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.gitattributes

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+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+clothesDB/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use Python 3.10
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip and install required python packages
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 8501 available to the world outside this container
+EXPOSE 8501
+
+# Run app.py when the container launches
+CMD ["streamlit", "run", "app.py"]

README.md

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----
-title: Accessary
-emoji: 🔥
-colorFrom: purple
-colorTo: green
-sdk: streamlit
-sdk_version: 1.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Itda Nosegmentation
+emoji: 🐨
+colorFrom: gray
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.38.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

accessaryDB/682a1a15-1caf-49d8-b42d-7cff88354fa2/data_level0.bin

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accessaryDB/682a1a15-1caf-49d8-b42d-7cff88354fa2/header.bin

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accessaryDB/682a1a15-1caf-49d8-b42d-7cff88354fa2/length.bin

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+size 4000

accessary_weights.onnx

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+oid sha256:f7647fb4791077446a1b5ff2fb97fab43af297957ad65ec6d429408431ce5688
+size 44731025

app.py

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+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
+import cv2
+import chromadb
+
+@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_onnx_model():
+    session = ort.InferenceSession("./accessary_weights.onnx")
+    return session
+
+onnx_session = load_onnx_model()
+
+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
+
+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_onnx(image):
+    input_image = np.array(image.resize((640, 640)), dtype=np.float32)
+    input_image = np.transpose(input_image, [2, 0, 1]) 
+    input_image = np.expand_dims(input_image, axis=0)  
+    input_image /= 255.0  
+
+    inputs = {onnx_session.get_inputs()[0].name: input_image}
+    outputs = onnx_session.run(None, inputs)
+    
+    detections = outputs[0]  
+    categories = []
+    for detection in detections:
+        x1, y1, x2, y2, conf, cls = detection
+        category = str(int(cls))  
+        if category in ['Bracelets', 'Broches', '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
+
+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("Advanced Fashion 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 Clothing"):
+        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_onnx(query_image)
+                if st.session_state.detections:
+                    st.session_state.step = 'select_category'
+                else:
+                    st.warning("No clothing 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.")
+
+# Update the 'select_category' step
+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.")

requirements.txt

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+streamlit
+open_clip_torch
+torch
+torchvision
+transformers
+requests
+Pillow
+numpy
+pandas
+matplotlib
+scikit-learn
+scipy
+opencv-python-headless
+inference_sdk
+ultralytics
+streamlit-img-label
+pascal-voc-writer
+chromadb