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.gitattributes

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

README.md

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-# CLIP-image-search
-Deployment of CLIP model using Hugging Face space to perform image search.
+---
+title: CLIP Image Search
+emoji: 📸
+colorFrom: pink
+colorTo: pink
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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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):
+    """"
+
+    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)