app.py

import streamlit as st
import open_clip
import torch
import requests
from PIL import Image
from io import BytesIO
import time
import json
import numpy as np
import onnxruntime as ort
from ultralytics import YOLO
import cv2
import chromadb

# CLIP 모델 로드
@st.cache_resource
def load_clip_model():
    model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
    tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    return model, preprocess_val, tokenizer, device

clip_model, preprocess_val, tokenizer, device = load_clip_model()

@st.cache_resource
def load_yolo_model():
    return YOLO("./accessaries.pt")

yolo_model = load_yolo_model()

# URL에서 이미지 로드
def load_image_from_url(url, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.get(url, timeout=10)
            response.raise_for_status()
            img = Image.open(BytesIO(response.content)).convert('RGB')
            return img
        except (requests.RequestException, Image.UnidentifiedImageError) as e:
            if attempt < max_retries - 1:
                time.sleep(1)
            else:
                return None

# ChromaDB 클라이언트 설정
client = chromadb.PersistentClient(path="./accessaryDB")
collection = client.get_collection(name="accessary_items_ver2")

def get_image_embedding(image):
    image_tensor = preprocess_val(image).unsqueeze(0).to(device)
    with torch.no_grad():
        image_features = clip_model.encode_image(image_tensor)
        image_features /= image_features.norm(dim=-1, keepdim=True)
    return image_features.cpu().numpy()

def get_text_embedding(text):
    text_tokens = tokenizer([text]).to(device)
    with torch.no_grad():
        text_features = clip_model.encode_text(text_tokens)
        text_features /= text_features.norm(dim=-1, keepdim=True)
    return text_features.cpu().numpy()

def get_all_embeddings_from_collection(collection):
    all_embeddings = collection.get(include=['embeddings'])['embeddings']
    return np.array(all_embeddings)

def get_metadata_from_ids(collection, ids):
    results = collection.get(ids=ids)
    return results['metadatas']
    
def find_similar_images(query_embedding, collection, top_k=5):
    database_embeddings = get_all_embeddings_from_collection(collection)
    similarities = np.dot(database_embeddings, query_embedding.T).squeeze()
    top_indices = np.argsort(similarities)[::-1][:top_k]
    
    all_data = collection.get(include=['metadatas'])['metadatas']
    
    top_metadatas = [all_data[idx] for idx in top_indices]
    
    results = []
    for idx, metadata in enumerate(top_metadatas):
        results.append({
            'info': metadata,
            'similarity': similarities[top_indices[idx]]
        })
    return results

def detect_clothing(image):
    results = yolo_model(image)
    detections = results[0].boxes.data.cpu().numpy()
    categories = []
    for detection in detections:
        x1, y1, x2, y2, conf, cls = detection
        category = yolo_model.names[int(cls)]
        if category in ['Bracelets', 'Broches', 'bag', 'belt', 'earring', 'maangtika', 'necklace', 'nose ring', 'ring', 'tiara']:
            categories.append({
                'category': category,
                'bbox': [int(x1), int(y1), int(x2), int(y2)],
                'confidence': conf
            })
    return categories

# 이미지 자르기
def crop_image(image, bbox):
    return image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))

# 세션 상태 초기화
if 'step' not in st.session_state:
    st.session_state.step = 'input'
if 'query_image_url' not in st.session_state:
    st.session_state.query_image_url = ''
if 'detections' not in st.session_state:
    st.session_state.detections = []
if 'selected_category' not in st.session_state:
    st.session_state.selected_category = None

# Streamlit app
st.title("Accessary Search App")

# 단계별 처리
if st.session_state.step == 'input':
    st.session_state.query_image_url = st.text_input("Enter image URL:", st.session_state.query_image_url)
    if st.button("Detect acsseary"):
        if st.session_state.query_image_url:
            query_image = load_image_from_url(st.session_state.query_image_url)
            if query_image is not None:
                st.session_state.query_image = query_image
                st.session_state.detections = detect_clothing(query_image)
                if st.session_state.detections:
                    st.session_state.step = 'select_category'
                else:
                    st.warning("No items detected in the image.")
            else:
                st.error("Failed to load the image. Please try another URL.")
        else:
            st.warning("Please enter an image URL.")

elif st.session_state.step == 'select_category':
    st.image(st.session_state.query_image, caption="Query Image", use_column_width=True)
    st.subheader("Detected Clothing Items:")
    
    for detection in st.session_state.detections:
        col1, col2 = st.columns([1, 3])
        with col1:
            st.write(f"{detection['category']} (Confidence: {detection['confidence']:.2f})")
        with col2:
            cropped_image = crop_image(st.session_state.query_image, detection['bbox'])
            st.image(cropped_image, caption=detection['category'], use_column_width=True)
    
    options = [f"{d['category']} (Confidence: {d['confidence']:.2f})" for d in st.session_state.detections]
    selected_option = st.selectbox("Select a category to search:", options)
    
    if st.button("Search Similar Items"):
        st.session_state.selected_category = selected_option
        st.session_state.step = 'show_results'

elif st.session_state.step == 'show_results':
    st.image(st.session_state.query_image, caption="Query Image", use_column_width=True)
    selected_detection = next(d for d in st.session_state.detections 
                              if f"{d['category']} (Confidence: {d['confidence']:.2f})" == st.session_state.selected_category)
    cropped_image = crop_image(st.session_state.query_image, selected_detection['bbox'])
    st.image(cropped_image, caption="Cropped Image", use_column_width=True)
    query_embedding = get_image_embedding(cropped_image)
    similar_images = find_similar_images(query_embedding, collection)
    
    st.subheader("Similar Items:")
    for img in similar_images:
        col1, col2 = st.columns(2)
        with col1:
            st.image(img['info']['image_url'], use_column_width=True)
        with col2:
            st.write(f"Name: {img['info']['name']}")
            st.write(f"Brand: {img['info']['brand']}")
            st.write(f"Category: {img['info']['category']}")
            st.write(f"Price: {img['info']['price']}")
            st.write(f"Discount: {img['info']['discount']}%")
            st.write(f"Similarity: {img['similarity']:.2f}")
    
    if st.button("Start New Search"):
        st.session_state.step = 'input'
        st.session_state.query_image_url = ''
        st.session_state.detections = []
        st.session_state.selected_category = None


else:  # Text search
    query_text = st.text_input("Enter search text:")
    if st.button("Search by Text"):
        if query_text:
            text_embedding = get_text_embedding(query_text)
            similar_images = find_similar_images(text_embedding, collection)
            st.subheader("Similar Items:")
            for img in similar_images:
                col1, col2 = st.columns(2)
                with col1:
                    st.image(img['info']['image_url'], use_column_width=True)
                with col2:
                    st.write(f"Name: {img['info']['name']}")
                    st.write(f"Brand: {img['info']['brand']}")
                    st.write(f"Category: {img['info']['category']}")
                    st.write(f"Price: {img['info']['price']}")
                    st.write(f"Discount: {img['info']['discount']}%")
                    st.write(f"Similarity: {img['similarity']:.2f}")
        else:
            st.warning("Please enter a search text.")