Add files via upload

main.py

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+import streamlit as st
+import os
+from PIL import Image
+import pickle
+import tensorflow
+import numpy as np
+from numpy.linalg import norm
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.layers import GlobalMaxPooling2D
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from sklearn.neighbors import NearestNeighbors
+
+feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
+filenames = pickle.load(open('filenames.pkl', 'rb'))
+
+model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+model.trainable = False
+
+model = tensorflow.keras.Sequential([
+    model,
+    GlobalMaxPooling2D()
+])
+
+st.title("Fashion Recommender System")
+
+
+def extract_features(img_path, model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    image_array = image.img_to_array(img)
+    expanded_image_array = np.expand_dims(image_array, axis=0)
+    processed_image = preprocess_input(expanded_image_array)
+    result = model.predict(processed_image).flatten()
+    normalized_result = result / norm(result)
+    return normalized_result
+
+def recommend(features,feature_list):
+    neighbors = NearestNeighbors(n_neighbors=5, algorithm='brute', metric='euclidean')
+    neighbors.fit(feature_list)
+
+    distances, indices = neighbors.kneighbors([features])
+    return indices
+
+
+def save_uploaded_file(uploaded_file):
+    try:
+        with open(os.path.join('uploads', uploaded_file.name), 'wb') as f:
+            f.write(uploaded_file.getbuffer())
+        return 1
+    except:
+        return 0
+
+
+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 = extract_features(os.path.join("uploads",uploaded_file.name),model)
+        #st.text(features)
+        indices = recommend(features,feature_list)
+        col1,col2,col3,col4,col5 = st.columns(5)
+        with col1:
+            st.image(filenames[indices[0][0]])
+        with col2:
+            st.image(filenames[indices[0][1]])
+        with col3:
+            st.image(filenames[indices[0][2]])
+        with col4:
+            st.image(filenames[indices[0][3]])
+        with col5:
+            st.image(filenames[indices[0][4]])
+    else:
+        st.header("Some error has occured while uploading file")