initial commit

app.py

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+# 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() 

precomputed_clips.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f31005adeddd524deed221bff7b6f9b5acb313aad0bf6ee1b591b529c9a62369
+size 1322672

requirements.txt

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+numpy==1.26.4
+scipy==1.11.4
+scikit-learn==1.3.2
+fastai==2.7.17
+gradio==4.44.1
+timm==1.0.9
+torch==2.2.1
+torchvision==0.17.1
+setuptools