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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('🧚‍♀️ Moda Tavsiye Sistemi 🧚‍♀️')
+
+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()
+])
+model.build((None, 224, 224, 3)) 
+
+neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+neighbors.fit(Image_features)
+
+uploaded_file = st.file_uploader("Resim yükle", type=["jpg","png"])
+
+if uploaded_file is not None:
+    with open(os.path.join('upload', uploaded_file.name), 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    st.subheader('Yüklenen Resim')
+    st.image(uploaded_file)
+    input_img_features = extract_features_from_images(uploaded_file, model)
+    distance,indices = neighbors.kneighbors([input_img_features])
+    
+    st.subheader('Tavsiye Edilen Resimler')
+    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]])
+    

requirments.txt

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+streamlit
+pickle
+tensorflow
+scikit-learn
+numpy
+os