Add files and folders to G-FASHION

.gitattributes

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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+feature_list.pkl filter=lfs diff=lfs merge=lfs -text
+images1/* filter=lfs diff=lfs merge=lfs -text
+images2/* filter=lfs diff=lfs merge=lfs -text
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+filepath.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
-title: G FASHION
-emoji: 📈
-colorFrom: red
-colorTo: blue
+title: Goody Goody Fashion Store
+emoji: 🏆
+colorFrom: yellow
+colorTo: indigo
 sdk: streamlit
-sdk_version: 1.34.0
+sdk_version: 1.26.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 ---
 
+"FASHION RECOMMENDER SYSTEM"
+
+Game Plan Image Dataset: Start with a dataset containing 44,000 fashion images.
+
+Pre-trained Model - ResNet-50: Utilize the pre-trained ResNet-50 model, a convolutional neural network (CNN) trained on a large dataset for image classification tasks.
+
+Extracting Embeddings: Pass each image through the ResNet-50 model to extract feature embeddings. The output will be a numerical vector (embedding) that represents the image's features
+
+Embedding Extraction Function: Define a function to extract embeddings using the pre-trained ResNet-50 model. This function takes an image as input and returns the corresponding feature embedding.
+
+Search for Similar Embeddings: When a new image is presented to the system: Use the embedding extraction function to obtain the embedding for the new image. Compare this embedding with the embeddings of all images in the dataset.
+
+Similarity Ranking: Calculate the similarity between the embeddings (e.g., cosine similarity). Rank the images based on their similarity to the embedding of the new image.
+
+Top 5 Similar Images: Select the top 5 images with the highest similarity scores.
+
+Display Recommendations: Display the top 5 images as fashion recommendations for the given new image.
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import pickle
+import os
+import tensorflow
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.layers import GlobalMaxPooling2D, Dense
+from sklearn.neighbors import NearestNeighbors
+from numpy.linalg import norm
+from classification_models.tfkeras import Classifiers
+
+filenames = pickle.load(open('filepath.pkl','rb'))
+feature_list = pickle.load(open('feature_list.pkl','rb'))
+feature_list = np.array(feature_list)
+# Get the ResNeXt model
+ResNeXt50, preprocess_input = Classifiers.get('resnext50')
+# create a model instance
+model = ResNeXt50(include_top=False, input_shape=(224, 224, 3), weights='imagenet')
+model.trainable = False
+model = tensorflow.keras.Sequential([model,GlobalMaxPooling2D()])
+
+st.title('goody goody fashion store')
+
+def save_uploaded_file(uploaded_file):
+    try:
+        # Check if the 'uploads' directory exists. If not, create it.
+        if not os.path.exists('uploads'):
+            os.makedirs('uploads')
+
+        with open(os.path.join('uploads',uploaded_file.name),'wb') as f:
+            f.write(uploaded_file.getbuffer())
+        return 1
+    except Exception as e:
+        # Print the exception message
+        print(f"Error in save_uploaded_file: {e}")
+        return 0
+
+def feature_extraction(img_path,model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    expanded_img_array = np.expand_dims(img_array, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    result = model.predict(preprocessed_img).flatten()
+    normalized_result = result / norm(result)
+
+    return normalized_result
+
+def recommend(features,feature_list):
+    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='cosine')
+    neighbors.fit(feature_list)
+
+    distances, indices = neighbors.kneighbors([features])
+
+    return indices
+
+uploaded_file = st.file_uploader("Choose an image")
+if uploaded_file is not None:
+    if save_uploaded_file(uploaded_file):
+        display_image = Image.open(uploaded_file)
+        st.image(display_image)
+        features = feature_extraction(os.path.join("uploads",uploaded_file.name),model)
+        indices = recommend(features,feature_list)
+        col1,col2,col3,col4,col5 = st.columns(5)
+
+        # Add a print statement to print the file path
+        print(f"File path: {filenames[indices[0][0]]}")
+
+        with col1:
+            st.image(r'{}'.format(filenames[indices[0][0]]))
+        with col2:
+            st.image(r'{}'.format(filenames[indices[0][1]]))
+        with col3:
+            st.image(r'{}'.format(filenames[indices[0][2]]))
+        with col4:
+            st.image(r'{}'.format(filenames[indices[0][3]]))
+        with col5:
+            st.image(r'{}'.format(filenames[indices[0][4]]))
+    else:
+        st.header("Some error occured in file upload")

feature_list.pkl

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+size 365621870

filepath.pkl

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+size 881913