Upload app.py

app.py

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+import numpy as np
+import pandas as pd
+from PIL import Image
+from PIL import ImageFile
+import urllib.request
+from sklearn.metrics import pairwise_distances
+from datetime import datetime
+import streamlit as st
+
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+fashion_df = pd.read_csv("./fashion.csv")
+boys_extracted_features = np.load('./Boys_ResNet_features.npy')
+boys_Productids = np.load('./Boys_ResNet_feature_product_ids.npy')
+girls_extracted_features = np.load('./Girls_ResNet_features.npy')
+girls_Productids = np.load('./Girls_ResNet_feature_product_ids.npy')
+men_extracted_features = np.load('./Men_ResNet_features.npy')
+men_Productids = np.load('./Men_ResNet_feature_product_ids.npy')
+women_extracted_features = np.load('./Women_ResNet_features.npy')
+women_Productids = np.load('./Women_ResNet_feature_product_ids.npy')
+fashion_df["ProductId"] = fashion_df["ProductId"].astype(str)
+
+st.image("https://storage.googleapis.com/danacita-website-v3-prd/website_v3/images/biaya_bootcamp__kursus_hacktiv8_6.original.png")
+st.markdown('---')
+st.subheader('FashClass - HCK-14 Final Project') 
+st.write('Name :')
+st.write('1. Anjas Fajar Maulana    (Data Science)')
+st.write('2. Fazrin Muhammad    (Data Analyst)')
+st.write('3. Naufal Andika Ramadhan (Data Engineer)')
+st.write('4. Salsa Sabitha Hurriyah (Data Science)')
+
+st.write('---')
+def load_data(file_path):
+    return pd.read_csv(file_path)
+
+# Path to the CSV file
+file_path = "fashion.csv"
+# Load the data
+data = load_data(file_path)
+
+# Display the data using Streamlit
+st.write("### List of Product")
+st.write(data)
+
+st.write('---')
+def get_similar_products_cnn(product_id, num_results):
+    if(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Boys"):
+        extracted_features = boys_extracted_features
+        Productids = boys_Productids
+    elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Girls"):
+        extracted_features = girls_extracted_features
+        Productids = girls_Productids
+    elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Men"):
+        extracted_features = men_extracted_features
+        Productids = men_Productids
+    elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Women"):
+        extracted_features = women_extracted_features
+        Productids = women_Productids
+    Productids = list(Productids)
+    doc_id = Productids.index(product_id)
+    pairwise_dist = pairwise_distances(extracted_features, extracted_features[doc_id].reshape(1,-1))
+    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
+    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]
+    st.write("""
+         #### input item details
+         """)
+    ip_row = fashion_df[['ImageURL','ProductTitle']].loc[fashion_df['ProductId']==Productids[indices[0]]]
+    for indx, row in ip_row.iterrows():
+        image = Image.open(urllib.request.urlopen(row['ImageURL']))
+        image = image.resize((224,224))
+        st.image(image)
+        st.write(f"Product Title: {row['ProductTitle']}")
+    st.write(f"""
+         #### Top {num_results-1} Recommended items
+         """)
+    for i in range(1,len(indices)):
+        rows = fashion_df[['ImageURL','ProductTitle']].loc[fashion_df['ProductId']==Productids[indices[i]]]
+        for indx, row in rows.iterrows():
+            #image = Image.open(Image(url=row['ImageURL'], width = 224, height = 224,embed=True))
+            image = Image.open(urllib.request.urlopen(row['ImageURL']))
+            image = image.resize((224,224))
+            st.image(image)
+            st.write(f"Product Title: {row['ProductTitle']}")
+            st.write(f"Euclidean Distance from input image: {pdists[i]}")
+
+st.write("""
+         ## FashClass Recommendation
+         """
+         )
+
+
+user_input1 = st.text_input("Enter the item id")
+user_input2 = st.text_input("Enter number of products to be recommended")
+
+
+button = st.button('Generate recommendations')
+if button:
+    get_similar_products_cnn(str(user_input1), int(user_input2))