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.gitignore

@@ -1,94 +1,93 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# Virtual Environment
-venv/
-env/
-ENV/
-.env
-.venv
-env.bak/
-venv.bak/
-
-# IDE specific files
-.idea/
-.vscode/
-*.swp
-*.swo
-.project
-.pydevproject
-.settings/
-
-# Project specific
-# data/
-# *.pkl
-# temp_query_image.jpg
-# embeddings.pkl
-*.h5
-src/__init__.py
-models/
-temp/
-logs/
-
-# OS specific
-.DS_Store
-Thumbs.db
-*.db
-*.sqlite3
-
-# Jupyter Notebook
-.ipynb_checkpoints
-*.ipynb
-
-# Distribution / packaging
-.Python
-*.manifest
-*.spec
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Logs
-*.log
-local_settings.py
-db.sqlite3
-
-# Environment variables
-.env
-.env.local
-.env.*.local
-
-# Docker
-Dockerfile
-docker-compose.yml
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.env
+.venv
+env.bak/
+venv.bak/
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.project
+.pydevproject
+.settings/
+
+# Project specific
+# data/
+# *.pkl
+# temp_query_image.jpg
+# embeddings.pkl
+*.h5
+models/
+temp/
+logs/
+
+# OS specific
+.DS_Store
+Thumbs.db
+*.db
+*.sqlite3
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Distribution / packaging
+.Python
+*.manifest
+*.spec
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Logs
+*.log
+local_settings.py
+db.sqlite3
+
+# Environment variables
+.env
+.env.local
+.env.*.local
+
+# Docker
+Dockerfile
+docker-compose.yml
 .docker/

