Initial Commit

.dockerignore

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+model_creation/
+LICENSE
+README.md

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+uploaded_image.jpg
+model_creation/

LICENSE

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+MIT License
+
+Copyright (c) 2024 Rahul Bhoyar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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-print("Hello World")
+import streamlit as st
+import os
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cosine
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
+from tensorflow.keras.models import Model
+import numpy as np
+import pickle
+from PIL import Image
+import matplotlib.pyplot as plt
+import glob
+from tensorflow.keras.models import load_model
+
+# Function for preprocessing image
+def preprocess_image(img):
+    img = image.load_img(img, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array_expanded = np.expand_dims(img_array, axis=0)
+    return preprocess_input(img_array_expanded)
+
+# Function for extracting features
+def extract_features(model, preprocessed_img):
+    features = model.predict(preprocessed_img)
+    flattened_features = features.flatten()
+    normalized_features = flattened_features / np.linalg.norm(flattened_features)
+    return normalized_features
+
+# Function for recommending fashion items
+def recommend_fashion_items_cnn(input_image, all_features, all_image_names, model, top_n=4):
+    # pre-process the input image and extract features
+    preprocessed_img = preprocess_image(input_image)
+    input_features = extract_features(model, preprocessed_img)
+
+    # calculate similarities and find the top N similar images
+    similarities = [1 - cosine(input_features, other_feature) for other_feature in all_features]
+    similar_indices = np.argsort(similarities)[-top_n:]
+
+    # filter out the input image index from similar_indices
+    similar_indices = [idx for idx in similar_indices]# if idx != all_image_names.index(input_image_path)]
+    # display the input image and recommended images
+    recommended_images = [] # Add input image as the first recommendation
+
+    for idx in similar_indices:
+        recommedneded_image_path = os.path.join('model_creation/women_fashion_data', all_image_names[idx])
+        recommended_images.append(recommedneded_image_path)
+        
+    return recommended_images
+
+
+
+# directory path containing your images
+image_directory = 'model_creation/women_fashion_data'
+
+image_paths_list = [file for file in glob.glob(os.path.join(image_directory, '*.*')) if file.endswith(('.jpg', '.png', '.jpeg', 'webp'))]
+
+
+base_model = VGG16(weights='imagenet', include_top=False)
+model = Model(inputs=base_model.input, outputs=base_model.output)
+    
+all_features = []
+all_image_names = []
+
+for img_path in image_paths_list:
+    preprocessed_img = preprocess_image(img_path)
+    features = extract_features(model, preprocessed_img)
+    all_features.append(features)
+    all_image_names.append(os.path.basename(img_path))
+    
+def main():
+    # Title and Description
+    st.title('Women Fashion Recommendation System')
+    st.subheader("Author : Rahul Bhoyar")
+    st.write("Upload an image of the clothing item you want recommendations for:")
+
+    # Upload image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        # Get the path to the uploaded image
+        input_image_path = "uploaded_image.jpg"  # Save the uploaded image temporarily
+        image.save(input_image_path)
+
+        # Display waiting message
+        with st.spinner("Please wait while we process your request..."):
+            # Display recommendation results
+            st.write("Here are the recommended images based on the uploaded image:")
+            
+            # Call the recommend_fashion_items_cnn function
+            recommended_images = recommend_fashion_items_cnn(input_image_path, all_features, all_image_names, model)
+
+            # Display recommended images
+            col1, col2 = st.columns(2)
+            columns = [col1, col2]
+
+            for i, recommended_image_path in enumerate(recommended_images, start=1):
+                recommended_image = Image.open(recommended_image_path)
+                with columns[i % 2]:
+                    st.image(recommended_image, caption=f"Recommendation {i}", width=200, use_column_width='auto')
+                    
+    # Footer Section
+    st.text('Powered by Streamlit')
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+jupyter
+streamlit
+matplotlib
+scipy
+tensorflow
+numpy
+Pillow
+idna
+huggingface-cli
+huggingface_hub[cli]