fIn:
+    img_names, img_emb = pickle.load(fIn)
+
+
+def search_text(query):
+    """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.]
+    """
+    inputs = tokenizer([query], padding=True, return_tensors="pt")
+    query_emb = model.get_text_features(**inputs)
+
+    hits = util.semantic_search(query_emb, img_emb, top_k=8)[0]
+    # Create list of tuples with image name and similarity score
+    images = []
+    for hit in hits:
+        try:
+            image = Image.open(cwd / "photos" / img_names[hit["corpus_id"]])
+            images.append((image, f"{hit['score'] * 100:.2f}%"))
+        except Exception as e:
+            print(e)
+    return images
+
+
+gr.Interface(
+    title="Text2Image search using CLIP model",
+    description="This is a demo of the CLIP model. It is a multimodal model that can be used for zero-shot image classification, image captioning, image-text retrieval, and more. It is trained on a variety of data sources, including images from the internet, text from Wikipedia, and captions from Flickr.",
+    article="By Julien Assuied, Elie Brosset, Lucas Chapron et Alexis Japas",
+    fn=search_text,
+    theme="gstaff/sketch",
+    allow_flagging="never",
+    inputs=[
+        gr.Textbox(
+            lines=2,
+            label="What do you want to see ?",
+            placeholder="Write the prompt here...",
+        ),
+    ],
+    outputs=[
+        gr.Gallery(
+            label="Most similar images", show_label=True, elem_id="gallery"
+        ).style(grid=[2], height="auto"),
+    ],
+    examples=[
+        [("Two cats")],
+        [("A plane flying")],
+        [("A family picture")],
+        [("un homme marchant sur le parc")],
+    ],
+).launch(debug=True)

download_images.py

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+import os
+import zipfile
+
+from sentence_transformers import util
+from tqdm.autonotebook import tqdm
+
+img_folder = 'photos/'
+if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
+    os.makedirs(img_folder, exist_ok=True)
+
+    photo_filename = 'unsplash-25k-photos.zip'
+    if not os.path.exists(photo_filename):   #Download dataset if does not exist
+        util.http_get('http://sbert.net/datasets/'+photo_filename, photo_filename)
+
+    #Extract all images
+    with zipfile.ZipFile(photo_filename, 'r') as zf:
+        for member in tqdm(zf.infolist(), desc='Extracting'):
+            zf.extract(member, img_folder)

requirements.txt

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+transformers
+sentence-transformers
+torch
+gradio

unsplash-25k-photos-embeddings.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e60d49bd6334c29bc15fc2bc18c30b6d047a5584ad67c793eba376e95eaef8e
+size 51816207