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import streamlit as st
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import os
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from PIL import Image
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import pickle
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import tensorflow
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import numpy as np
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from numpy.linalg import norm
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.layers import GlobalMaxPooling2D
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from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
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from sklearn.neighbors import NearestNeighbors
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feature_list = np.array(pickle.load(open('embeddings2.pkl', 'rb')))
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filenames = pickle.load(open('filenames2.pkl', 'rb'))
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model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
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model.trainable = False
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model = tensorflow.keras.Sequential([
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model,
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GlobalMaxPooling2D()
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])
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st.title("Fashion Recommender System")
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def extract_features(img_path, model):
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img = image.load_img(img_path, target_size=(224, 224))
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image_array = image.img_to_array(img)
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expanded_image_array = np.expand_dims(image_array, axis=0)
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processed_image = preprocess_input(expanded_image_array)
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result = model.predict(processed_image).flatten()
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normalized_result = result / norm(result)
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return normalized_result
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def recommend(features,feature_list):
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neighbors = NearestNeighbors(n_neighbors=5, algorithm='brute', metric='euclidean')
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neighbors.fit(feature_list)
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distances, indices = neighbors.kneighbors([features])
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return indices
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def save_uploaded_file(uploaded_file):
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try:
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with open(os.path.join('uploads', uploaded_file.name), 'wb') as f:
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f.write(uploaded_file.getbuffer())
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return 1
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except:
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return 0
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uploaded_file = st.file_uploader("choose an image")
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if uploaded_file is not None:
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if save_uploaded_file(uploaded_file):
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display_image = Image.open(uploaded_file)
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st.image(display_image)
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features = extract_features(os.path.join("uploads",uploaded_file.name),model)
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indices = recommend(features,feature_list)
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col1,col2,col3,col4,col5 = st.columns(5)
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with col1:
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st.image(filenames[indices[0][0]])
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with col2:
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st.image(filenames[indices[0][1]])
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with col3:
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st.image(filenames[indices[0][2]])
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with col4:
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st.image(filenames[indices[0][3]])
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with col5:
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st.image(filenames[indices[0][4]])
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else:
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st.header("Some error has occured while uploading file")
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