Delete app.py

app.py

@@ -1,95 +0,0 @@
-import gradio as gr
-from PIL import Image
-import torch
-import numpy as np
-import faiss
-
-from transformers import (
-    BlipProcessor,
-    BlipForConditionalGeneration,
-    CLIPProcessor,
-    CLIPModel
-)
-from datasets import load_dataset
-
-wikiart_dataset = load_dataset("huggan/wikiart", split="train", streaming=True)
-
-def get_item_streaming(dataset, idx):
-    for i, item in enumerate(dataset):
-        if i == idx:
-            return item
-    raise IndexError("Index out of range")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
-
-blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device).eval()
-
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device).eval()
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-image_index = faiss.read_index("image_index.faiss")
-text_index = faiss.read_index("text_index.faiss")
-
-def generate_caption(image: Image.Image):
-    inputs = blip_processor(image, return_tensors="pt").to(device)
-    with torch.no_grad():
-        caption_ids = blip_model.generate(**inputs)
-    caption = blip_processor.decode(caption_ids[0], skip_special_tokens=True)
-    return caption
-
-def get_clip_text_embedding(text):
-    inputs = clip_processor(text=[text], return_tensors="pt", padding=True).to(device)
-    with torch.no_grad():
-        features = clip_model.get_text_features(**inputs)
-    features = features.cpu().numpy().astype("float32")
-    faiss.normalize_L2(features)
-    return features
-
-def get_clip_image_embedding(image):
-    inputs = clip_processor(images=image, return_tensors="pt").to(device)
-    with torch.no_grad():
-        features = clip_model.get_image_features(**inputs)
-    features = features.cpu().numpy().astype("float32")
-    faiss.normalize_L2(features)
-    return features
-
-def get_results_with_images(embedding, index, top_k=2):
-    D, I = index.search(embedding, top_k)
-    results = []
-    for idx in I[0]:
-        try:
-            item = get_item_streaming(wikiart_dataset, int(idx))
-            img = item["image"]
-            title = item.get("title", "Untitled")
-            artist = item.get("artist", "Unknown")
-            caption = f"ID: {idx}\n{title} — {artist}"
-            results.append((img, caption))
-        except IndexError:
-            continue
-    return results
-
-# Основная функция поиска
-def search_similar_images(image: Image.Image):
-    caption = generate_caption(image)
-    text_emb = get_clip_text_embedding(caption)
-    image_emb = get_clip_image_embedding(image)
-
-    text_results = get_results_with_images(text_emb, text_index)
-    image_results = get_results_with_images(image_emb, image_index)
-
-    return caption, 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 (CLIP)", height="auto", columns=2),
-        gr.Gallery(label="🎨 Похожие по изображению (CLIP)", height="auto", columns=2)
-    ],
-    title="🎨 Semantic WikiArt Search (BLIP + CLIP)",
-    description="Загрузите изображение. Модель BLIP сгенерирует описание, а CLIP найдёт похожие картины по тексту и изображению."
-)
-
-demo.launch()