Upload app.py

app.py

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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')
+
+Image_features = pkl.load(open('Images_features.pkl','rb'))
+filenames = pkl.load(open('filenames.pkl','rb'))
+
+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
+model = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))
+model.trainable = False
+
+model = tf.keras.models.Sequential([model,
+                                   GlobalMaxPool2D()
+                                   ])
+neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+neighbors.fit(Image_features)
+upload_file = st.file_uploader("Upload Image")
+if upload_file is not None:
+    with open(os.path.join('upload', upload_file.name), 'wb') as f:
+        f.write(upload_file.getbuffer())
+    st.subheader('Uploaded Image')
+    st.image(upload_file)
+    input_img_features = extract_features_from_images(upload_file, model)
+    distance,indices = neighbors.kneighbors([input_img_features])
+    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]])