README.md

@@ -1,133 +1,117 @@
----
-language: en
-license: mit
-tags:
-- image-search
-- machine-learning
-title: Image Search Engine Fashion
-sdk: streamlit
-emoji: 💻
-colorFrom: blue
-colorTo: pink
----
-
----
-title: Image Search Engine Fashion
-emoji: 🔍
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-## Image Similarity Search Engine
-A deep learning-based image similarity search engine that uses EfficientNetB0 for feature extraction and FAISS for fast similarity search. The application provides a web interface built with Streamlit for easy interaction.
-
-Features
-- Deep Feature Extraction: Uses EfficientNetB0 (pre-trained on ImageNet) to extract meaningful features from images
-- Fast Similarity Search: Implements FAISS for efficient nearest-neighbor search
-- Interactive Web Interface: User-friendly interface built with Streamlit
-- Real-time Processing: Shows progress and time estimates during feature extraction
-- Scalable Architecture: Designed to handle large image datasets efficiently
-
-## Installation
-## Prerequisites
-
-Python 3.8 or higher
-pip package manager
-
-## Setup
-
-1. Clone the repository:
-```
-git clone https://github.com/yourusername/image-similarity-search.git
-cd image-similarity-search
-```
-2. Create and activate a virtual environment:
-```
-python -m venv venv
-source venv/bin/activate  # On Windows use: venv\Scripts\activate
-```
-3. Install required packages:
-```
-pip install -r requirements.txt
-```
-
-## Project Structure
-```
-image-similarity-search/
-├── data/
-│   ├── images/                     # Directory for train dataset images
-│   ├── sample-test-images/         # Directory for test dataset images
-│   └── embeddings.pkl              # Pre-computed image embeddings
-├── src/
-│   ├── feature_extractor.py    # EfficientNetB0 feature extraction
-│   ├── preprocessing.py        # Image preprocessing and embedding computation
-│   ├── similarity_search.py    # FAISS-based similarity search
-│   └── main.py                 # Streamlit web interface
-├── requirements.txt
-├── README.md
-└── .gitignore
-```
-## Usage
-
-1. **Prepare Your Dataset:**
-Get training image dataset from drive:
-```
-https://drive.google.com/file/d/1U2PljA7NE57jcSSzPs21ZurdIPXdYZtN/view?usp=drive_link
-```
-Place your image dataset in the data/images directory
-Supported formats: JPG, JPEG, PNG
-
-2. **Generate Embeddings:**
-```
-python -m src.preprocessing
-```
-
-**This will**:
-- Process all images in the dataset
-- Show progress and time estimates
-- Save embeddings to data/embeddings.pkl
-
-3. **Run the Web Interface:**
-```
-streamlit run src/main.py
-```
-
-4. Using the Interface:
-
-- Upload a query image using the file uploader
-- Click "Search Similar Images"
-- View the most similar images from your dataset
-
-
-
-## Technical Details
-**Feature Extraction**
-- Uses EfficientNetB0 without top layers
-- Input image size: 224x224 pixels
-- Output feature dimension: 1280
-
-**Similarity Search**
-- Uses FAISS IndexFlatL2 for L2 distance-based search
-- Returns top-k most similar images (default k=5)
-
-**Web Interface**
-- Responsive design with Streamlit
-- Displays original and similar images with similarity scores
-- Progress tracking during processing
-
-**Dependencies**
-- TensorFlow 2.x
-- FAISS-cpu (or FAISS-gpu for GPU support)
-- Streamlit
-- Pillow
-- NumPy
-- tqdm
-
-**Performance**
-- Feature extraction: ~1 second per image on CPU
-- Similarity search: Near real-time for datasets up to 100k images
-- Memory usage depends on dataset size (approximately 5KB per image embedding)
+---
+language: en
+license: mit
+tags:
+  - image-search
+  - machine-learning
+---
+
+## Image Similarity Search Engine
+A deep learning-based image similarity search engine that uses EfficientNetB0 for feature extraction and FAISS for fast similarity search. The application provides a web interface built with Streamlit for easy interaction.
+
+Features
+- Deep Feature Extraction: Uses EfficientNetB0 (pre-trained on ImageNet) to extract meaningful features from images
+- Fast Similarity Search: Implements FAISS for efficient nearest-neighbor search
+- Interactive Web Interface: User-friendly interface built with Streamlit
+- Real-time Processing: Shows progress and time estimates during feature extraction
+- Scalable Architecture: Designed to handle large image datasets efficiently
+
+## Installation
+## Prerequisites
+
+Python 3.8 or higher
+pip package manager
+
+## Setup
+
+1. Clone the repository:
+```
+git clone https://github.com/yourusername/image-similarity-search.git
+cd image-similarity-search
+```
+2. Create and activate a virtual environment:
+```
+python -m venv venv
+source venv/bin/activate  # On Windows use: venv\Scripts\activate
+```
+3. Install required packages:
+```
+pip install -r requirements.txt
+```
+
+## Project Structure
+```
+image-similarity-search/
+├── data/
+│   ├── images/                     # Directory for train dataset images
+│   ├── sample-test-images/         # Directory for test dataset images
+│   └── embeddings.pkl              # Pre-computed image embeddings
+├── src/
+│   ├── feature_extractor.py    # EfficientNetB0 feature extraction
+│   ├── preprocessing.py        # Image preprocessing and embedding computation
+│   ├── similarity_search.py    # FAISS-based similarity search
+│   └── main.py                 # Streamlit web interface
+├── requirements.txt
+├── README.md
+└── .gitignore
+```
+## Usage
+
+1. **Prepare Your Dataset:**
+Get training image dataset from drive:
+```
+https://drive.google.com/file/d/1U2PljA7NE57jcSSzPs21ZurdIPXdYZtN/view?usp=drive_link
+```
+Place your image dataset in the data/images directory
+Supported formats: JPG, JPEG, PNG
+
+2. **Generate Embeddings:**
+```
+python -m src.preprocessing
+```
+
+**This will**:
+- Process all images in the dataset
+- Show progress and time estimates
+- Save embeddings to data/embeddings.pkl
+
+3. **Run the Web Interface:**
+```
+streamlit run src/main.py
+```
+
+4. Using the Interface:
+
+- Upload a query image using the file uploader
+- Click "Search Similar Images"
+- View the most similar images from your dataset
+
+
+
+## Technical Details
+**Feature Extraction**
+- Uses EfficientNetB0 without top layers
+- Input image size: 224x224 pixels
+- Output feature dimension: 1280
+
+**Similarity Search**
+- Uses FAISS IndexFlatL2 for L2 distance-based search
+- Returns top-k most similar images (default k=5)
+
+**Web Interface**
+- Responsive design with Streamlit
+- Displays original and similar images with similarity scores
+- Progress tracking during processing
+
+**Dependencies**
+- TensorFlow 2.x
+- FAISS-cpu (or FAISS-gpu for GPU support)
+- Streamlit
+- Pillow
+- NumPy
+- tqdm
+
+**Performance**
+- Feature extraction: ~1 second per image on CPU
+- Similarity search: Near real-time for datasets up to 100k images
+- Memory usage depends on dataset size (approximately 5KB per image embedding)

