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import numpy as np
import pickle as pkl
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.layers import GlobalMaxPool2D
from sklearn.neighbors import NearestNeighbors
import os
from numpy.linalg import norm
import streamlit as st
st.header('Fashion Recommendation System')
# Load precomputed features
Image_features = pkl.load(open('Images_features.pkl', 'rb'))
filenames = pkl.load(open('filenames.pkl', 'rb'))
# Feature extraction function
def extract_features_from_images(image_path, model):
img = image.load_img(image_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_expand_dim = np.expand_dims(img_array, axis=0)
img_preprocess = preprocess_input(img_expand_dim)
result = model.predict(img_preprocess).flatten()
norm_result = result / norm(result)
return norm_result
# Load ResNet50 model
model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model.trainable = False
model = tf.keras.models.Sequential([model, GlobalMaxPool2D()])
# Nearest Neighbors Model
neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
neighbors.fit(Image_features)
# Upload Image
upload_file = st.file_uploader("Upload Image")
if upload_file is not None:
upload_path = os.path.join('/tmp', upload_file.name) # Use /tmp instead of 'upload/'
with open(upload_path, 'wb') as f:
f.write(upload_file.getbuffer())
st.subheader('Uploaded Image')
st.image(upload_file)
# Extract features
input_img_features = extract_features_from_images(upload_path, model)
# Get recommendations
distance, indices = neighbors.kneighbors([input_img_features])
# Display Recommended Images
st.subheader('Recommended Images')
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.image(filenames[indices[0][1]])
with col2:
st.image(filenames[indices[0][2]])
with col3:
st.image(filenames[indices[0][3]])
with col4:
st.image(filenames[indices[0][4]])
with col5:
st.image(filenames[indices[0][5]])
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