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import gradio as gr
from PIL import Image
import torch
import numpy as np
import faiss
from transformers import (
GitProcessor,
GitForCausalLM,
AutoTokenizer,
AutoModelForCausalLM,
CLIPProcessor,
CLIPModel
)
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")
tokenizer_llama = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model_llama = AutoModelForCausalLM.from_pretrained(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
).eval()
text_encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device).eval()
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# Загрузка только первых 10000 изображений через streaming
MAX_IMAGES = 10_000
dataset_stream = load_dataset("huggan/wikiart", split="train", streaming=True)
first_10000 = [x for i, x in enumerate(dataset_stream) if i < MAX_IMAGES]
image_index = faiss.read_index("image_index_llama.faiss")
text_index = faiss.read_index("text_index_llama.faiss")
def clean_caption(text):
return text.replace("[ unused0 ]", "").strip()
def generate_captions(image: Image.Image):
inputs = git_processor(images=image, return_tensors="pt")["pixel_values"].to(device)
captions = []
with torch.no_grad():
deterministic_ids = git_model.generate(
pixel_values=inputs,
max_new_tokens=30,
do_sample=False
)
captions.append(clean_caption(git_processor.tokenizer.decode(deterministic_ids[0], skip_special_tokens=True)))
sampled_ids = git_model.generate(
pixel_values=inputs,
max_new_tokens=30,
do_sample=True,
top_k=100,
temperature=0.8,
num_return_sequences=2
)
sampled = git_processor.tokenizer.batch_decode(sampled_ids, skip_special_tokens=True)
captions.extend([clean_caption(c) for c in sampled])
return captions
def refine_caption(base, desc1, desc2):
prompt = f"""
Given the base caption that is true and factual:
\"{base}\"
And two descriptive captions:
1) {desc1}
2) {desc2}
Write a short, coherent description that is faithful to the base caption but incorporates descriptive elements from captions 1 and 2 without contradicting the original meaning.
"""
inputs = tokenizer_llama(prompt, return_tensors="pt").to(model_llama.device)
with torch.no_grad():
output = model_llama.generate(**inputs, max_new_tokens=100, do_sample=False)
text = tokenizer_llama.decode(output[0], skip_special_tokens=True)
answer = text[len(prompt):].strip()
for prefix in ["Example:", "example:"]:
if answer.startswith(prefix):
answer = answer[len(prefix):].strip()
return answer
def get_text_embedding(text):
emb = text_encoder.encode([text], normalize_embeddings=False).astype("float32")
faiss.normalize_L2(emb)
return emb
def get_image_embedding(image):
inputs = clip_processor(images=image, return_tensors="pt").to(device)
with torch.no_grad():
image_features = clip_model.get_image_features(**inputs)
emb = image_features.cpu().numpy().astype("float32")
faiss.normalize_L2(emb)
return emb
def get_results_with_images(embedding, index, top_k=2):
D, I = index.search(embedding, top_k)
results = []
for idx in I[0]:
if idx >= MAX_IMAGES:
continue
try:
item = first_10000[idx]
img = item["image"]
caption = item["caption"]
caption_text = f"ID: {idx}\n{caption}"
results.append((img, caption_text))
except IndexError:
continue
return results
def search_similar_images(image: Image.Image):
captions = generate_captions(image)
refined = refine_caption(captions[0], captions[1], captions[2])
text_emb = get_text_embedding(refined)
image_emb = get_image_embedding(image)
text_results = get_results_with_images(text_emb, text_index)
image_results = get_results_with_images(image_emb, image_index)
return refined, text_results, image_results
demo = gr.Interface(
fn=search_similar_images,
inputs=gr.Image(label="Загрузите изображение", type="pil"),
outputs=[
gr.Textbox(label="📜 Сгенерированное описание"),
gr.Gallery(label="🔍 Похожие по описанию (caption)", height="auto", columns=2),
gr.Gallery(label="🎨 Похожие по изображению (CLIP)", height="auto", columns=2)
],
title="🎨 Semantic WikiArt Search",
description="Загрузите изображение. Модель сгенерирует описание, получит эмбеддинги и найдёт похожие картины по описанию и изображению."
)
demo.launch()
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