app.py

@@ -1,50 +1,50 @@
-import streamlit as st
-from PIL import Image
-from src.feature_extractor import FeatureExtractor
-from src.similarity_search import SimilaritySearchEngine
-
-def main():
-    st.title('Image Similarity Search')
-
-    # Upload query image
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-
-    if uploaded_file is not None:
-        # Load the uploaded image
-        query_img = Image.open(uploaded_file)
-
-        # Resize and display the query image
-        query_img_resized = query_img.resize((263, 385))
-        st.image(query_img_resized, caption='Uploaded Image', use_container_width=False)
-
-        # Feature extraction and similarity search
-        if st.button("Search Similar Images"):
-            with st.spinner("Analyzing query image..."):
-                try:
-                    # Initialize feature extractor and search engine
-                    extractor = FeatureExtractor()
-                    search_engine = SimilaritySearchEngine()
-
-                    # Save the uploaded image temporarily
-                    query_img_path = 'temp_query_image.jpg'
-                    query_img.save(query_img_path)
-
-                    # Extract features from the query image
-                    query_embedding = extractor.extract_features(query_img_path)
-
-                    # Perform similarity search
-                    similar_images, distances = search_engine.search_similar_images(query_embedding)
-
-                    # Display similar images
-                    st.subheader('Similar Images')
-                    cols = st.columns(len(similar_images))
-                    for i, (img_path, dist) in enumerate(zip(similar_images, distances)):
-                        with cols[i]:
-                            similar_img = Image.open(img_path).resize((375, 550))
-                            st.image(similar_img, caption=f'Distance: {dist:.2f}', use_container_width=True)
-
-                except Exception as e:
-                    st.error(f"Error during similarity search: {e}")
-
-if __name__ == '__main__':
-    main()
+import streamlit as st
+from PIL import Image
+from src.feature_extractor import FeatureExtractor
+from src.similarity_search import SimilaritySearchEngine
+
+def main():
+    st.title('Image Similarity Search')
+
+    # Upload query image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        # Load the uploaded image
+        query_img = Image.open(uploaded_file)
+
+        # Resize and display the query image
+        query_img_resized = query_img.resize((263, 385))
+        st.image(query_img_resized, caption='Uploaded Image', use_container_width=False)
+
+        # Feature extraction and similarity search
+        if st.button("Search Similar Images"):
+            with st.spinner("Analyzing query image..."):
+                try:
+                    # Initialize feature extractor and search engine
+                    extractor = FeatureExtractor()
+                    search_engine = SimilaritySearchEngine()
+
+                    # Save the uploaded image temporarily
+                    query_img_path = 'temp_query_image.jpg'
+                    query_img.save(query_img_path)
+
+                    # Extract features from the query image
+                    query_embedding = extractor.extract_features(query_img_path)
+
+                    # Perform similarity search
+                    similar_images, distances = search_engine.search_similar_images(query_embedding)
+
+                    # Display similar images
+                    st.subheader('Similar Images')
+                    cols = st.columns(len(similar_images))
+                    for i, (img_path, dist) in enumerate(zip(similar_images, distances)):
+                        with cols[i]:
+                            similar_img = Image.open(img_path).resize((375, 550))
+                            st.image(similar_img, caption=f'Distance: {dist:.2f}', use_container_width=True)
+
+                except Exception as e:
+                    st.error(f"Error during similarity search: {e}")
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -1,10 +1,7 @@
-tensorflow
-numpy
-opencv-python
-scikit-learn
-streamlit
-Pillow
-faiss-cpu
-python-dotenv
-matplotlib
-pandas
+tensorflow
+numpy
+opencv-python
+scikit-learn
+streamlit
+Pillow
+faiss-cpu