Upload app.py

app.py

@@ -75,26 +75,25 @@ def get_similar_products_cnn(product_id, num_results):
          #### Top {num_results} Recommended items
          """)
     for i in range(1,len(indices)):
-        rows = fashion_df[['ImageURL','ProductTitle']].loc[fashion_df['ProductId']==Productids[indices[i]]]
+        rows = fashion_df[['Gender','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"Gender Class: {row['Gender']}")
             st.write(f"Product Title: {row['ProductTitle']}")
-            st.write(f"Euclidean Distance from input image: {pdists[i]}")
+            #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')
-st.markdown('---')
-
+st.write('---')
 if button:
     get_similar_products_cnn(str(user_input1), int(user_input2))