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app.py

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+import streamlit as st
+import os
+from PIL import Image
+import numpy as np
+import pickle
+import tensorflow
+import pandas as pd
+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
+from numpy.linalg import norm        
+
+feature_list = np.array(pickle.load(open('embedding_large.pkl','rb')))
+# print(feature_list)
+filenames = pd.read_pickle('filenames_large.pkl')
+
+# print(filenames)
+
+feature_list_myntra = np.array(pickle.load(open('embedding_myntra.pkl','rb')))
+# print(feature_list)
+filenames_myntra = pd.read_pickle('filenames_myntra.pkl')
+
+
+
+
+    
+
+
+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 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
+
+def feature_extraction(img_path,model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    expanded_img_array = np.expand_dims(img_array, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    result = model.predict(preprocessed_img).flatten()
+    normalized_result = result / norm(result)
+
+    return normalized_result
+
+def recommend(features,feature_list):
+    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+    neighbors.fit(feature_list)
+
+    distances, indices = neighbors.kneighbors([features])
+    print(distances,indices)
+
+    
+
+
+    return indices
+
+
+def recommend_myntra(features,feature_list):
+    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+    neighbors.fit(feature_list_myntra)
+
+    distances, indices = neighbors.kneighbors([features])
+    print(distances,indices)
+
+    
+
+
+    return indices
+
+#
+
+menu = ['FR','FRM','AB']
+option = st.sidebar.selectbox("Select your model",menu)
+
+if option=='FR':
+    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) 
+            # feature extract
+            features = feature_extraction(os.path.join("uploads",uploaded_file.name),model)
+            # recommendention
+            indices = recommend(features,feature_list)
+            # show
+            st.header("Recommend For You....")
+            st.text("")
+            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]])
+        else:
+            st.header("Some error occured in file upload")
+
+
+elif option=='FRM':
+    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) 
+            # feature extract
+            features = feature_extraction(os.path.join("uploads",uploaded_file.name),model)
+            # recommendention
+            indices = recommend_myntra(features,feature_list)
+            # show
+            st.header("Recommend For You....")
+            st.text("")
+            col1,col2,col3,col4,col5 = st.columns(5)
+            with col1:
+                st.image(filenames_myntra[indices[0][1]])
+            with col2:
+                st.image(filenames_myntra[indices[0][2]])
+            with col3:
+                st.image(filenames_myntra[indices[0][3]])
+            with col4:
+                st.image(filenames_myntra[indices[0][4]])
+            with col5:
+                st.image(filenames_myntra[indices[0][5]])
+        else:
+            st.header("Some error occured in file upload")
+
+
+elif option=="AB":
+    st.markdown("FR: First Model Only Recommend Women Fashion Dresses...")
+    st.markdown("FRM: Second Model Recommend Men Women include also footwears and clothes.")
+    st.title("Product Recommendation Engine V-2.0")
+    st.markdown("This Engine Developed by <a href='https://github.com/datamind321'>DataMind Platform</a>",unsafe_allow_html=True)
+    st.subheader("if you have any query Contact us on : bme19rahul.r@invertisuniversity.ac.in") 
+    st.markdown("More on : ")
+    
+    
+    st.markdown("[![Linkedin](https://content.linkedin.com/content/dam/me/business/en-us/amp/brand-site/v2/bg/LI-Bug.svg.original.svg)](https://www.linkedin.com/in/rahul-rathour-402408231/)",unsafe_allow_html=True)
+    
+
+    st.markdown("[![Instagram](https://img.icons8.com/color/1x/instagram-new.png)](https://instagram.com/_technical__mind?igshid=YmMyMTA2M2Y=)")
+
+

embedding_large.pkl

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+oid sha256:7c43edde14bf1064d3b57543dafd687bfd47b9523ccbeefb1684cf9a0bc5e2e2
+size 116924045

embedding_myntra.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f9a02954886f35f0fd2450d156d94921f8589047c2bbc0123773a752ef2e70d
+size 23908148

filenames_large.pkl

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+oid sha256:602fbb469cad2793999dadee5d009348a96c5624fc4487ee7d8086c04bb55266
+size 312069

filenames_myntra.pkl

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+oid sha256:461692e11e7b5fffbc50fd9c1f8b1c96f134c98748e4cfec799a88155c787fa9
+size 48891